Xuefei Song1,2, Zijia Liu3, Guangtao Zhai4, Huifang Zhou5,6, Lunhao Li1,2, Zhongpai Gao3, Xianqun Fan1,2. 1. Department of Ophthalmology, Ninth People's Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai, China. 2. Shanghai Key Laboratory of Orbital Diseases and Ocular Oncology, Shanghai, China. 3. School of Electronic Information and Electrical Engineering, Shanghai Jiao Tong University, Shanghai, China. 4. School of Electronic Information and Electrical Engineering, Shanghai Jiao Tong University, Shanghai, China. zhaiguangtao@sjtu.edu.cn. 5. Department of Ophthalmology, Ninth People's Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai, China. fangzzfang@163.com. 6. Shanghai Key Laboratory of Orbital Diseases and Ocular Oncology, Shanghai, China. fangzzfang@163.com.
Abstract
PURPOSE: Thyroid-associated ophthalmopathy (TAO) might lead to blindness and orbital deformity. The early diagnosis and treatment are conducive to control disease progression, but currently, there is no effective screening method. The present study aimed to introduce an artificial intelligence (AI) model for screening and testing the model with TAO patients under clinical conditions. METHODS: A total of 1435 computed tomography (CT) scans were obtained from the hospital. These CT scans were preprocessed by resampling and extracting the region of interest. CT from 193 TAO patients and 715 healthy individuals were adopted for three-dimensional (3D)-ResNet model training, and 49 TAO patients and 178 healthy people were adopted for external verification. Data from 150 TAO patients and 150 healthy people were utilized for application tests under clinical conditions, including non-inferiority experiments and diagnostic tests, respectively. RESULTS: In the external verification of the model, the area under the receiver operating characteristic (ROC) curve (AUC) was 0.919, indicating a satisfactory classification effect. The accuracy, sensitivity, and specificity were 0.87, 088, and 0.85, respectively. In non-inferiority experiments: the accuracy was 85.67% in the AI group and 84.33% in the resident group. The model passed both non-inferiority experiments (p = 0.001) and diagnostic test (the AI group sensitivity = 0.87 and specificity = 0.84%). CONCLUSIONS: A promising orbital CT-based TAO screening AI model was established and passed application tests under clinical conditions. This may provide a new TAO screening tool with further validation.
PURPOSE: Thyroid-associated ophthalmopathy (TAO) might lead to blindness and orbital deformity. The early diagnosis and treatment are conducive to control disease progression, but currently, there is no effective screening method. The present study aimed to introduce an artificial intelligence (AI) model for screening and testing the model with TAO patients under clinical conditions. METHODS: A total of 1435 computed tomography (CT) scans were obtained from the hospital. These CT scans were preprocessed by resampling and extracting the region of interest. CT from 193 TAO patients and 715 healthy individuals were adopted for three-dimensional (3D)-ResNet model training, and 49 TAO patients and 178 healthy people were adopted for external verification. Data from 150 TAO patients and 150 healthy people were utilized for application tests under clinical conditions, including non-inferiority experiments and diagnostic tests, respectively. RESULTS: In the external verification of the model, the area under the receiver operating characteristic (ROC) curve (AUC) was 0.919, indicating a satisfactory classification effect. The accuracy, sensitivity, and specificity were 0.87, 088, and 0.85, respectively. In non-inferiority experiments: the accuracy was 85.67% in the AI group and 84.33% in the resident group. The model passed both non-inferiority experiments (p = 0.001) and diagnostic test (the AI group sensitivity = 0.87 and specificity = 0.84%). CONCLUSIONS: A promising orbital CT-based TAO screening AI model was established and passed application tests under clinical conditions. This may provide a new TAO screening tool with further validation.