Predicting visual acuity with machine learning in treated ocular trauma patients

作     者：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 Wen-Tao Xia 

作者机构：Department of Forensic MedicineGuizhou Medical UniversityGuiyang 550009Guizhou ProvinceChina Shanghai Key Laboratory of Forensic MedicineShanghai Forensic Service PlatformInstitute of Forensic ScienceMinistry of JusticeShanghai 200063China The SMART(Smart Medicine and AI-based Radiology Technology)LabShanghai Institute for Advanced Communication and Data ScienceShanghai UniversityShanghai 200444China School of Communication and Information EngineeringShanghai UniversityShanghai 200444China Basic Medical CollegeJiamusi UniversityJiamusi 154007Heilongjiang ProvinceChina 

出 版 物：《International Journal of Ophthalmology(English edition)》 (国际眼科杂志（英文版）)

年 卷 期：2023年第16卷第7期

页      面：1005-1014页

核心收录：

学科分类：12[管理学] 1201[管理学-管理科学与工程(可授管理学、工学学位)] 1002[医学-临床医学] 081104[工学-模式识别与智能系统] 08[工学] 100212[医学-眼科学] 0835[工学-软件工程] 0811[工学-控制科学与工程] 0812[工学-计算机科学与技术（可授工学、理学学位）] 10[医学] 

基　　金：Supported by National Key R&D Program of China(No.2022YFC3302001) the Human Injury and Disability Degree Classification(No.SF20181312) the National Natural Science Foundation of China(No.62071285) 

主　　题：ocular trauma predicting visiual acuity best-corrected visual acuity visual dysfunction machine learning 

摘      要：AIM:To predict best-corrected visual acuity(BCVA)by machine learning in patients with ocular trauma who were treated for at least ***:The internal dataset consisted of 850 patients with 1589 eyes and an average age of *** initial visual acuity was 0.99 log *** test dataset consisted of 60 patients with 100 eyes collected while the model was *** 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 *** features were obtained from outpatient records,and ocular parameters were extracted from optical coherence tomography images and fundus *** features were put into different machine-learning models,and the obtained predicted values were compared with the actual BCVA *** best-performing model and the best variable selected were further evaluated in the test ***: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 log MAR,*** the traumatic and healthy groups(group B),the lowest MAE and RMSE were 0.20 and 0.33 log MAR,*** sensitivity was always higher than the specificity in group A,in contrast to the results in group *** classification accuracy and precision were above 0.80 in both *** MAE,RMSE,and PCC of the test dataset were 0.20,0.29,and 0.96,*** sensitivity,precision,specificity,and accuracy of the test dataset were 0.83,0.92,0.95,and 0.90,***:Predicting BCVA using machine-learning models in patients wit