WO2025265109A1 - Systèmes, procédés et dispositifs de surveillance de la pression intraoculaire - Google Patents

Systèmes, procédés et dispositifs de surveillance de la pression intraoculaire

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Publication number
WO2025265109A1
WO2025265109A1 PCT/US2025/034691 US2025034691W WO2025265109A1 WO 2025265109 A1 WO2025265109 A1 WO 2025265109A1 US 2025034691 W US2025034691 W US 2025034691W WO 2025265109 A1 WO2025265109 A1 WO 2025265109A1
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WIPO (PCT)
Prior art keywords
images
eye
subject
blinks
quantifying
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PCT/US2025/034691
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English (en)
Inventor
Amal Isaiah
Scott Strome
Justin Schumacher
Giuliano Scarcelli
Kevin Aroom
John Robertson RZASA
Vito Ivano D'ALESSANDRO
Antonio Fiore
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University of Maryland Baltimore
University of Maryland College Park
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University of Maryland Baltimore
University of Maryland College Park
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Publication of WO2025265109A1 publication Critical patent/WO2025265109A1/fr
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    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B3/00—Apparatus for testing the eyes; Instruments for examining the eyes
    • A61B3/10—Objective types, i.e. instruments for examining the eyes independent of the patients' perceptions or reactions
    • A61B3/16—Objective types, i.e. instruments for examining the eyes independent of the patients' perceptions or reactions for measuring intraocular pressure, e.g. tonometers
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B3/00—Apparatus for testing the eyes; Instruments for examining the eyes
    • A61B3/0016—Operational features thereof
    • A61B3/0025—Operational features thereof characterised by electronic signal processing, e.g. eye models
    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; COUNTING
    • G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
    • G06V40/00—Recognition of biometric, human-related or animal-related patterns in image or video data
    • G06V40/10—Human or animal bodies, e.g. vehicle occupants or pedestrians; Body parts, e.g. hands
    • G06V40/18—Eye characteristics, e.g. of the iris
    • G06V40/19—Sensors therefor

Definitions

  • the present disclosure relates to systems and methods for non-contact, real-time measurement and/or monitoring of intraocular pressure of a subject using optical techniques.
  • Physiological control of fluid pressures within the eye is significant to maintenance of healthy vision.
  • One of skill in the art will appreciate that a manifestation of chronic increase in fluid pressures within the eye is a condition called glaucoma.
  • the most common type of glaucoma, open-angle glaucoma results in slow loss of vision in a patient when left untreated.
  • Open-angle glaucoma is the leading cause of irreversible blindness in the world.
  • Tonometry usually requires a visit to an optometrist or an ophthalmologist who performs IOP measurement and examines the retina for changes related to elevated eye pressures. Once a patient is diagnosed with glaucoma, regular IOP monitoring, examination of the retina and timely medical or surgical treatment are necessary to assess progression of the disease and prevent irreversible changes to the retina and the optic nerve. Glaucoma is responsible for almost 10 million patient visits annually with consequent direct costs in excess of $2.5 billion. Therefore, identification of cost-reduction intervention points is a priority shared by physicians as well as governmental agencies. Furthermore, medication non-compliance is estimated to be over 25% in patients who require medical treatment of elevated IOP.
  • IOP measurement derived from application of mechanical force directly to the eye by direct contact (e.g., applanation).
  • a less reliable technique utilizes pneumatic (e.g., air) pressure.
  • the primary limitation of all these conventional techniques, including the applanation tonometry is fluctuation of IOP outside the time of measurement.
  • Time-based (e.g., temporal) measurements are limited by the number of visits to the physician, and hence are not practical to perform on a day-to-day basis. Tonometry and current non-invasive measurements using current commercially available devices, thus, only obtain spot measurements, and cannot monitor continuous changes in IOP.
  • lens sensors with transducers custom molded to the shape of the patient’s cornea, or other physical characteristics of the patient.
  • lens sensors are expensive and do not necessarily address the costs associated with regular measurements made by trained personnel.
  • lens sensors demand additional dexterity from the patient at home.
  • the lens sensor also requires additional washing, storage, replacement requirements at regular intervals. Therefore, constant monitoring of IOP remains a clinical challenge unsolved by currently available technologies.
  • the present disclosure addresses the shortcomings disclosed above by providing systems and methods for non-contact, real-time measurement and/or monitoring of intraocular pressure of a subject.
  • the systems, methods, and devices of the present disclosure are configured to assess changes within a retina and determine if a change is due to elevated IOP, including trends of IOP over time for a respective subject, such as over the course of minutes, hours, or days.
  • the systems, methods, and devices acquire data for longitudinal assessment of changes within the retina through a plurality of images.
  • the plurality of images is related to the light scattering characteristics of the retina and the optic nerve head in response to directed illumination of the macula (e.g., central portion of the visual field) using a coherent light source.
  • an imaging device for measurement and/or acquiring the images is mounted on typical a frame (e.g., a head-mounted display), such as a first frame with a power source coupled to the frame (e.g., a 3 Volt (V) battery).
  • a frame e.g., a head-mounted display
  • a power source coupled to the frame
  • the systems, methods, and devices utilized a microelectromechanical (MEMS) architecture for continuous, non-invasive monitoring of eye changes related to glaucoma, providing data on (i) trends in IOP and/or (ii) early changes within the retina in response to chronic elevation of eye pressures (e.g., i) trends in IOP and (ii) early changes within the retina in response to chronic elevation of eye pressures).
  • MEMS microelectromechanical
  • the systems, methods, and devices integrates both (i) and (ii) within a portable diagnostic footprint. Furthermore, in some embodiments, the systems, methods, and devices determine trends over time by determining measurement (e.g., quantifying values) for long periods of time, such as at least 12 hours during a day.
  • the imaging device includes a sensor that monitors compliance and usage patterns, which allows for providing a report for visualization, such as by a medical practitioner associated with the subject.
  • the systems and methods of the present disclosure enable an end-user, such as a clinician at a second computer system, which includes radiologists, pathologists, oncologists, or the like, to receive a similarly optimized subset of the encoded byte stream, such as for utilizing with high-throughput clinical decision making and diagnosis. Accordingly, in some embodiments, the systems and methods of the present disclosure match an optimal resolution for a plurality of graphical data of an encoded byte stream based on, for instance, a clinical use case and/or a form factor (e.g., hardware specifications) of a device including one or more feature extraction models configured to perform an evaluation on a respective modality of graphical data.
  • a form factor e.g., hardware specifications
  • the systems and methods of the present disclosure yield higher throughput, without negatively impacting clinical decision making as well as performance when using a respective computational model with the encoded byte stream, resulting in faster turnaround times, and reduced overall cost of data storage and transmission in comparison to conventional progressive encoding and/or decoding techniques.
  • one aspect of the present disclosure is directed to providing a method.
  • the method includes acquiring a plurality of images associated with a blink by an eye of a subject.
  • the acquiring the plurality of images is performed using a camera proximate to an eye of the subject configured to capture an image in the plurality of images at a range between 200 frames per second and 700 frames per second.
  • the camera includes a field of view.
  • the field of view includes a profile or substantially profile view of the eye of the subject.
  • the plurality of images includes a range between 2 images and 60 images.
  • a frequency of the acquiring the plurality of images is a range between 1.5 milliseconds (ms) per image and 3.5 ms per image.
  • the plurality of images is associated with at least two blinks by the eye of the subject.
  • the plurality of images is associated with a range between two blinks and two thousand blinks.
  • each respective image in the plurality of images is a two- dimensional image having a size of at most 500 kilobytes.
