2023/8/22 11:33:40 Wei-Hua Yang, Yi Shao, Yan-Wu Xu, Expert Workgroup of Guidelines on Clinical Research Evaluation of Artificial Intelligence in Ophthalmology (2023), Ophthalmic Imaging and Intelligent Medicine Branch of Chinese Medicine Education Association, Intelligent Medicine Committee of Chinese Medicine Education Association 12true 2023/8/22 11:33:40 Yi Shao, Ying Jie, Zu-Guo Liu, Expert Workgroup of Guidelines for the application of artificial intelligence in the diagnosis of anterior segment diseases(2023), Ophthalmic Imaging and Intelligent Medicine Branch of Chinese Medicine Education Association, Ophthalmology Committee of International Association of Translational Medicine, Chinese Ophthalmic Imaging Study Groups 11true 2023/8/22 11:33:41 Bang Chen, Xin-Wen Fang, Mao-Nian Wu, Shao-Jun Zhu, Bo Zheng, Bang-Quan Liu, Tao Wu, Xiang-Qian Hong, Jian-Tao Wang, Wei-Hua Yang 10true 2023/8/22 11:33:41 Yun-Fang Liu, Yu-Ke Ji, Fang-Qin Fei, Nai-Mei Chen, Zhen-Tao Zhu, Xing-Zhen Fei 9true 2023/8/22 11:33:41 Hua-Qing Du, Qi Dai, Zu-Hui Zhang, Chen-Chen Wang, Jing Zhai, Wei-Hua Yang, Tie-Pei Zhu 8true 2023/8/22 11:33:41 Pei-Fang Ren, Xu-Yuan Tang, Chen-Ying Yu, Li-Li Zhu, Wei-Hua Yang, Ye Shen 7true 2023/8/22 11:33:41 Dong-Qing Yuan, Fu-Nan Tang, Chun-Hua Yang, Hui Zhang, Ying Wang, Wei-Wei Zhang, Liu-Wei Gu, Qing-Huai Liu 6true 2023/8/22 11:33:42 Hai-Wen Feng, Jun-Jie Chen, Zhi-Chang Zhang, Shao-Chong Zhang, Wei-Hua Yang 5true 2023/6/27 16:00:20 Shao-Jun Zhu, Hao-Dong Zhan, Mao-Nian Wu, Bo Zheng, Bang-Quan Liu, Shao-Chong Zhang and Wei-Hua Yang 4true 0.6]. Considering only the data from the traumatic group (group A) into account, the lowest mean absolute error (MAE) and root mean square error (RMSE) were 0.30 and 0.40 logMAR, respectively. In the traumatic and healthy groups (group B), the lowest MAE and RMSE were 0.20 and 0.33 logMAR, respectively. The sensitivity was always higher than the specificity in group A, in contrast to the results in group B. The classification accuracy and precision were above 0.80 in both groups. The MAE, RMSE, and PCC of the test dataset were 0.20, 0.29, and 0.96, respectively. The sensitivity, precision, specificity, and accuracy of the test dataset were 0.83, 0.92, 0.95, and 0.90, respectively. CONCLUSION: Predicting BCVA using machine-learning models in patients with treated ocular trauma is accurate and helpful in the identification of visual dysfunction.]]> 2023/6/27 16:00:20 Zhi-Lu Zhou, Yi-Fei Yan, Jie-Min Chen, Rui-Jue Liu, Xiao-Ying Yu, Meng Wang, Hong-Xia Hao, Dong-Mei Liu, Qi Zhang, Jie Wang and Wen-Tao Xia 3true 2023/10/25 10:43:18 Fang Liu, Xing-Hui Yu, Yu-Chuan Wang, Miao Cao, Lian-Feng Xie, Jing Liu, Lin-Lin Liu 2true 2023/9/19 15:49:11 Bin Fang, Qiu-Jian Zhu, Hui Yang, Li-Cheng Fan 1true