Eyeworld

DECEMBER 2022 | EYEWORLD | 13 References 1. Abràmoff MD, et al. Improved automated detection of diabetic retinopathy on a publicly avail- able dataset through integration of deep learning. Invest Ophthal- mol Vis Sci. 2016;57:5200–5206. 2. Pead E, et al. Automated detection of age-related macular degeneration in color fundus photography: a systematic review. Surv Ophthalmol. 2019;64:498–511. 3. Devalla SK, et al. Glaucoma management in the era of artifi- cial intelligence. Br J Ophthalmol. 2020;104:301–311. 4. Lu Q, et al. Lens Opacities Classification System III-based artificial intelligence pro- gram for automatic cataract grading. J Cataract Refract Surg. 2022;48:528–534. Discussion In the field of ophthalmology, AI offers a poten- tial solution to address deficiencies of cataract evaluation, particularly in developing countries. This study shows that adequate performance can be achieved with relatively small training sets in grading and providing appropriate refer- ral for nuclear cataracts. However, limitations remain, especially with regard to bias, before algorithms like this can be used for research and patient care. The AI was evaluated on a monoracial sample that was very similar to that of the training set, which leads to performance overestimation. In addition, it may be helpful and posterior subcapsular-external cataracts, respectively. The authors found 99.4% of the internal photographs and 100% of the external photographs had an absolute predictive error of ≤1.0 for grading nuclear cataracts. The accu- racy of grading a nuclear cataract was found to be 93.6% and 92.7% for the external dataset, respectively. For grading cortical cataracts, the authors found 75% of the internal dataset and 93.5% of the external dataset had an absolute prediction error of ≤1.0 The accuracy of grad- ing a cortical cataract was found to be 84.4% and 80.4% for the internal and external dataset, respectively. In contrast, the grading of posterior subcapsular cataracts was found to be inconsis- tent with no clinical value. continued on page 14 University of Iowa residents and faculty; top row: Arnulfo Reyes, MD, Brandon Baksh, MD, Patrick Donegan, MD, Cheryl Wang, MD, Joanna Silverman, MD, Chad Lewis, MD, Samuel Tadros, MD, Bilal Ahmed, MD, Matthew Meyer, MD; middle row: Caroline Yu, MD, Sean Rodriguez, MD, Mahsaw Motlagh, MD, Aaron Dotson, MD; bottom row: Program Director Pavlina Kemp, MD, Tirth Shah, MD, Michael Abramoff, MD, Zachary Mortensen, MD Source: University of Iowa

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