Remote photonic sensing of biomedical parameters based on machine learning methods

 Corneal Thickness (CoT) is an important tool in the evaluation process of several eye disorders and assessment of Intra Ocular Pressure (IOP). We present a novel method enabling high-precision measurement of CoT based on secondary speckle patterns (SSP) and processing of the information by machine learning (ML) algorithms. 

The proposed configuration includes capturing images by fast camera, back scattered laser beam on the eye-cornea which in turns creates speckle patterns we have analyzed by ML based classification and regression networks created specifically for this task.

The technique was tested on series of phantoms having different thickness as well as on human eyes.

The results show high precision of cornea thickness measurement and it is fast to implement in comparison to other known measurement methods.

* M.Sc. research supervised by Prof. Zeev Zalevsky.