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ORIGINAL RESEARCH article
Front. Med.
Sec. Ophthalmology
Volume 11 - 2024 |
doi: 10.3389/fmed.2024.1458356
The development of a machine learning model to train junior
Provisionally accepted- 1 Peking Union Medical College Hospital (CAMS), Beijing, China
- 2 Beijing Ming Vision Clinic, BEIJING, China
- 3 Beijing Fenglian Jiayue Lege Clinic, BEIJING, China
This study aims to evaluate the diagnostic performance of a machine learning model (ML model) to train junior ophthalmologists in detecting preclinical keratoconus (PKC).METHODS:A total of 1334 corneal topography images (The Pentacam HR system) from 413 keratoconus eyes, 32 PKC eyes and 222 normal eyes were collected. Five junior ophthalmologists were trained and annotated the images with or without the suggestions proposed by the ML model.The diagnostic performance of PKC was evaluated among three groups: junior ophthalmologist group (control group), ML model group and ML model-training junior ophthalmologist group (test group).The accuracy of the ML model between the eyes of patients with KC and NEs in all three clinics (99% accuracy, area under the receiver operating characteristic (ROC) curve AUC of 1.00, 99% sensitivity, 99% specificity) was higher than that for Belin-Ambrósio enhanced ectasia display total deviation (BAD-D) (86% accuracy, AUC of 0.97, 97% sensitivity, 69% specificity).The accuracy of the ML model between eyes with PKC and NEs in all three clinics (98% accuracy, AUC of 0.96, 98% sensitivity, 98% specificity) was higher than that of BAD-D (69% accuracy, AUC of 0.73, 67% sensitivity, 69% specificity).The diagnostic accuracy of PKC was 47.5% (95%CI, 0.5% ~ 71.6%), 100% (95%CI, 100% ~ 100%) and 94.4% (95%CI, 14.7% ~ 94.7%) in the control group, ML model group and test group. With the assistance of the proposed ML model, the diagnostic accuracy of junior ophthalmologists improved with statistical significance (P < 0.05). According to the questionnaire of all the junior ophthalmologists, the average score was 4(total 5) regarding to the comprehensiveness that the AI model has been in their keratoconus diagnosis learning; the average score was 4.4(total 5) regarding to the convenience that the AI 3 model has been in their keratoconus diagnosis learning.The proposed ML model provided a novel approach for the detection of PKC with high diagnostic accuracy and assisted to improve the performance of junior ophthalmologists, resulting especially in reducing the risk of missed diagnoses.
Keywords: machine learning, preclinical keratoconus, training, Education, Keratoconus
Received: 02 Jul 2024; Accepted: 02 Sep 2024.
Copyright: © 2024 Jiang, jiang, zhang, chen, li, zhou, Chen and li. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
* Correspondence:
hanyu jiang, Peking Union Medical College Hospital (CAMS), Beijing, China
jing zhang, Beijing Ming Vision Clinic, BEIJING, China
tao chen, Beijing Fenglian Jiayue Lege Clinic, BEIJING, China
ying li, Peking Union Medical College Hospital (CAMS), Beijing, China
yuehua zhou, Beijing Ming Vision Clinic, BEIJING, China
fusheng li, Beijing Ming Vision Clinic, BEIJING, China
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