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Predicting visual acuity with machine learning in treated ocular trauma patients

International Journal of Ophthalmology(English edition) 2023年第7期16卷 1005-1014页

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 Department of Forensic Medicine Guizhou Medical UniversityGuiyang 550009Guizhou ProvinceChina Shanghai Key Laboratory of Forensic Medicine Shanghai Forensic Service PlatformInstitute of Forensic ScienceMinistry of JusticeShanghai 200063China The SMART(Smart Medicine and AI-based Radiology Technology)Lab Shanghai Institute for Advanced Communication and Data ScienceShanghai UniversityShanghai 200444China School of Communication and Information Engineering Shanghai UniversityShanghai 200444China Basic Medical College Jiamusi UniversityJiamusi 154007Heilongjiang ProvinceChina

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

关键词： ocular trauma predicting visiual acuity best-corrected visual acuity visual dysfunction machine learning

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