Corneal thickness measurement by secondary speckle tracking and image processing using machine-learning algorithms

Corneal thickness (CoT) is an important tool in the evaluation process for several disorders and in the assessment of intraocular pressure. We present a method enabling high-precision measurement of CoT based on secondary speckle tracking and processing of the information by machine-learning (ML) algorithms. The proposed configuration includes capturing by fast camera the laser beam speckle patterns backscattered from the corneal-scleral border, followed by ML processing of the image. The technique was tested on a series of phantoms having different thicknesses as well as in clinical trials on human eyes. The results show high accuracy in determination of eye CoT, and implementation is speedy in comparison with other known measurement methods.