  • the acquiring the plurality of images includes concurrently illuminating a portion of the field of view that includes the profile or substantially the profile view of the eye of the subject.
  • the illuminating is performed using a first light source set associated with a wavelength in a range between 380 nm and 750 nm.
  • the illuminating is performed using a first light source set associated with a wavelength in a range between 750 nm and 1,000 nm.
  • the acquiring the plurality of images includes acquiring, when the illuminating the portion of the field of view, a corresponding value for a set of plurality of boundary conditions based upon a plurality of measurements associated with a region of interest (ROI) of the eye of the subject exposed to the light during the acquiring the plurality of images.
  • the acquiring the plurality of images includes interrupting the acquiring in accordance with a determination a boundary condition in the set of boundary conditions satisfies a threshold dimension.
  • the method includes quantifying a corneal response using the plurality of images.
  • the quantifying the corneal response includes determining a blinking phase of a respective blink by the eye of the subject; determining a contour of the cornea of the eye in accordance with a determination a dimension of the eye satisfies a threshold dimension. In some embodiments, the quantifying the corneal response includes determining a centroid of the cornea using the contour of the cornea. In some embodiments, the quantifying the corneal response includes determining a displacement of the centroid during some or all of a respective blinking phase of a first eye blink by the eye of the subject. In some embodiments, the quantifying the corneal response includes quantifying the corneal response by comparing the displacement of the centroid against a baseline displacement.
  • the determining the displacement of the centroid includes determining a time constant value associated with a velocity of the eye during the respective blinking phase.
  • the quantifying the corneal response comprises inputting the plurality of images into a first model that iteratively compare sets of images in the plurality of images until a set of images satisfies a threshold contour of the cornea, and wherein each respective temporally adjacent set of images is associated with a corresponding blink of the eye by the subject.
  • the first model is trained to compare temporally adjacent sets of images.
  • the first model is trained to compare sequential sets of images.
  • the first model is trained to compare collective sets of images.
  • the first model is a neural network architecture includes a plurality of parameters.
  • the plurality of parameters comprises at least 1 x 10 6 parameters.
  • the neural network architecture provides the categorization of each respective in the plurality of images by application of the at least 1 x 10 6 parameters to each image in the plurality of images.
  • the quantifying the corneal response comprises inputting the plurality of images into a second model that categorizes one or more sets of images in the plurality of images into one of a set of eye blink states, wherein the set of eye blink states includes at least an eye state associated with an open or substantially open eye and a second eye state associated with a closed or substantially closed eye.
  • the set of eye blink states comprises an open state, a partially open state, and a closed state.
  • the second model is a neural network architecture that includes a plurality of parameters, in which the plurality of parameters comprises at least 1 x 10 6 parameters, and the neural network architecture provides the categorization of each respective in the plurality of images by application of the at least 1 x 10 6 parameters to each image in the plurality of images.
  • the method includes quantifying an eye pressure of the subject using the corneal response.
  • the quantifying the eye pressure comprises quantifying an intraocular pressure of the eye of the subject.
  • the method includes quantifying a risk of glaucoma for the subject using the eye pressure of the subject.
  • the quantifying the risk of glaucoma comprises generating a value defining a comparison of the eye pressure of the against a threshold baseline pressure.
  • the threshold baseline pressure is in a range between 18 millimeters of mercury (mmHg) and 25 mmHg.
  • the acquiring, the quantifying the corneal response, the quantifying the eye pressure, and the quantifying the risk of glaucoma are performed in real time.
  • Figure 1 illustrates an exemplary system topology including a distributed computer system including a graphical data system and one or more imaging devices, in accordance with an exemplary embodiment of the present disclosure
  • Figures 2A and 2B collectively illustrates a graphical data system for, at least, acquiring a plurality of images, quantifying a corneal response, quantifying an eye pressure of the subject using the corneal response, quantifying a risk of glaucoma for the subject using the eye pressure of the subject, or a combination thereof.
  • Figure 3 illustrates an imaging device, in accordance with an embodiment of the present disclosure
  • Figures 4A, 4B, 4C, 4D, and 4E collectively illustrate exemplary methods for acquiring a plurality of images, quantifying a corneal response, quantifying an eye pressure of the subject using the corneal response, quantifying a risk of glaucoma for the subject using the eye pressure of the subject, or a combination thereof, in which optional embodiments are indicated by dashed boxes, in accordance with some embodiments of the present disclosure;
  • Figure 5 illustrates a view of an imaging device, in accordance with some embodiments of the present disclosure
  • Figure 6A is a side view of an imaging device, in accordance with some embodiments of the present disclosure.
  • Figure 6B is a rear view of an imaging device, in accordance with some embodiments of the present disclosure.
  • Figure 7 illustrates a graphical user interface for visualizing an eye of a subject using an imaging device, in accordance with some embodiments of the present disclosure
  • Figure 8A illustrates a plurality of images of an opening phase of a blink of an eye of a subject, in accordance with some embodiments of the present disclosure
  • Figure 8B illustrates a chart depicting different quantification for corneal dynamics during blinking, including (1) whole globe translation; (2) corneal deformation; and (3) superposition of globe translation and corneal deformation, in accordance with some embodiments of the present disclosure
  • Figure 8C illustrates a chart depicting an IOP as a function of corneal displacement and a baseline eye pressure, in accordance with some embodiments of the present disclosure
  • Figures 9A, 9B, 9C, and 9D collectively illustrate a chart depicting an evaluation of corneal dynamics during a blink, in which sequential images of the blink visualize the transition from a slightly open-eye state to a fully open-eye state, with a predicted corneal masks from a model overlaid in blue (dark gray) color, and the green (light gray) dot indicates the centroid of the corneal profile, in accordance with some embodiments of the present disclosure;
  • Figure 10 illustrates a chart depicting corneal centroid displacement along the x-axis during the eye-opening phase, including raw data (blue (dark gray) circles) and the corresponding exponential fit (red (gray) line) based on key metrics (T and A), in accordance with some embodiments of the present disclosure;
  • Figure 12 illustrates a chart depicting a correlation coefficient signal when the adaptive template matching algorithm is employed, in which onsets represent the starting points of the opening phase, whereas offsets represent the ending points of the opening phase, in accordance with some embodiments of the present disclosure
  • Figures 13, 14A, and 14B collectively illustrates a chart depicting corneal dynamics for a single participant in baseline and elevated IOP conditions, in which Figure 13 depicts normalized longitudinal centroid displacement during the blinks’ opening phase, the blue (dark gray) curves represent the displacement under baseline IOP condition, whereas the red (gray) curves correspond to the displacement under elevated IOP condition (Valsalva), in which normalization was applied only for visualization, as eye positions may vary across blinks recorded in different videos, Figure 14A depicts A of baseline versus elevated IOP conditions, and Figure 14B depicts T of baseline versus elevated IOP conditions, in accordance with some embodiments of the present disclosure;
  • FIGS 15A and 15B collectively illustrate charts depicting the in vivo ability of the imaging device a) differentiate between the two responses, corneal applanation and eyeball translation; and b) have sufficient spatio-temporal resolution to characterize the corneal applanation and rebound post-blinking, in which the ability of the instrument to separate the two phenomena using different markers, e.g. cornea vertex for applanation and lashes or pupil displacement for eyeball translation and that the corneal displacement is tracked with sufficient spatio-temporal resolution to analyze the magnitude of applanation and speed of rebound, further that the behavior of the cornea is consistent with the ex vivo data thus validating the ability to infer IOP, in accordance with some embodiments of the present disclosure;
  • markers e.g. cornea vertex for applanation and lashes or pupil displacement for eyeball translation
  • the corneal displacement is tracked with sufficient spatio-temporal resolution to analyze the magnitude of applanation and speed of rebound
  • Figure 16 illustrates a chart depicting a comparison of centroid displacement between normal and Valsalva blinks, in accordance with some embodiments of the present disclosure
  • Figure 17 illustrates a chart depicting a time constant, in accordance with some embodiments of the present disclosure
  • Figure 18 illustrates a chart depicting a rise time of an eye lid during a blink, in accordance with some embodiments of the present disclosure
  • Figure 19 illustrates a chart depicting a bilinear approximate, in accordance with some embodiments of the present disclosure
  • Figure 20 illustrates a chart depicting a normalized centroid displacement for all the acquired blinks (normal and Valsalva), in accordance with some embodiments of the present disclosure
  • Figures 21 and 22 collectively illustrates charts depicting a statistical analysis for 15 healthy subjects without ocular disease history, in which Figure 21 depicts T of normal and Valsalva IOP conditions across 15 samples (P ⁇ 0.05), and Figure 22 depicts A of normal and Valsalva IOP conditions across 15 samples (no statistically significant difference), in accordance with some embodiments of the present disclosure;
  • Figure 23 illustrates a flow chart of an adaptive template matching algorithm to detect the opening phase of a blink, in accordance with some embodiments of the present disclosure
  • Figure 24 illustrates exemplary logical functions that are used implemented in various embodiments of the present disclosure.
