Choice of refractive surgery types for myopia assisted by machine learning based on doctors' surgical selection data

• Published in: BMC medical informatics and decision making Vol. 24; no. 1; p. 41
• Main Authors: Li, Jiajing, Dai, Yuanyuan, Mu, Zhicheng, Wang, Zhonghai, Meng, Juan, Meng, Tao, Wang, Jimin
• Format: Journal Article
• Language: English
• Published: England BioMed Central Ltd 08.02.2024; BioMed Central; BMC
• Subjects: Artificial intelligence; Back propagation networks; Care and treatment; Clinical decision making; Cornea; Data loss; Data mining; Datasets; Decision making; Decision support systems; Eye; Humans; Intraocular lenses; Learning algorithms; Machine Learning; Mathematical models; Medical science; Methods; Myopia; Myopia - surgery; Neural networks; Ophthalmologists; Ophthalmology; Patients; Practice; Quality of Life; Random forest; Refractive surgery; Refractive Surgical Procedures; SMOTE; Statistical analysis; Surgery; Technology application; Visual Acuity; China; Random forest; SMOTE; Data mining; Machine learning; Refractive surgery
• ISSN: 1472-6947; 1472-6947
• DOI: 10.1186/s12911-024-02451-0

In recent years, corneal refractive surgery has been widely used in clinics as an effective means to restore vision and improve the quality of life. When choosing myopia-refractive surgery, it is necessary to comprehensively consider the differences in equipment and technology as well as the specificity of individual patients, which heavily depend on the experience of ophthalmologists. In our study, we took advantage of machine learning to learn about the experience of ophthalmologists in decision-making and assist them in the choice of corneal refractive surgery in a new case. Our study was based on the clinical data of 7,081 patients who underwent corneal refractive surgery between 2000 and 2017 at the Department of Ophthalmology, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences. Due to the long data period, there were data losses and errors in this dataset. First, we cleaned the data and deleted the samples of key data loss. Then, patients were divided into three groups according to the type of surgery, after which we used SMOTE technology to eliminate imbalance between groups. Six statistical machine learning models, including NBM, RF, AdaBoost, XGBoost, BP neural network, and DBN were selected, and a ten-fold cross-validation and grid search were used to determine the optimal hyperparameters for better performance. When tested on the dataset, the multi-class RF model showed the best performance, with agreement with ophthalmologist decisions as high as 0.8775 and Macro F1 as high as 0.8019. Furthermore, the results of the feature importance analysis based on the SHAP technique were consistent with an ophthalmologist's practical experience. Our research will assist ophthalmologists in choosing appropriate types of refractive surgery and will have beneficial clinical effects.