Journal of refractive surgery (Thorofare, N.J. : 1995) · 2025
Automated Detection of Keratorefractive Laser Surgeries on Optical Coherence Tomography Using Deep Learning.
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Assaf Jad F, Yazbeck Hady, Reinstein Dan Z, Archer Timothy J, Assaf Roland, de Ortueta Diego, Arbelaez Juan, Arbelaez Maria Clara, Awwad Shady T
Аннотация
PURPOSE: To report a deep learning neural network on anterior segment optical coherence tomography (AS-OCT) for automated detection of different keratorefractive laser surgeries-including laser in situ keratomileusis with femtosecond microkeratome (femto-LASIK), LASIK with mechanical microkeratome, photorefractive keratectomy (PRK), keratorefractive lenticule extraction (KLEx), and non-operated eyes-while also distinguishing between myopic and hyperopic treatments within these procedures.
METHODS: A total of 14,948 eye scans from 2,278 eyes of 1,166 patients were used to develop a deep learning neural network algorithm with an 80/10/10 patient distribution for training, validation, and testing phases, respectively. The algorithm was evaluated for its accuracy, F1 scores, area under precision-recall curve (AUPRC), and area under receiver operating characteristic curve (AUROC).
RESULTS: On the test dataset, the neural network was able to detect the different surgical classes with an accuracy of 96%, a weighted-average F1 score of 96%, and a macro-average F1 score of 96%. The neural network was further able to detect hyperopic and myopic subclasses within each surgical class, with an accuracy of 90%, weighted-average F1 score of 90%, and macro-average F1 score of 83%.
CONCLUSIONS: Neural networks can accurately classify a patient's keratorefractive laser history from AS-OCT scans, which may support treatment planning, intraocular lens calculations, and ectasia assessment, particularly in cases where electronic health records are incomplete. This represents a step toward transforming OCT from a diagnostic to a more comprehensive screening tool in refractive clinics. [J Refract Surg. 2025;41(3):e248-e256.].
METHODS: A total of 14,948 eye scans from 2,278 eyes of 1,166 patients were used to develop a deep learning neural network algorithm with an 80/10/10 patient distribution for training, validation, and testing phases, respectively. The algorithm was evaluated for its accuracy, F1 scores, area under precision-recall curve (AUPRC), and area under receiver operating characteristic curve (AUROC).
RESULTS: On the test dataset, the neural network was able to detect the different surgical classes with an accuracy of 96%, a weighted-average F1 score of 96%, and a macro-average F1 score of 96%. The neural network was further able to detect hyperopic and myopic subclasses within each surgical class, with an accuracy of 90%, weighted-average F1 score of 90%, and macro-average F1 score of 83%.
CONCLUSIONS: Neural networks can accurately classify a patient's keratorefractive laser history from AS-OCT scans, which may support treatment planning, intraocular lens calculations, and ectasia assessment, particularly in cases where electronic health records are incomplete. This represents a step toward transforming OCT from a diagnostic to a more comprehensive screening tool in refractive clinics. [J Refract Surg. 2025;41(3):e248-e256.].