  • the present disclosure provides systems and methods for monitoring intraocular pressure are provided.
  • a plurality of images is acquired using a camera, in which the plurality of images is associated with a blink by an eye of a subject.
  • the plurality of images is associated with a plurality of blinks by the eye of the subject, such as a first blink during a first period of time and a second blink during the first period of time, which allows for capturing the eye during different points in time during the first period of time for a more accurate and precise data of the eye.
  • a corneal response is quantified using the plurality of images, such as some or all of the plurality of images.
  • the corneal response is defined, at least in part, by an application of the cornea caused by the blink (e.g., by pressure exerted on the cornea by the eyelid of the subject, etc. .
  • the present disclosure provides for determining the corneal response using non-invasive techniques, which is advantageous to users lacking access to medical practitioners.
  • an eye pressure of the subject is quantified using the corneal response, such as by comparing the eye pressure to a predetermined baseline eye pressure or a threshold baseline eye pressure determined, at least in part, by a characteristic of the subject, such as a prior eye pressure value associated with the subject.
  • the present disclosure is not limited thereto.
  • a risk of glaucoma is quantified for the subject using the eye pressure of the subject.
  • the subject or a medical practitioner associated with the subject is provided the risked of glaucoma without having to utilize an invasive or resource extensive process.
  • the risk of glaucoma is quantified using historic information (e.g., eye pressures) of the subject, which allows for individualizing the risk of glaucoma to the subject.
  • historic information e.g., eye pressures
  • first, second, etc. may be used herein to describe various elements, these elements should not be limited by these terms. These terms are only used to distinguish one element from another. For instance, a first subject could be termed a second subject, and, similarly, a second subject could be termed a first subject, without departing from the scope of the present disclosure. The first subject and the second subject are both subjects, but they are not the same subject.
  • phrase “if it is determined” or “if [a stated condition or event] is detected” may be construed to mean “upon determining” or “in response to determining” or “upon detecting [the stated condition or event]” or “in response to detecting [the stated condition or event],” depending on the context.
  • the term “about” or “approximately” can mean within an acceptable error range for the particular value as determined by one of ordinary skill in the art, which can depend in part on how the value is measured or determined, e.g., the limitations of the measurement system. For example, “about” can mean within 1 or more than 1 standard deviation, per the practice in the art. “About” can mean a range of ⁇ 20%, ⁇ 10%, ⁇ 5%, or ⁇ 1% of a given value. Where particular values are described in the application and claims, unless otherwise stated, the term “about” means within an acceptable error range for the particular value. The term “about” can have the meaning as commonly understood by one of ordinary skill in the art. The term “about” can refer to ⁇ 10%. The term “about” can refer to ⁇ 5%.
  • AxB denotes a resolution of two-dimensional graphical data in which “A” is a number of pixels in a horizontal direction and “B” is a number of pixels in a vertical direction.
  • classifier or “model” refers to a machine learning model or algorithm.
  • a model includes an unsupervised learning algorithm.
  • an unsupervised learning algorithm is cluster analysis.
  • a model includes supervised machine learning.
  • supervised learning algorithms include, but are not limited to, logistic regression, neural networks, support vector machines, Naive Bayes algorithms, nearest neighbor algorithms, random forest algorithms, decision tree algorithms, boosted trees algorithms, multinomial logistic regression algorithms, linear models, linear regression, Gradient Boosting, mixture models, hidden Markov models, Gaussian NB algorithms, linear discriminant analysis, or any combinations thereof.
  • a model is a multinomial classifier algorithm.
  • a model is a 2-stage stochastic gradient descent (SGD) model.
  • a model is a deep neural network (e.g., a deep-and-wide sample-level model).
  • the model is a neural network (e.g., a convolutional neural network and/or a residual neural network).
  • Neural network algorithms also known as artificial neural networks (ANNs), include convolutional and/or residual neural network algorithms (deep learning algorithms).
  • neural networks are machine learning algorithms that are trained to map an input dataset to an output dataset, where the neural network includes an interconnected group of nodes organized into multiple layers of nodes.
  • the neural network architecture includes at least an input layer, one or more hidden layers, and an output layer.
  • the neural network includes any total number of layers, and any number of hidden layers, where the hidden layers function as trainable feature extractors that allow mapping of a set of input data to an output value or set of output values.
  • a deep learning algorithm is a neural network including a plurality of hidden layers, e.g., two or more hidden layers.
  • each layer of the neural network includes a number of nodes (or “neurons”).
  • a node receives input that comes either directly from the input data or the output of nodes in previous layers, and performs a specific operation, e.g., a summation operation.
  • a connection from an input to a node is associated with a parameter (c.g, a weight and/or weighting factor).
  • the node sums up the products of all pairs of inputs, xi, and their associated parameters.
  • the weighted sum is offset with a bias, b.
  • the output of a node or neuron is gated using a threshold or activation function, f, which, in some instances, is a linear or non-linear function.
  • the activation function is, for example, a rectified linear unit (ReLU) activation function, a Leaky ReLU activation function, or other function such as a saturating hyperbolic tangent, identity, binary step, logistic, arcTan, softsign, parametric rectified linear unit, exponential linear unit, softPlus, bent identity, softExponential, Sinusoid, Sine, Gaussian, or sigmoid function, or any combination thereof.
  • ReLU rectified linear unit
  • Leaky ReLU activation function or other function such as a saturating hyperbolic tangent, identity, binary step, logistic, arcTan, softsign, parametric rectified linear unit, exponential linear unit, softPlus, bent identity, softExponential, Sinusoid, Sine, Gaussian, or sigmoid function, or any combination thereof.
  • the weighting factors, bias values, and threshold values, or other computational parameters of the neural network are “taught” or “learned” in a training phase using one or more sets of training data.
  • the parameters are trained using the input data from a training dataset and a gradient descent or backward propagation method so that the output value(s) that the ANN computes are consistent with the examples included in the training dataset.
  • the parameters are obtained from a back propagation neural network training process.
  • Examples include, but are not limited to, graph neural networks, feedforward neural networks, radial basis function networks, recurrent neural networks, residual neural networks, convolutional neural networks, residual convolutional neural networks, and the like, or any combination thereof.
