[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-119734-en":3,"doc-seo-119734-105":30,"detail-sidebar-cat-0-en-105":91},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":20,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},119734,687197100911,"Himbo","https://ap-avatar.wpscdn.com/avatar/a000239b6f1da00475?x-image-process=image/resize,m_fixed,w_180,h_180&k=1785132997149421697",7,"Healthcare","Predicting visual acuity with machine learning in treated ocular trauma patients","Aims at predicting best-corrected visual acuity (BCVA) using machine-learning models in patients with ocular trauma treated for at least 6 months. An internal dataset of 850 patients (1589 eyes) and a test dataset of 60 patients (100 eyes) were used. Outpatient clinical features and optical coherence tomography/fundus photograph ocular parameters were modeled. Four regression algorithms predicted BCVA and four classification algorithms assessed BCVA after treatment.","·Intelligent Ophthalmology ·  \nPredicting visual acuity with machine learning in treated ocular trauma patients  \nZhi-Lu Zhou1,2, Yi-Fei Yan3,4, Jie-Min Chen2, Rui-Jue Liu2, Xiao-Ying Yu2, Meng Wang2, Hong-Xia Hao2,5, Dong-Mei Liu2, Qi Zhang3,4, Jie Wang1, Wen-Tao Xia2  \n1Department of Forensic Medicine, Guizhou Medical University, Guiyang 550009, Guizhou Province, China 2Shanghai Key Laboratory of Forensic Medicine, Shanghai Forensic Service Platform, Institute of Forensic Science, Ministry of Justice, Shanghai 200063, China  \n3The SMART (Smart Medicine and AI-based Radiology Technology) Lab, Shanghai Institute for Advanced Communication and Data Science, Shanghai University, Shanghai 200444, China  \n4 School of Communication and Information Engineering, Shanghai University, Shanghai 200444, China  \n5Basic Medical College, Jiamusi University, Jiamusi 154007, Heilongjiang Province, China  \nCo-first authors: Zhi-Lu Zhou, Yi-Fei Yan, and Jie-Min Chen Correspondence to: Wen-Tao Xia. No.1347, Guangfu West Road, Putuo, Shanghai 200063, [China. xiawt@ssfjd.cn](China. xiawt@ssfjd.cn) ; Jie Wang. No.9, Beijing Road, Yunyan, Guiyang 50009, Guizhou Province, [China. Wj6400@gmc.edu.cn](China. Wj6400@gmc.edu.cn) ; Qi Zhang. School of Communication and Information Engineering, Shanghai University, Shanghai 200444, [China. zhangq@t.shu.edu.cn](China. zhangq@t.shu.edu.cn)[ ](China. zhangq@t.shu.edu.cn)[Received: 2022-05-05 Accepted: 2023-05-29](Received: 2022-05-05 Accepted: 2023-05-29)  \nAbstract  \n● AIM: To predict best-corrected visual acuity (BCVA) by machine learning in patients with ocular trauma who were treated for at least 6mo.  \n● METHODS: The internal dataset consisted of 850 patients with 1589 eyes and an average age of 44.29y. The initial visual acuity was 0.99 logMAR. The test dataset consisted of 60 patients with 100 eyes collected while the model was optimized. Four different machine-learning algorithms (Extreme Gradient Boosting, support vector regression, Bayesian ridge, and random forest regressor) were used to predict BCVA, and four algorithms (Extreme Gradient Boosting, support vector machine, logistic regression, and random forest classifier) were used to classify BCVA in patients with ocular trauma after treatment for 6mo or longer. Clinical features were obtained from outpatient records, and ocular parameters were extracted from optical coherence tomography images and fundus  \nphotographs. These features were put into different machine-learning models, and the obtained predicted values were compared with the actual BCVA values. The best-performing model and the best variable selected were further evaluated in the test dataset.  \n● RESULTS: There was a significant correlation between the predicted and actual values [all Pearson correlation coefficient (PCC)>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.  \n● CONCLUSION: Predicting BCVA using machine-learning models in patients with treated ocular trauma is accurate and helpful in the identification of visual dysfunction.  \n● KEYWORDS: ocular trauma; predicting visiual acuity; bestcorrected visual acuity; visual dysfunction; machine learning DOI:10.18240/ijo.2023.07.02  \nCitation: Zhou ZL, Yan YF, Chen JM, Liu RJ, Yu XY, Wang M, Hao HX, Liu DM, Zhang Q, Wang J, Xia WT. Predicting visual acuity with machine learning in treated ocular trau","cbCaigdKD8ij2DKe","https://ap.wps.com/l/cbCaigdKD8ij2DKe","pdf",3142429,1,10,"English","en",105,"# Abstract\n## AIM\n## METHODS\n## RESULTS\n## CONCLUSION\n# INTRODUCTION","[{\"question\":\"What outcome does the study aim to predict for treated ocular trauma patients?\",\"answer\":\"The study aims to predict best-corrected visual acuity (BCVA) and related visual dysfunction after treatment for at least 6 months.\"},{\"question\":\"Which data sources and features are used to build the machine-learning models?\",\"answer\":\"Clinical features are obtained from outpatient records, while ocular parameters are extracted from optical coherence tomography images and fundus photographs.\"},{\"question\":\"How are model performance and prediction accuracy evaluated?\",\"answer\":\"Predicted values are compared with actual BCVA, with correlation and error metrics reported for regression, and sensitivity/specificity and accuracy metrics reported for classification on the test dataset.\"}]","Predicting visual acuity with machine learning in treated ocular trauma patients | 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outcome does the study aim to predict for treated ocular trauma patients?","Question",{"text":75,"@type":76},"The study aims to predict best-corrected visual acuity (BCVA) and related visual dysfunction after treatment for at least 6 months.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"Which data sources and features are used to build the machine-learning models?",{"text":80,"@type":76},"Clinical features are obtained from outpatient records, while ocular parameters are extracted from optical coherence tomography images and fundus photographs.",{"name":82,"@type":73,"acceptedAnswer":83},"How are model performance and prediction accuracy evaluated?",{"text":84,"@type":76},"Predicted values are compared with actual BCVA, with correlation and error metrics reported for regression, and sensitivity/specificity and accuracy metrics reported for classification on the test 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