Vitreoretinal Surgical Instrument Tracking in Three Dimensions Using Deep Learning.

ABSTRACT:

Purpose

To evaluate the potential for artificial intelligence-based video analysis to determine surgical instrument characteristics when moving in the three-dimensional vitreous space.

Methods

We designed and manufactured a model eye in which we recorded choreographed videos of many surgical instruments moving throughout the eye. We labeled each frame of the videos to describe the surgical tool characteristics: tool type, location, depth, and insertional laterality. We trained two different deep learning models to predict each of the tool characteristics and evaluated model performances on a subset of images.

Results

The accuracy of the classification model on the training set is 84% for the x-y region, 97% for depth, 100% for instrument type, and 100% for laterality

SUBMITTER: Baldi PF 

PROVIDER: S-EPMC9851279 | biostudies-literature | 2023 Jan

REPOSITORIES: biostudies-literature