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Vitreoretinal Surgical Instrument Tracking in Three Dimensions Using Deep Learning.

Authors :
Baldi PF
Abdelkarim S
Liu J
To JK
Ibarra MD
Browne AW
Source :
Translational vision science & technology [Transl Vis Sci Technol] 2023 Jan 03; Vol. 12 (1), pp. 20.
Publication Year :
2023

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.<br />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.<br />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 of insertion. The accuracy of the classification model on the validation dataset is 83% for the x-y region, 96% for depth, 100% for instrument type, and 100% for laterality of insertion. The close-up detection model performs at 67 frames per second, with precision for most instruments higher than 75%, achieving a mean average precision of 79.3%.<br />Conclusions: We demonstrated that trained models can track surgical instrument movement in three-dimensional space and determine instrument depth, tip location, instrument insertional laterality, and instrument type. Model performance is nearly instantaneous and justifies further investigation into application to real-world surgical videos.<br />Translational Relevance: Deep learning offers the potential for software-based safety feedback mechanisms during surgery or the ability to extract metrics of surgical technique that can direct research to optimize surgical outcomes.

Details

Language :
English
ISSN :
2164-2591
Volume :
12
Issue :
1
Database :
MEDLINE
Journal :
Translational vision science & technology
Publication Type :
Academic Journal
Accession number :
36648414
Full Text :
https://doi.org/10.1167/tvst.12.1.20