Eyeworld

12 | EYEWORLD | DECEMBER 2022 EYEWORLD JOURNAL CLUB ASCRS NEWS by Tirth J. Shah, MD,* Zachary Q. Mortensen, MD,* Michael D. Abramoff, MD, and Thomas A. Oetting, MD *Co-first authors posterior subcapsular cataracts. Eyes with small pupils, blurred region of interest, or any corneal disease that interfered with lens observation were excluded. The images were graded based on LOCSIII. Nuclear cataracts were graded on a scale from 1.0 to 6.0 with respect to nuclear col- or. Cortical and posterior subcapsular cataracts were graded on a scale from 1.0 to 5.0. The images were graded by at least two experienced ophthalmologists, and the reference standard was the average of the two experts. A grade of 3.0 or greater was regarded as "moderate to severe" requiring referral. Advanced deep learn- ing algorithms, including Faster R-CNN and ResNet, were applied to identify the capture modes, annotate regions of interest, and grade the cataracts. Interobserver repeatability among the various ophthalmologists was assessed with intraclass correlation coefficients (ICC) using a mixed-effects model. Results A total of 847 slit beam, 326 diffuse illuminat- ed, and 155 retroilluminated photographs were taken from the internal dataset, and 192 slit beam, 92 diffuse illuminated, and 71 retroil- luminated photographs were taken from the external dataset. Interobserver reproducibility of cataract evaluations yielded ICC values of 0.958, 0.718, 0.787, 0.733, 0.835, and 0.780 for nucle- ar-internal, nuclear-external, cortical-internal, cortical-external, posterior subcapsular-internal, Review of "LOCSIII-based artificial intelligence program for automatic cataract grading" T he rapid rise of artificial intelligence (AI) has revolutionized the field of medicine. As an image-centric spe- cialty, ophthalmology stands at the frontier of AI applications. The imple- mentation of AI has already provided large-scale early screening and detection of eye diseases, improved health disparities through better access, and decreased cost, in both the U.S. and low-income countries. Comparatively, the development of AI for evaluation of the lens is still in its infancy. 1,2,3 Lu et al. set out to establish and validate an AI-assisted automatic cataract grading program based on the Lens Opacities Classification System III (LOCSIII). 4 Methods The authors prospectively analyzed an internal dataset of slit lamp photographs of the ante- rior segment of cataract-affected eyes taken between 2018 and 2020 at the Fudan Univer- sity Department of Ophthalmology. The group also obtained an external dataset of slit lamp photographs from March 2018 to August 2019 from the Pujiang Eye Study in Shanghai. All photographs were taken under pharmacologic mydriasis. The internal dataset was used to train, validate, and test, while the external data- set was used for testing. Each patient received slit beam imaging for nuclear cataract, diffuse illumination imaging for cortical cataracts, and retroillumination for Thomas A. Oetting, MD Associate Residency Program Director Department of Ophthalmology and Visual Sciences University of Iowa Iowa City, Iowa Zachary Q. Mortensen, MD Ophthalmology Resident University of Iowa Iowa City, Iowa Tirth J. Shah, MD Ophthalmology Resident University of Iowa Iowa City, Iowa The implementation of AI has already provided large-scale early screening and detection of eye disease, improved health disparities through better access, and decreased cost, in both the U.S. and low-income countries. Comparatively, the development of AI for evaluation of the lens is still in its infancy.

• Blank Page
• Blank Page (1)