Tong Cui1, Yan Wang2, ShuFan Ji3, Yan Li3, WeiTing Hao1, HaoHan Zou1, Vishal Jhanji4. 1. Tianjin Eye Hospital, Tianjin Eye Institute, Tianjin Key Laboratory of Ophthalmology and Visual Science, Tianjin, China; Clinical College of Ophthalmology, Tianjin Medical University, Tianjin, China. 2. Tianjin Eye Hospital, Tianjin Eye Institute, Tianjin Key Laboratory of Ophthalmology and Visual Science, Tianjin, China; Clinical College of Ophthalmology, Tianjin Medical University, Tianjin, China. Electronic address: wangyan7143@vip.sina.com. 3. School of Computer Science and Engineering, Beijing University of Aeronautics and Astronautics, Beijing, China. 4. Department of Ophthalmology, UPMC Eye Center, University of Pittsburgh School of Medicine, Pittsburgh, Pennsylvania, USA.
Abstract
PURPOSE: To analyze the outcome of machine learning technique for prediction of small incision lenticule extraction (SMILE) nomogram. DESIGN: Prospective, comparative clinical study. METHODS: A comparative study was conducted on the outcomes of SMILE surgery between surgeon group (nomogram set by surgeon) and machine learning group (nomogram predicted by machine learning model). The machine learning model was trained by 865 ideal cases (spherical equivalent [SE] within ±0.5 diopter [D] 3 months postoperatively) from an experienced surgeon. The visual outcomes of both groups were compared for safety, efficacy, predictability, and SE correction. RESULTS: There was no statistically significant difference between the baseline data in both groups. The efficacy index in the machine learning group (1.48 ± 1.08) was significantly higher than in the surgeon group (1.3 ± 0.27) (t = -2.17, P < .05). Eighty-three percent of eyes in the surgeon group and 93% of eyes in the machine learning group were within ±0.50 D, while 98% of eyes in the surgeon group and 96% of eyes in the machine learning group were within ±1.00 D. The error of SE correction was -0.09 ± 0.024 and -0.23 ± 0.021 for machine learning and surgeon groups, respectively. CONCLUSIONS: The machine learning technique performed as well as surgeon in safety, but significantly better than surgeon in efficacy. As for predictability, the machine learning technique was comparable to surgeon, although less predictable for high myopia and astigmatism.
PURPOSE: To analyze the outcome of machine learning technique for prediction of small incision lenticule extraction (SMILE) nomogram. DESIGN: Prospective, comparative clinical study. METHODS: A comparative study was conducted on the outcomes of SMILE surgery between surgeon group (nomogram set by surgeon) and machine learning group (nomogram predicted by machine learning model). The machine learning model was trained by 865 ideal cases (spherical equivalent [SE] within ±0.5 diopter [D] 3 months postoperatively) from an experienced surgeon. The visual outcomes of both groups were compared for safety, efficacy, predictability, and SE correction. RESULTS: There was no statistically significant difference between the baseline data in both groups. The efficacy index in the machine learning group (1.48 ± 1.08) was significantly higher than in the surgeon group (1.3 ± 0.27) (t = -2.17, P < .05). Eighty-three percent of eyes in the surgeon group and 93% of eyes in the machine learning group were within ±0.50 D, while 98% of eyes in the surgeon group and 96% of eyes in the machine learning group were within ±1.00 D. The error of SE correction was -0.09 ± 0.024 and -0.23 ± 0.021 for machine learning and surgeon groups, respectively. CONCLUSIONS: The machine learning technique performed as well as surgeon in safety, but significantly better than surgeon in efficacy. As for predictability, the machine learning technique was comparable to surgeon, although less predictable for high myopia and astigmatism.
Authors: Nihaal Mehta; Cecilia S Lee; Luísa S M Mendonça; Khadija Raza; Phillip X Braun; Jay S Duker; Nadia K Waheed; Aaron Y Lee Journal: JAMA Ophthalmol Date: 2020-10-01 Impact factor: 8.253