Automatic quantifying and monitoring follow-ups for implantable collamer lens implantation using AS-OCT images

Abstract

Purpose: To develop a deep learning method to automatically monitor the implantable collamer lens (ICL) position and quantify subtle alterations in the anterior chamber using anterior segment optical coherence tomography (AS-OCT) images for high myopia patients with ICL implantation.Methods: In this study, 798 AS-OCT images of 203 patients undergoing ICL implantation at our eye center from April 2017 to June 2021 were involved. A deep learning system was developed to first isolate the corneoscleral, ICL, and lens, and then quantify clinical important parameters in AS-OCT images (central corneal thickness, anterior chamber depth, and lens vault).Results: The deep learning system was able to accurately isolate the corneoscleral, ICL, and lens with the Dice coefficient ranging from 0.911 to 0.960, and all the F1 scores >0.900. The relative error between automated measurements and the ground truth for 95% (188 images out of 198) of LVs was within 10%. Intraclass correlation coefficients (ICCs) of the machine-ground truth measurements ranged from 0.928 to 0.995. The deep learning method also showed better repeatability than human graders.Conclusion: The deep learning method provides reliable detection and quantification of AS-OCT scans for postoperative ICL implantation, which can simplify and optimize the management of clinical outcomes of ICL implantations. Also, this is a step towards an objective measurement of the postoperative vault, making the data more comparable and repeatable to each other.