  • the machine learning makes use of a pre-trained and/or transfer-learned ANN or deep learning architecture.
  • convolutional and/or residual neural networks are used, in accordance with the present disclosure.
  • a deep neural network model includes an input layer, a plurality of individually parameterized (e.g., weighted) convolutional layers, and an output scorer.
  • the parameters (e.g., weights) of each of the convolutional layers as well as the input layer contribute to the plurality of parameters (e.g., weights) associated with the deep neural network model.
  • at least 50 parameters, at least 100 parameters, at least 1,000 parameters, at least 2,000 parameters or at least 5,000 parameters are associated with the deep neural network model.
  • deep neural network models require a computer to be used because they cannot be mentally solved. In other words, given an input to the model, the model output needs to be determined using a computer rather than mentally in such embodiments.
  • Neural network algorithms including convolutional neural network algorithms, suitable for use as models are disclosed in, for example, Vincent et al., 2010, “Stacked denoising autoencoders: Learning useful representations in a deep network with a local denoising criterion,” J Mach Learn Res 11, pp. 3371-3408; Larochelle et al., 2009, “Exploring strategies for training deep neural networks, ””dec J Mach Learn Res 10, pp. 1-40; and Hassoun, 1995, Fundamentals of Artificial Neural Networks, Massachusetts Institute of Technology, each of which is hereby incorporated by reference.
  • Additional example neural networks suitable for use as models are disclosed in Duda et al., 2001, Pattern Classification, Second Edition, John Wiley & Sons, Inc., New York; and Hastie et al., 2001, The Elements of Statistical Learning, Springer-Verlag, New York, each of which is hereby incorporated by reference in its entirety. Additional example neural networks suitable for use as models are also described in Draghici, 2003, Data Analysis Tools for DNA Microarrays, Chapman & Hall/CRC; and Mount, 2001, Bioinformatics: sequence and genome analysis, Cold Spring Harbor Laboratory Press, Cold Spring Harbor, New York, each of which is hereby incorporated by reference in its entirety.
  • Ensembles of models and boosting are used.
  • a boosting technique such as AdaBoost is used in conjunction with many other types of learning algorithms to improve the performance of the model.
  • AdaBoost boosting technique
  • the output of any of the models disclosed herein, or their equivalents is combined into a weighted sum that represents the final output of the boosted model.
  • the plurality of outputs from the models is combined using any measure of central tendency known in the art, including but not limited to a mean, median, mode, a weighted mean, weighted median, weighted mode, etc.
  • the plurality of outputs is combined using a voting method.
  • a respective model in the ensemble of models is weighted or unweighted.
  • the term “parameter” refers to any coefficient or, similarly, any value of an internal or external element (e.g., a weight and/or a hyperparameter) in an algorithm, model, regressor, and/or classifier that can affect (e.g., modify, tailor, and/or adjust) one or more inputs, outputs, and/or functions in the algorithm, model, regressor and/or classifier.
  • a parameter refers to any coefficient, weight, and/or hyperparameter that can be used to control, modify, tailor, and/or adjust the behavior, learning, and/or performance of an algorithm, model, regressor, and/or classifier.
  • a parameter is used to increase or decrease the influence of an input (e.g., a feature) to an algorithm, model, regressor, and/or classifier.
  • a parameter is used to increase or decrease the influence of a node (e.g., of a neural network), where the node includes one or more activation functions. Assignment of parameters to specific inputs, outputs, and/or functions is not limited to any one paradigm for a given algorithm, model, regressor, and/or classifier but can be used in any suitable algorithm, model, regressor, and/or classifier architecture for a desired performance.
  • a parameter has a fixed value.
  • a value of a parameter is manually and/or automatically adjustable.
  • a value of a parameter is modified by a validation and/or training process for an algorithm, model, regressor, and/or classifier (e.g., by error minimization and/or backpropagation methods).
  • an algorithm, model, regressor, and/or classifier of the present disclosure includes a plurality of parameters.
  • the plurality of parameters is n parameters, where: n > 2; n > 5; n > 10; n > 25; n > 40; n > 50; n > 75; n > 100; n > 125; n > 150; n > 200; n > 225; n > 250; n > 350; n > 500; n > 600; n > 750; n > 1,000; n > 2,000; n > 4,000; n > 5,000; n > 7,500; n > 10,000; n > 20,000; n > 40,000; n > 75,000; n > 100,000; n > 200,000; n > 500,000, n > 1 x 10 6 , n > 5 x 10 6 , or n > 1 x 10 7 .
  • n is between 10,000 and 1 x 10 7 , between 100,000 and 5 x 10 6 , or between 500,000 and 1 x 10 6 .
  • the algorithms, models, regressors, and/or classifier of the present disclosure operate in a k-dimensional space, where k is a positive integer of 5 or greater (e.g., 5, 6, 7, 8, 9, 10, etc.). As such, the algorithms, models, regressors, and/or classifiers of the present disclosure cannot be mentally performed.
  • the term “untrained model” refers to a machine learning model or algorithm, such as a classifier or a neural network, that has not been trained on a target dataset.
  • “training a model” refers to the process of training an untrained or partially trained model (e.g., “an untrained or partially trained neural network”).
  • the term “untrained model” does not exclude the possibility that transfer learning techniques are used in such training of the untrained or partially trained model.
  • auxiliary training datasets that can be used to complement the primary training dataset in training the untrained model in the present disclosure.
  • two or more auxiliary training datasets, three or more auxiliary training datasets, four or more auxiliary training datasets or five or more auxiliary training datasets are used to complement the primary training dataset through transfer learning, where each such auxiliary dataset is different than the primary training dataset. Any manner of transfer learning is used, in some such embodiments. For instance, consider the case where there is a first auxiliary training dataset and a second auxiliary training dataset in addition to the primary training dataset.
  • the parameters learned from the first auxiliary training dataset (by application of a first model to the first auxiliary training dataset) are applied to the second auxiliary training dataset using transfer learning techniques (e.g., a second model that is the same or different from the first model), which in turn results in a trained intermediate model whose parameters are then applied to the primary training dataset and this, in conjunction with the primary training dataset itself, is applied to the untrained model.
  • transfer learning techniques e.g., a second model that is the same or different from the first model
  • a first set of parameters learned from the first auxiliary training dataset (by application of a first model to the first auxiliary training dataset) and a second set of parameters learned from the second auxiliary training dataset (by application of a second model that is the same or different from the first model to the second auxiliary training dataset) are each individually applied to a separate instance of the primary training dataset (e.g., by separate independent matrix multiplications) and both such applications of the parameters to separate instances of the primary training dataset in conjunction with the primary training dataset itself (or some reduced form of the primary training dataset such as principal components or regression coefficients learned from the primary training set) are then applied to the untrained model in order to train the untrained model.
  • model z refers to the z th model in a plurality of models (e.g., a model 118-z in a plurality of models 118).
  • descriptions of devices and systems will include implementations of one or more computers.
  • a graphical data system 200 is represented as a single device that includes all the functionality of a computer system.
  • an imaging device 300 is represented as a single device that includes all the functionality of a computer system.
  • the present disclosure is not limited thereto.
  • the functionality of the graphical data system 200 is spread across any number of networked computers and/or reside on each of several networked computers and/or by hosted on one or more virtual machines and/or containers at a remote location accessible across a communications network (c.g, communications network 186 of Figure 1) and/or the functionality of the imaging device 300 is spread across any number of networked computers and/or reside on each of several networked computers and/or by hosted on one or more virtual machines and/or containers.
  • a communications network c.g, communications network 186 of Figure 1
  • the functionality of the imaging device 300 is spread across any number of networked computers and/or reside on each of several networked computers and/or by hosted on one or more virtual machines and/or containers.
