H H Zou1, J H Xu2, L Zhang1, S F Ji2, Y Wang1. 1. Tianjin Medical University, Clinical College of Ophthalmology, Tianjin Eye Hospital, Tianjin Eye Institute, Tianjin Key Laboratory of Ophthalmology and Visual Science, Tianjin 300020, China. 2. School of Computer Science and Engineering, Beihang University, Beijing 100191, China.
Abstract
Objective: To investigate the diagnosis of normal cornea, subclinical keratoconus and keratoconus by artifical intelligence. Methods: Diagnostic study. From January 2016 to January 2019, who admitted to Tianjin Eye Hospital from 18 to 48 years old, with an average of (28.4±8.2) years of myopia patients in 2 018 cases. Two experienced ophthalmologists labeled keratoconus, subclinical keratconus and nomal cornea based on the topography. The data of 80% (1 615 cases) patients were randomly selected as the training set by computer random sampling method, and the data of 20% (403 cases) patients were used as the verification set. Using the Gradient Boosting Decision Tree (GBDT) algorithm to extract 28 corneal parameters, and establish an algorithm model to diagnose the corneal condition of the patient, verify the diagnostic accuracy of the model by using the 10-fold cross-validation method, and evaluate the model using the receiver operating characteristic curve. Sensitivity and specificity with the original labeling and ophthalmic resident labeling. Results: The diagnostic accuracy of the model was 95.53%. The area under the receiver operating characteristic curve (AUC) of the validation set was 0.996 6. The accuracy of the model for diagnosis of subclinical keratoconus and normal cornea was 96.67%, the AUC of the validation set was 0.993 6; the accuracy of diagnosis of keratoconus and normal cornea was 98.91%, and the AUC of the validation set was 0.998 2. The diagnostic accuracy of the model is 95.53%, which is significantly better than the resident's 93.55%. Conclusion: The model established by artifical intelligence can diagnose the subclinical keratoconus with high accuracy, which can greatly improve the clinical diagnosis efficiency and accuracy of young and primary ophthalmologists. (Chin J Ophthalmol, 2019, 55: 911-915).
Objective: To investigate the diagnosis of normal cornea, subclinical keratoconus and keratoconus by artifical intelligence. Methods: Diagnostic study. From January 2016 to January 2019, who admitted to Tianjin Eye Hospital from 18 to 48 years old, with an average of (28.4±8.2) years of myopiapatients in 2 018 cases. Two experienced ophthalmologists labeled keratoconus, subclinical keratconus and nomal cornea based on the topography. The data of 80% (1 615 cases) patients were randomly selected as the training set by computer random sampling method, and the data of 20% (403 cases) patients were used as the verification set. Using the Gradient Boosting Decision Tree (GBDT) algorithm to extract 28 corneal parameters, and establish an algorithm model to diagnose the corneal condition of the patient, verify the diagnostic accuracy of the model by using the 10-fold cross-validation method, and evaluate the model using the receiver operating characteristic curve. Sensitivity and specificity with the original labeling and ophthalmic resident labeling. Results: The diagnostic accuracy of the model was 95.53%. The area under the receiver operating characteristic curve (AUC) of the validation set was 0.996 6. The accuracy of the model for diagnosis of subclinical keratoconus and normal cornea was 96.67%, the AUC of the validation set was 0.993 6; the accuracy of diagnosis of keratoconus and normal cornea was 98.91%, and the AUC of the validation set was 0.998 2. The diagnostic accuracy of the model is 95.53%, which is significantly better than the resident's 93.55%. Conclusion: The model established by artifical intelligence can diagnose the subclinical keratoconus with high accuracy, which can greatly improve the clinical diagnosis efficiency and accuracy of young and primary ophthalmologists. (Chin J Ophthalmol, 2019, 55: 911-915).
Entities:
Keywords:
Artificial intelligence; Diagnosis, computer-assisted; Early diagnosis; Keratoconus