Eyeworld

14 | EYEWORLD | DECEMBER 2022 Contact Mortensen: zachary-mortensen@uiowa.edu Oetting: thomas-oetting@uiowa.edu Shah: tirth-shah@uiowa.edu ASCRS NEWS to know how the images were captured and whether this can be generalizable to another setting, where there may be more variability in ophthalmic examination instruments and slit lamp digital cameras. While LOCSIII is effective for standardizing and grading cataracts, it may be challenging to apply in the clinical setting because indications for surgical intervention rely largely on patient visual function. However, such a system of auto- mated cataract grading may have potential for utility in research, such as monitoring cataract progression in large scale drug trials. Overall, we commend the authors on their impressive work. We hope their work adds to the growing AI literature and contributes to furthering research and addressing healthcare disparities. continued from page 13 Lens Opacities Classification System III-based artificial intelligence program for automatic cataract grading Lu Q, et al. J Cataract Refract Surg. 2022;48:528–534 n Purpose: To establish and validate an artificial intelligence (AI)-assisted automatic cataract grading program based on the Lens Opacities Classification System III (LOCSIII). n Setting: Eye and Ear, Nose, and Throat (EENT) Hospital, Fudan University, Shanghai, China. n Design: AI training n Methods: Advanced deep-learning algorithms, including Faster R-CNN and ResNet, were applied to the localization and analysis of the region of interest. An internal dataset from the EENT Hospital of Fudan University and an external dataset from the Pujiang Eye Study were used for AI training, validation, and testing. The datasets were automatically labeled on the AI platform in terms of the capture mode and cataract grading based on the LOCSIII system. n Results: The AI program showed reliable capture mode recognition, grading, and referral capability for nuclear and cortical cataract grading. In the internal and external datasets, 99.4% and 100% of automatic nuclear grading, respectively, had an absolute prediction error of ≤1.0, with a satisfactory referral capability (area under the curve [AUC]: 0.983 for the internal dataset; 0.977 for the external dataset). 75.0% (internal dataset) and 93.5% (external dataset) of the automatic cortical grades had an absolute prediction error of ≤1.0, with AUCs of 0.855 and 0.795 for referral, respectively. Good consistency was observed between automatic and manual grading when both nuclear and cortical cataracts were evaluated. However, automatic grading of posterior subcapsular cataracts was impractical. n Conclusions: The AI program proposed in this study shows robust grading and diagnostic performance for both nuclear and cortical cataracts, based on LOCSIII. While LOCSIII is effective for standardizing and grading cataracts, it may be challenging to apply in the clinical setting because indications for surgical intervention rely largely on patient visual function.

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