  • the illustrated devices and systems may wirelessly transmit information between each other.
  • the exemplary topology shown in Figure 1 merely serves to describe the features of an embodiment of the present disclosure in a manner that will be readily understood to one of skill in the art.
  • Figures 2A and 2B collectively depicts a block diagram of a graphical data system 200 according to some embodiments of the present disclosure.
  • the graphical data system 200 at least facilitates quantifying a corneal response using aplurality of images (e.g., images captured using the imaging device 300 of Figure 5, etc.), quantifying an eye pressure of the subject using the corneal response, quantifying a risk of glaucoma for the subject using the eye pressure of the subject, or a combination thereof.
  • the communication network 186 optionally includes the Internet, one or more local area networks (LANs), one or more wide area networks (WANs), other types of networks, or a combination of such networks.
  • LANs local area networks
  • WANs wide area networks
  • Examples of communication networks 186 include the World Wide Web (WWW), an intranet and/or a wireless network, such as a cellular telephone network, a wireless local area network (LAN) and/or a metropolitan area network (MAN), and other devices by wireless communication.
  • WWW World Wide Web
  • LAN wireless local area network
  • MAN metropolitan area network
  • the wireless communication optionally uses any of a plurality of communications standards, protocols and technologies, including Global System for Mobile Communications (GSM), Enhanced Data GSM Environment (EDGE), high-speed downlink packet access (HSDPA), high-speed uplink packet access (HSUPA), Evolution, Data-Only (EV-DO), HSPA, HSPA+, Dual-Cell HSPA (DC-HSPDA), long term evolution (LTE), near field communication (NFC), wideband code division multiple access (W-CDMA), code division multiple access (CDMA), time division multiple access (TDMA), Bluetooth, Wireless Fidelity (Wi-Fi) (e.g., IEEE 802.
  • GSM Global System for Mobile Communications
  • EDGE Enhanced Data GSM Environment
  • HSDPA high-speed downlink packet access
  • HUPA high-speed uplink packet access
  • Evolution, Data-Only (EV-DO) Evolution, Data-Only
  • HSPA HSPA+
  • DC-HSPDA Dual-Cell HSPA
  • LTE long term evolution
  • NFC near
  • I la IEEE 802.1 lac, IEEE 802.1 lax, IEEE 802.1 lb, IEEE 802.11g and/or IEEE 802.1 In
  • VoIP voice over Internet Protocol
  • WiMAX a protocol for e-mail (e.g., Internet message access protocol (IMAP) and/or post office protocol (POP)), instant messaging (e.g., extensible messaging and presence protocol (XMPP), Session Initiation Protocol for Instant Messaging and Presence Leveraging Extensions (SIMPLE), Instant Messaging and Presence Service (IMPS)), and/or Short Message Service (SMS), or any other suitable communication protocol, including communication protocols not yet developed as of the filing date of this document.
  • IMAP Internet message access protocol
  • POP post office protocol
  • instant messaging e.g., extensible messaging and presence protocol (XMPP), Session Initiation Protocol for Instant Messaging and Presence Leveraging Extensions (SIMPLE), Instant Messaging and Presence Service (IMPS)
  • SMS Short Message Service
  • the graphical data system 200 includes one or more processing units (CPUs) 172, a network or other communications interface 174, and memory 192.
  • CPUs processing units
  • memory 192 memory
  • the graphical data system 200 includes a user interface 176.
  • the user interface 176 typically includes a display 178 for presenting media, such as a result by a plurality of models (e.g., first model 118-1, second model 118-2, . . ., model X 118-X of Figure 2B), a graphical user interface of a client application (e.g., graphical user interface 700 of Figure 7), or a portion (e.g., some or all) of a plurality of images captured by an imaging device 300.
  • the display 178 is integrated within the graphical data system 200 (e.g., housed in the same chassis as the CPU 172 and the memory 192).
  • the graphical data system 200 includes one or more input device(s) 180, which allow a subject to interact with the graphical data system 200.
  • the input devices 180 include a keyboard, a mouse, and/or other input mechanisms.
  • the display 178 includes a touch-sensitive surface (e.g., where display 178 is a touch-sensitive display or the graphical data system 200 includes a touch pad).
  • the graphical data system 200 presents media to a user through the display 178.
  • Examples of media presented by the display 178 include one or more images, a video, audio (e.g., waveforms of an audio sample), or a combination thereof.
  • the one or more images, the video, the audio, or the combination thereof is presented by the display 178 through a client application 130.
  • the audio is presented through an external device (e.g., speakers, headphones, input/output (I/O) subsystem, etc.) that receives audio information from the graphical data system 200 and presents audio data based on this audio information.
  • an external device e.g., speakers, headphones, input/output (I/O) subsystem, etc.
  • the user interface 176 also includes an audio output device, such as speakers or an audio output for connecting with speakers, earphones, or headphones.
  • the memory 192 includes high-speed random access memory, such as DRAM, SRAM, DDR RAM, or other random access solid state memory devices, and optionally also includes non-volatile memory, such as one or more magnetic disk storage devices, optical disk storage devices, flash memory devices, or other non-volatile solid state storage devices.
  • the memory 192 may optionally include one or more storage devices remotely located from the CPU(s) 172.
  • the memory 192, or alternatively the non-volatile memory device(s) within the memory 192 includes a non-transitory computer readable storage medium.
  • the memory 192 can include mass storage that is remotely located with respect to the CPU(s) 172.
  • some data stored in memory 192 may in fact be hosted on devices that are external to the graphical data system 200, but that can be electronically accessed by the graphical data system 200 over an Internet, intranet, or other form of network 186 or electronic cable using communication interface 184.
  • the memory 192 of the graphical data system 200 optimizing decoding of graphical data 108 stores:
  • an operating system 102 e.g., ANDROID, iOS, DARWIN, RTXC, LINUX, UNIX, OS X, WINDOWS, or an embedded operating system such as VxWorks) that includes procedures for handling various basic system services;
  • an operating system 102 e.g., ANDROID, iOS, DARWIN, RTXC, LINUX, UNIX, OS X, WINDOWS, or an embedded operating system such as VxWorks
  • an operating system 102 e.g., ANDROID, iOS, DARWIN, RTXC, LINUX, UNIX, OS X, WINDOWS, or an embedded operating system such as VxWorks
  • an electronic address 104 associated with the graphical data system 200 that identifies graphical data system 200 (e.g., within the communication network 186);
  • a graphical data store 106 that stores a record of graphical data (e.g., first plurality of graphical data 108-1, second one or more graphical data 108-2, . . ., graphical data C 108-C of Figure 2A), each graphical data 108 is defined by a plurality of characteristics (e.g., first plurality of characteristics 110-1 of Figure 2A), that collectively characterize a corresponding graphical data 108 by one or more characteristics (e.g., value of first characteristic 112-1-1, value of second characteristic 112-2-1, . .
  • model library 116 that retains a plurality of models (e.g., first model 118-1, second model 118-2, . .
  • a camera control model 122 that includes a light control module 124 for controlling illumination of a field of view associated with one or more cameras 504 and an image capture module 126 for facilitating capture of one or more images 710 using at least one camera 702 in the one or more cameras 504; and
  • a client application 130 for presenting information (e.g., media) using a display 178 of the graphical data system 200.
  • the graphical data system 200 includes an operating system 102 that includes procedures for handling various basic system services.
  • the operating system 102 e g., iOS, ANDROID, DARWIN, RTXC, LINUX, UNIX, OS X, WINDOWS, or an embedded operating system such as VxWorks
  • the operating system 102 includes various software components and or drivers for controlling and managing general system tasks (e.g., memory management, storage device control, power management, etc.) and facilitates communication between various hardware and software components.
