Automatic segmentation of intraocular lens, the retrolental space and Berger's space using deep learning.

Purpose: To develop and validate a deep learning model to automatically segment three structures using an anterior segment optical coherence tomography (AS‐OCT): The intraocular lens (IOL), the retrolental space (IOL to the posterior lens capsule) and Berger's space (BS; posterior capsule to the anterior hyaloid membrane). Methods: An artificial intelligence (AI) approach based on a deep learning model to automatically segment the IOL, the retrolental space, and BS in AS‐OCT, was trained using annotations from an experienced clinician. The training, validation and test set consisted of 92 cross‐sectional OCT slices, acquired in 47 visits from 41 eyes. Annotations from a second experienced clinician in the test set were additionally evaluated to conduct an inter‐reader variability analysis. Results: The AI model achieved a Precision/Recall/Dice score of 0.97/0.90/0.93 for IOL, 0.54/0.65/0.55 for retrolental space, and 0.72/0.58/0.59 for BS. For inter‐reader variability, Precision/Recall/Dice values were 0.98/0.98/0.98 for IOL, 0.74/0.59/0.62 for retrolental space, and 0.58/0.57/0.57 for BS. No statistical differences were observed between the automated algorithm and the inter‐reader variability for BS segmentation. Conclusion: The deep learning model allows for fully automatic segmentation of all investigated structures, achieving human‐level performance in BS segmentation. We, therefore, expect promising applications of the algorithm with particular interest in BS in automated big data analysis and real‐time intra‐operative support in ophthalmology, particularly in conjunction with primary posterior capsulotomy in femtosecond laser‐assisted cataract surgery. [ABSTRACT FROM AUTHOR]