Retina (Philadelphia, Pa.) · 2025
ARTIFICIAL INTELLIGENCE-ENHANCED OPTICAL COHERENCE TOMOGRAPHY ANALYSIS FOR DETECTING INTERNAL LIMITING MEMBRANE REMOVAL IN EPIRETINAL MEMBRANE SURGERY.
СтатьяПоддержка NIH: внешняя программаПоддержка исследования: вне правительства СШАНазвание и аннотация: Перевод пока не подготовлен
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Mehta Nehal N, Le An D, Nagel Ines D, Agnihotri Akshay, Heinke Anna, Cheng Lingyun, Bartsch Dirk-Uwe, Tran Melanie, Truong Nguyen, Cheolhong An, Freeman William R
Аннотация
PURPOSE: This study evaluates how accurately humans and artificial intelligence can identify the type of surgery performed for epiretinal membrane (ERM) removal by analyzing postoperative optical coherence tomography (OCT) scans.
METHODS: A retrospective analysis at the University of California San Diego included 250 eyes from 239 patients who underwent vitrectomy for idiopathic ERM between January 2013 and October 2024. Eyes were categorized into two groups: one with both the internal limiting membrane and ERM removed using indocyanine green staining, and another with only ERM removal, guided by triamcinolone. Postoperative OCT scans were labeled as either ERM-only or internal limiting membrane + ERM peel based on surgical notes. Both the human grader and artificial intelligence model were trained on 200 labeled OCT scans and tested on 50 masked OCT scans to classify the surgery type.
RESULTS: Accuracy of the human grader in identifying the surgical technique was 50%, while the artificial intelligence models demonstrated significantly higher accuracy. The ResNet18 model achieved 61 ± 3%, while UwU-OrthLatt with DB4 initialization and UwU-PR-Relax with Symlet4 initialization reached 70 ± 5% and 69 ± 3%, respectively.
CONCLUSION: Artificial intelligence outperformed human grading in detecting internal limiting membrane removal from OCT scans, demonstrating artificial intelligence's potential in improving ophthalmic imaging for clinical use.
METHODS: A retrospective analysis at the University of California San Diego included 250 eyes from 239 patients who underwent vitrectomy for idiopathic ERM between January 2013 and October 2024. Eyes were categorized into two groups: one with both the internal limiting membrane and ERM removed using indocyanine green staining, and another with only ERM removal, guided by triamcinolone. Postoperative OCT scans were labeled as either ERM-only or internal limiting membrane + ERM peel based on surgical notes. Both the human grader and artificial intelligence model were trained on 200 labeled OCT scans and tested on 50 masked OCT scans to classify the surgery type.
RESULTS: Accuracy of the human grader in identifying the surgical technique was 50%, while the artificial intelligence models demonstrated significantly higher accuracy. The ResNet18 model achieved 61 ± 3%, while UwU-OrthLatt with DB4 initialization and UwU-PR-Relax with Symlet4 initialization reached 70 ± 5% and 69 ± 3%, respectively.
CONCLUSION: Artificial intelligence outperformed human grading in detecting internal limiting membrane removal from OCT scans, demonstrating artificial intelligence's potential in improving ophthalmic imaging for clinical use.