  • an optional electronic address 104 is associated with the graphical data system 200.
  • the optional electronic address 104 is utilized to at least uniquely identify the graphical data system 200 from other devices and components of the distributed system 100, such as other client devices 300 having access to the communications network 186.
  • the electronic address 104 is utilized to receive a request from an imaging device 300 to communicate a one or more images 710 captured at the imaging device 30, such as in real-time or after deemed to have completed capture of each image 710 in the one or more images 710.
  • the present disclosure is not limited thereto.
  • the graphical data system 200 includes a graphical data store 106 that stores a variety of image data 108, such as one or more sets of a respective plurality of images 702.
  • the graphical data store 106 is configured to retain between three and 20 sets of image data associated with one or more subjects, between 5 and 40 sets of images, between 15 and 100 sets of images, or between 25 and 150 sets of images.
  • Each respective image data 108 is defined by a corresponding plurality of characteristics (e.g., first plurality of characteristics 110-1 define first image data 108-1 of Figure 2A).
  • each respective plurality of images 710 is defined by between three and 20 characteristics 112, between 5 and 40 characteristics, between 15 and 100 characteristics, or between 25 and 150 characteristics. In some embodiments, each respective plurality of images 710 is defined by between three and 20 characteristics 112, between 5 and 40 characteristics, between 15 and 100 characteristics, or between 25 and 150 characteristics. In some embodiments, each respective plurality of images 710 is defined by at least 5 characteristics 112, at least 15 characteristics, at least 25 characteristics, at least 50 characteristics, at least 100 characteristics, or at least 150 characteristics. In some embodiments, each respective plurality of images 710 is defined by at most 5 characteristics 112, at most 15 characteristics, at most 25 characteristics, at most 50 characteristics, at most 100 characteristics, or at most 150 characteristics 112.
  • the plurality of characteristics 110 characterize the corresponding image data 108 by one or more characteristics 112.
  • the one or more characteristics provide information about one or more aspects of the corresponding graphical data 108.
  • the one or more characteristics 112 includes a first characteristic 112-1 associated with a corresponding modality of the plurality of images 710 (e.g., a first value of the first characteristic 112-1 is associated with a first modality, a second value of the first characteristic 112-1 is associated with a second modality different than the first modality, etc.), a second characteristic 112-2 associated with a sampling resolution of the plurality of images 710, a third characteristic 112-3 associated with a file format of the plurality of images 710 a, a fourth characteristic 112-4 associated with a subject matter (e.g., content) of the plurality of images 710, a fifth characteristic 112-5 associated with a capture setting (e.g., camera setting) associated with the plurality of images 710, or a combination thereof.
  • a first characteristic 112-1 associated with
  • the third characteristic 112-3 associated with the file format allows for retaining images 710 in any electronic image file format, including but not limited to JPEG/JFIF, TIFF, Exif, PDF, EPS, GIF, BMP, PNG, PPM, PGM, PBM, PNM, WebP, HDR raster formats, HEIF, BAT, BPG, DEEP, DRW, ECW, FITS, FLIF, ICO, ILBM, IMG, PAM, PCX, PGF, JPEG XR, Layered Image File Format, PLBM, SGI, SID, CD5, CPT, PSD, PSP, XCF, PDN, CGM, SVG, PostScript, PCT, WMF, EMF, SWF, XAML, and/or RAW.
  • the ability of the graphical data system 200 to retain a variety of electronic allows for compressing and reconstructing the plurality of images 710
  • the plurality of images 710 is obtained in any electronic color mode, including but not limited to grayscale, bitmap, indexed, RGB, CMYK, HSV, lab color, duotone, and/or multichannel.
  • the plurality of images 710 is manipulated (e.g., stitched, compressed and/or flattened).
  • the plurality of images 710 includes between 1 million and 25 million pixels. In some embodiments, each resolution is represented by five or more, ten or more, 100 or more, 1,000 or more contiguous pixels in an image. In some embodiments, each resolution is represented by between 1,000 and 250,000 contiguous pixels in a native image 125.
  • the plurality of images 710 is represented as an array (e.g., matrix) including a plurality of pixels, such that the location of each respective pixel in the plurality of pixels in the array (e.g., matrix) corresponds to its original location in the image.
  • the plurality of images 710 is represented as a vector including a plurality of pixels, such that each respective pixel in the plurality of pixels in the vector includes spatial information corresponding to its original location in an image 710 in the plurality of images 710.
  • a pixel includes one or more pixel values (e.g., an intensity value).
  • each respective pixel in the plurality of pixels includes one pixel intensity value, such that the plurality of pixels represents a single-channel image including a one-dimensional integer vector including the respective pixel values for each respective pixel.
  • an 8-bit single-channel image e.g., grey-scale
  • includes 2 8 or 256 different pixel values e.g., 0-255).
  • each respective pixel in the plurality of pixels of an image includes a plurality of pixel values, such that the plurality of pixels represents a multi-channel image including a multidimensional integer vector, where each vector element represents a plurality of pixel values for each respective pixel.
  • a 24-bit 3-channel image e.g., RGB color
  • 2 24 e.g., 2 8x3
  • each vector element includes 3 components, each between 0-255.
  • an //-bit image of the plurality of graphical data includes up to 2" different pixel values, where n is any positive integer. See, Uchida, 2013, “Image processing and recognition for biological images,” Develop. Growth Differ., 55, pg. 523-549, doi: 10.1111/dgd.12054, which is hereby incorporated herein by reference in its entirety for all purposes.
  • Neural network models 118 include conditional random fields models 118, convolutional neural network (CNN) models 118, attention based neural network models 118, deep learning models 118, long short term memory network model 118, or other neural models 118.
  • CNN convolutional neural network
  • an imaging device 300 includes a smart phone (e.g., an iPhone, an Android device, etc.), a laptop computer, a tablet computer, a desktop computer, a wearable device (e.g., a smart watch, a heads-up display (HUD) device, imaging device of Figure 5, imaging device 300 of Figure 6A, imaging device 300 of Figure 6B, etc.), a television (e.g., a smart television), or another form of electronic device such as a gaming console, a stand-alone device, and the like.
  • a smart phone e.g., an iPhone, an Android device, etc.
  • a laptop computer e.g., a tablet computer, a desktop computer
  • a wearable device e.g., a smart watch, a heads-up display (HUD) device, imaging device of Figure 5, imaging device 300 of Figure 6A, imaging device 300 of Figure 6B, etc.
  • a television e.g., a smart television
  • another form of electronic device such as a gaming console
  • the imaging device 300 illustrated in Figure 3 has one or more processing units (CPU’s) 272, a network or other communications interface 274, a memory 292 (e.g., random access memory), a user interface 276, the user interface 276 including a display 278 and input 280 (e.g., keyboard, keypad, touch screen, etc.), optional audio circuitry, an optional speaker, an optional microphone, an optional input/output (I/O) subsystem, one or more communication busses 270 for interconnecting the aforementioned components, and a power system (e.g., power supply) for powering the aforementioned components.
  • CPU processing unit
  • memory 292 e.g., random access memory
  • user interface 276 the user interface 276 including a display 278 and input 280 (e.g., keyboard, keypad, touch screen, etc.), optional audio circuitry, an optional speaker, an optional microphone, an optional input/output (I/O) subsystem, one or more communication busses 270 for interconnecting the aforementioned components,
  • the imaging device 300 illustrated in Figure 3 optionally includes, in addition to accelerometer(s), a magnetometer, and a global positioning system (GPS or GLONASS or other global navigation system) receiver for obtaining information concerning a current location (e.g., a latitude, a longitude, an elevation, etc.) and/or an orientation (e.g., a portrait or a landscape orientation of the device) of the imaging device 300.
