Method and system for early detection of neurodegeneration using progressive tracking of eye-markers

1. A method for the early detection of neurodegeneration, comprising the steps of:

a) extracting samples of a plurality of eye-markers of a user from a video stream captured by a visible light camera;

b) loading said samples of said plurality of eye-markers to a big data repository, analyzing and consolidating them into one biomarker for detecting multiple disorders by means of training a machine learning model; and

c) determining the risk of said user to develop a neurodegenerative disease using said trained machine learning model as part of an early detection screening or diagnosis process.

2. The method according to claim 1 , further comprising analyzing said samples of plurality of eye-markers, transforming some of said samples into derived features, to generate multiple features optimized for use within a machine learning model, using computational preprocessing methods for feature extraction.

3. The method according to claim 2 , wherein computational methods are selected from one or more of: Fourier transform, wavelet analysis differentiating certain frequency bands, criticality metrics based on Lyapunov exponents and fractal dimension analysis.

4. The method according to claim 1 , wherein one or more eye markers are extracted at a time.

5. The method according to claim 1 , wherein the eye-markers extracted include the following: pupil size, eye movements, blinks and gaze.

6. The method according to claim 2 , wherein the eye-markers are collected separately from each eye of the user as separate features of the machine learning model.

7. The method according to claim 6 , wherein the derived features are based on features of each eye separately or on a function of the features of the two eyes.

8. The method according to claim 1 , wherein the step of extracting samples is performed using one of the following: webcam, smartphone front or back camera, a camera of a Virtual Reality device, a camera of an Augmented Reality device, a camera of a wearable device or near IR-based eye-tracker.

9. The method according to claim 1 , wherein the analysis of eye-markers, machine learning and consolidation to biomarker take place over the cloud or on a local computer.

10. The method according to claim 1 , wherein additional machine learning features, besides the eye-markers, are collected from other device sensors during the eye tracking sessions.

11. The method according to claim 1 , further comprising a user registration step, wherein a face recognition algorithm is used during user registration to differentiate the identity of said user from other occasional users of the device.

12. A system for the early detection of neurodegeneration, comprising:

a) a camera of a mobile device or of Virtual Reality device or of an Augmented reality device for capturing video of one or both of the eyes of a user and extracting a plurality of eye marker samples;

b) a big data repository for aggregating said plurality of eye-marker samples over time; and

c) a machine learning module for analyzing said captured plurality of eye marker samples for consolidating said plurality of eye markers into a trained machine learning model reflecting a single biomarker; thereby, providing a prediction of an early stage diagnosis determining the risk of said user to develop neurodegenerative diseases.

13. The method according to claim 1 , wherein the consolidating step comprises applying the following supervised machine learning schema:

Y j i =H θ ( x i k )+ε i

in which

Y j i denotes the consolidated biomarker and the ground truth per sample during the model training process as a vector of multiclass labels for j classes, wherein i is a sample index;

H θ is a model mapping between eye-marker features and the one consolidated biomarker;

θ denotes a squashing function,

x i k denotes a features matrix consisting of k raw and derived eye-marker features, and

ε i is an error per sample which is minimized during the training process.

14. The method according to claim 1 , wherein the early detection screening or diagnosis process is performed on a subject that is not manifesting other clinical symptoms of neurodegeneration.

15. The method according to claim 1 , wherein the selected eye markers include variations in pupil size measured over a course of time of two minutes or longer.

16. The method according to claim 1 , wherein the selected eye markers are measured over a period of multiple days, and the step of analyzing and consolidating comprises analyzing a trend of gradual change over said period.