CatSkill: Artificial Intelligence-Based Metrics for the Assessment of Surgical Skill Level from Intraoperative Cataract Surgery Video Recordings.

Purpose: To develop and validate a novel artificial intelligence (AI)-powered video analysis system to assess surgeon proficiency in maintaining (1) eye neutrality, (2) eye centration, and (3) adequate focus of the operating microscope in cataract surgery and evaluate differences in these metrics between attending cataract surgeons and ophthalmology residents.

Design: A retrospective surgical video analysis.

Subjects: Six hundred twenty complete surgical video recordings of 620 cataract surgeries performed by either attending surgeons or ophthalmology residents.

Automated Abnormality Detection in Patient Retinal Function: A Deep Learning-Powered Electroretinogram Analysis System.

The electroretinogram (ERG) is an ophthalmic electrophysiology test designed to objectively measure the electrical response of the photoreceptor cells in the human retina. The analysis of the ERG is highly useful in evaluating various retinal diseases arising from both inherited and acquired disorders. However, analyzing ERG test results can be a time-intensive process for electrophysiologists, requiring meticulous examination of recorded ERG waveforms for accurate diagnosis and assessment.

A Computational Framework for Intraoperative Pupil Analysis in Cataract Surgery.

Purpose: Pupillary instability is a known risk factor for complications in cataract surgery. This study aims to develop and validate an innovative and reliable computational framework for the automated assessment of pupil morphologic changes during the various phases of cataract surgery.

Design: Retrospective surgical video analysis.

Evaluation of Methods for Detection and Semantic Segmentation of the Anterior Capsulotomy in Cataract Surgery Video.

Background: The capsulorhexis is one of the most important and challenging maneuvers in cataract surgery. Automated analysis of the anterior capsulotomy could aid surgical training through the provision of objective feedback and guidance to trainees.

Purpose: To develop and evaluate a deep learning-based system for the automated identification and semantic segmentation of the anterior capsulotomy in cataract surgery video.

Adaptive Tensor-Based Feature Extraction for Pupil Segmentation in Cataract Surgery.

Cataract surgery remains the only definitive treatment for visually significant cataracts, which are a major cause of preventable blindness worldwide. Successful performance of cataract surgery relies on stable dilation of the pupil. Automated pupil segmentation from surgical videos can assist surgeons in detecting risk factors for pupillary instability prior to the development of surgical complications.

Tensor-based Feature Extraction for Pupil Recognition in Cataract Surgery.

Cataract surgery remains the definitive treatment for cataracts, which are a major cause of preventable blindness worldwide. Adequate and stable dilation of the pupil are necessary for the successful performance of cataract surgery. Pupillary instability is a known risk factor for cataract surgery complications, and the accurate segmentation of the pupil from surgical video streams can enable the analysis of intraoperative pupil changes in cataract surgery.