  • a current location e.g., a latitude, a longitude, an elevation, etc.
  • an orientation e.g., a portrait or a landscape orientation of the device
  • the imaging device 300 illustrated in Figure 3 is only one example of a multifunctional device that may be used for acquiring a plurality of images associated with a blink by an eye of a subject, quantifying a corneal response using the plurality of images, quantifying an eye pressure of the subject using the corneal response, quantifying a risk of glaucoma for the subject using the eye pressure of the subject, or a combination thereof.
  • the imaging device 300 optionally has more or fewer components than shown, optionally combines two or more components, or optionally has a different configuration or arrangement of the components.
  • the various components shown in Figure 3 are implemented in hardware, software, firmware, or a combination thereof, including one or more signal processing and/or application specific integrated circuits.
  • the one or more CPU(s) 272 run or execute various software programs and/or sets of instructions stored in the memory 292, such as the client application 230, to perform various functions for the imaging device 300 and process data.
  • the audio circuitry, the optional speaker, and the optional microphone provide an audio interface between the respective subject and the imaging device 300, enabling the imaging device to provide a message that include audio data provided through the audio circuitry, the optional speaker, and/or the optional microphone.
  • the audio circuitry receives audio data from the peripherals interface, converts the audio data to electrical signals, and transmits the electrical signals to the speaker.
  • the speaker converts the electrical signals to human-audible sound waves.
  • the audio circuitry also receives electrical signals converted by the microphone from sound waves.
  • the audio circuitry converts the electrical signal to audio data and transmits the audio data to peripherals interface for processing. Audio data is, optionally, retrieved from and or transmitted to the memory 292 and or the RF circuitry by the peripherals interface.
  • a camera control model 222 that includes a light control module 224 for controlling illumination of a field of view associated with one or more cameras 504 and an image capture module 226 for facilitating capture of one or more images 710 using at least one camera 702 in the one or more cameras 504;
  • a client application 130 for presenting information (e.g., media) using a display 278.
  • the imaging device 300 preferably includes an operating system 202 that includes procedures for handling various basic system services.
  • the operating system 202 e g., iOS, ANDROID, DARWIN, RTXC, LINUX, UNIX, OS X, WINDOWS, or an embedded operating system such as VxWorks
  • the operating system 202 includes various software components and or drivers for controlling and managing general system tasks (e.g., memory management, storage device control, power management, etc.) and facilitates communication between various hardware and software components.
  • the imaging system developed to track the corneal profile during eye blinking is depicted in 6A.
  • the setup comprises a modified ophthalmology slit lamp and an imaging lens (focal length of 50 mm, numerical aperture of 0.18; Thorlabs, Newton, NJ).
  • This system is integrated with a high-frame-rate (510 FPS) camera (Allied Vision, Edmund Optics, Barrington, NJ) placed lateral to the participant’s eye to capture the corneal profile during each blink.
  • the videos were acquired in 8-bit grayscale format with a spatial resolution of 800x600 pixels.
  • a visible LED ring light aligned concentrically with the camera lens was employed as the illumination source, allowing a broader dynamic range [0-255] of grayscale values for improved image quality.
  • An example lateral eye image is shown in 6B.
  • This Example included healthy volunteers aged 18-50 without history of ocular or systemic conditions that could influence IOP. All participants provided written informed consent. This Example was conducted in compliance with the Declaration of Helsinki and approved by the Institutional Review Board (IRB) of the University of Maryland Baltimore. Each participant was assessed under two experimental conditions to examine corneal dynamics during a natural, complete blink (baseline) and during a Valsalva maneuver. For each participant, data were acquired only from the left eye.
  • IRS Institutional Review Board
  • the experimental protocol included four steps. First, baseline IOP was measured using a portable tonometer (iCare IC200, Icare USA, Inc., Raleigh, NC, USA) an FDA- approved medical device comparable to the Goldmann Applanation Tonometer (GAT).
  • a portable tonometer iCare IC200, Icare USA, Inc., Raleigh, NC, USA
  • GAT Goldmann Applanation Tonometer
  • a software program (StreamPix, NorPix Inc., Montreal, CA) was used to record blinks under two conditions: baseline and Valsalva.
  • baseline condition single or multiple videos totaling one minute in duration were recorded, during which participants were asked to perform natural blinks.
  • Valsalva condition multiple videos were recorded as each maneuver lasted approximately 15 seconds. A minimum of five blinks was considered acceptable for each condition.
  • a custom Python script was used to train a neural network specifically designed to predict eye masks during the eye-opening phase of each blink.
  • the predicted ocular masks were subsequently assessed for the dynamics of the corneal profile during each blink.
  • An exponential-like curve was produced for all the blinks by evaluating the longitudinal corneal displacement over time. The following parameters were computed: (i) the time constant, which quantifies the velocity at which the cornea rebounds during a natural, complete blink, and (ii) the displacement amplitude, which represents the extent of translation of the corneal profile ( Figure 10).
  • Frames related to the opening phase of blinking were classified into four different classes: closed eye, semi-closed eye, semi-open eye, and open eye.
  • a resampling method based on the mask area was applied to address the potential class imbalance.
  • Masks were categorized as follows: (i) closed eye: masks with zero area, (ii) semi-closed eye: masks with an area between 0% and 40% of the maximum observed area, (iii) semi-open eye: masks with an area between 40% and 80% of the maximum observed area, (iv) open eye: masks with an area greater than 80% of the maximum observed area.
  • An example of the classification of these images based on the mask area is shown in Figure 11.
  • a filtering step was applied to the fitted curves based on their fitted parameters as the root mean square error (RMSE) and R 2 . Specifically, R 2 values below /J. R 2 — 3 ⁇ J R 2 and RMSE values exceeding /J. RMS E + O RM SE were considered outliers.
  • RMSE root mean square error
  • This filtering process allows us to exclude blinks with poorly fitted curves, resulting from segmentation errors, non-natural blinks, or head movements during the blink acquisition process.
  • T and A the two parameters of the fitted curves
  • FIG. 13 shows the corneal centroid displacement along the x-axis during the blink’s opening phase for a single participant. In both conditions, the trajectory of the corneal centroid exhibits an exponential trend, stabilizing as the eye reaches its fully open state.
  • Figure 21 illustrates the variation of T for each participant who underwent the experiment. Out of 15 samples, 11 exhibit the expected trend, showing a lower T under the elevated IOP condition compared to the baseline IOP condition.
  • Figure 14A depicts the changes in A for each participant across the two IOP conditions, with the color scale indicating the change in the amplitude A between baseline and elevated IOP conditions.
  • the average amplitude was 35.25 ⁇ 2.73 px for the baseline IOP and 34.81 ⁇ 2.64 px for the elevated IOP condition.
  • A does not exhibit a clear or consistent pattern across participants, with changes appearing more variable and without a statistically significant difference.
  • This study presents a novel imaging system that provides information on the corneal dynamics during a natural, complete blink and its correlation with IOP changes.
  • the corneal dynamics were analyzed under two conditions: baseline IOP and elevated IOP, obtained during the Valsalva maneuver.
  • the deformation of the cornea by the eyelid during a blink provides an opportunity to assess lOP-dependence of this phenomenon.
  • the corneal deformation depends on its topography and biomechanics, leading to observable changes in corneal dynamics.
  • various structural and pathological conditions may be potential modifiers of the observed corneal response. For instance, keratoconus leads to localized thinning and steepening of the cornea, altering its curvature and mechanical stiffness, which can modify the corneal response to ELP.
  • post-surgical eyes typically exhibit a lower corneal hysteresis, which reflects the viscoelastic damping response of the corneal tissue to the applied force and corneal resistance factor, which is related to the time-independent corneal response to the applied force.
  • corneal resistance factor which is related to the time-independent corneal response to the applied force.
  • ELP causes a flattening effect on the cornea, with biomechanical IOP correlating with eyelid pressure and corneal deformation during a blink.
  • the results demonstrate that the velocity of the corneal rebound is sensitive to IOP changes: the observed decrease of T under elevated IOP conditions suggests that the cornea rebounds more rapidly after eyelid-induced deformation. This Example hypothesized that this is due to the increased internal pressure exerting a greater restoring force, leading to faster recovery of the corneal profile.
  • A did not significantly differ between conditions, indicating that the extent of corneal deformation is consistent across the two conditions.
  • the systems, methods, and devices of the present disclosure provide continuous non-contact IOP monitoring without requiring significant patient expertise.
  • a more accessible and frequent monitoring solution like this can lead to a better understanding of IOP patterns and their relationship to glaucoma progression.
  • a study of IOP monitoring conducted outside of regular office hours found that peak 24-hour IOP was greater than the peak IOP observed during prior office visits in 62% of the patients.
  • the results of 24-hour IOP monitoring led to an immediate treatment change in 36% of patients. Indeed, this technology could also promote more precise patientbased treatment strategies and mitigate the impact of untreated glaucoma.
  • glaucoma Several types exist, the most common being primary open-angle glaucoma which is characterized by elevated IOP values beyond the normal range and progressive optic nerve damage. However, not all forms of glaucoma are associated with increased IOP. In normal-tension glaucoma, IOP values remain within the clinically normal range, yet progressive optic nerve damage can still occur. In such cases, analyzing IOP fluctuations throughout the day could be beneficial for the early detection of the disease, as patients with normal-tension glaucoma often exhibit greater diurnal IOP variability, even if additional lOP-independent factors have gained increasing relevance in detecting this ocular disease. The proposed method can potentially assess these variations through frequent, non- invasive, home-based measurements.
  • ocular hypertension is characterized by IOP levels above the normal threshold despite the absence of optic nerve damage.
  • the absolute IOP value remains the primary diagnostic metric.
  • a calibration step is performed before conducting absolute IOP monitoring.
  • the variability of IOP elevation induced by the Valsalva maneuver presented challenges in achieving consistent IOP increments across trials. Additionally, IOP measurements during the Valsalva maneuver are taken before the acquisition of natural blinks, as it was not feasible to measure IOP using a portable tonometer and simultaneously record natural blinks during the application of the maneuver. This sequential process may introduce variability in the exact elevated IOP level during blink acquisition.
  • this technology could support more precise treatment optimization, improve medication adherence, and reduce the risk of vision loss, making it a valuable tool for glaucoma management.
  • An imaging device 300 was further configured to determine deformation of a cornea.
  • the imaging device 300 detects a blink, detects an edge of a cornea, determines a reference point of the cornea, and determines displacement of the reference point.
  • the reference point was an arbitrary point on a cornea or pertain to a physical or geometric property of the cornea, such as a centroid of the cornea.
  • displacement of the reference point was the difference in location, such as at the reference point or other points on the cornea, from a first time and a second time.
  • the first time is a time preceding the blink and the second time is a time following the blink.
  • the difference in location is measured from a point on curve configured to a model (e.g., such as a first-order system or a second-order system) a cornea.
  • the imaging device 300 is configured to be a take-home or easily accessible platform for patients to keep track of their IOP, which will then help make more informed and accurate treatment decisions.
  • the first method of measuring IOP with the imaging device 300 is using a laser reflectance configuration.
  • an eye-safe IR laser is directed from in front of the eye onto the center of the cornea.
  • a front facing high-speed IR camera measures the reflected laser spot coming off the eye.
  • the angle of the reflected beam changes, which is indicated by vertical and lateral shifts of the spot as seen by the camera’s image sensor. Analysis of how this spot moves over time can tell how much the cornea is deforming, and thereby measure the IOP.
  • Normal-tension glaucoma is it different from primary open-angle glaucoma?: Current Opinion in Ophthalmology. 2008; 19: 85-88; Jonas JB, Wang N, Wang YX, You QS, Yang D, Xu L. Ocular hypertension: general characteristics and estimated cerebrospinal fluid pressure. The Beijing Eye Study 2011. PLoS One. 2014; 9: el00533; Evinger C, Shaw MD, Peck CK, Manning KA, Baker R. Blinking and associated eye movements in humans, guinea pigs, and rabbits. J Neurophysiol. 1984; 52: 323-339; Bour LJ, Aramideh M, Ongerboer De Visser BW.
  • the present invention can be implemented as a computer program product that includes a computer program mechanism embedded in a non-transitory computer-readable storage medium.
  • the computer program product could contain instructions for operating the user interfaces disclosed herein.
  • These program modules can be stored on a CD-ROM, DVD, magnetic disk storage product, USB key, or any other non-transitory computer readable data or program storage product.

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  • Eye Examination Apparatus (AREA)

Abstract

L'invention concerne des systèmes, des procédés et des dispositifs de surveillance de la pression intraoculaire. Une pluralité d'images est acquise à l'aide d'une caméra, dans laquelle la pluralité d'images est associée à un clignement d'oeil d'un sujet. Une réponse cornéenne est quantifiée à l'aide de la pluralité d'images. Une pression oculaire du sujet est quantifiée à l'aide de la réponse cornéenne. Un risque de glaucome est quantifié pour le sujet à l'aide de la pression oculaire du sujet.
PCT/US2025/034691 2024-06-21 2025-06-21 Systèmes, procédés et dispositifs de surveillance de la pression intraoculaire Pending WO2025265109A1 (fr)

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US202463662660P 2024-06-21 2024-06-21
US63/662,660 2024-06-21
US202563741527P 2025-01-03 2025-01-03
US63/741,527 2025-01-03

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Citations (5)

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US20110237999A1 (en) * 2010-03-19 2011-09-29 Avedro Inc. Systems and methods for applying and monitoring eye therapy
US20160183784A1 (en) * 2007-09-14 2016-06-30 Neuroptics, Inc. Pupilary Screening System and Method
US20160270656A1 (en) * 2015-03-16 2016-09-22 Magic Leap, Inc. Methods and systems for diagnosing and treating health ailments
US20220007934A1 (en) * 2020-07-07 2022-01-13 Thomas Daniel Raymond Apparatus and method for automated non-contact eye examination
US20220361751A1 (en) * 2020-01-28 2022-11-17 Smartlens, Inc. Wearable device and method for remote optical monitoring of intraocular pressure

Patent Citations (5)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US20160183784A1 (en) * 2007-09-14 2016-06-30 Neuroptics, Inc. Pupilary Screening System and Method
US20110237999A1 (en) * 2010-03-19 2011-09-29 Avedro Inc. Systems and methods for applying and monitoring eye therapy
US20160270656A1 (en) * 2015-03-16 2016-09-22 Magic Leap, Inc. Methods and systems for diagnosing and treating health ailments
US20220361751A1 (en) * 2020-01-28 2022-11-17 Smartlens, Inc. Wearable device and method for remote optical monitoring of intraocular pressure
US20220007934A1 (en) * 2020-07-07 2022-01-13 Thomas Daniel Raymond Apparatus and method for automated non-contact eye examination

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