Artificial intelligence system for assessment of children carpal bone age
10.13929/j.1003-3289.201907065
- Author:
Min KANG
1
Author Information
1. Department of Radiology, Sichuan Province Hospital for Women and Children
- Publication Type:Journal Article
- Keywords:
Age determination by skeleton;
Carpal bones;
Child;
Deep learning;
Tomography, X-ray computed
- From:
Chinese Journal of Medical Imaging Technology
2019;35(12):1804-1807
- CountryChina
- Language:Chinese
-
Abstract:
Objective: To observe the clinical feasibility of carpal bone age assessment (BAA) using artificial intelligence (AI) system. Methods: Totally 130 hand-wrist radiographs of children aged 1-13 years were retrospectively studied. Carpal bone ages estimated by three senior radiologists were taken as reference standards. The root mean square error (RMSE) and mean absolute error (MAE) of carpal bone age estimations and carpal maturity scores relative to the reference standard were calculated and compared between AI system (model) and 3 junior radiologists (physician 1, 2, 3), respectively. The intraclass correlation coefficient (ICC) was used to test the agreement of BAA among the model, physicians and reference standards. BAA time was also compared between model and physicians, respectively. Results: There were significant differences of carpal BAA's MAE and RMSE of model and physician 1, 2 (all P<0.001), but not between model and physician 3 (all P>0.05). There were significant differences of carpal maturity score's MAE and RMSE between model and physicians 1 (both P<0.05), while no significant difference was found between model and physician 2, 3 (all P>0.05). ICC between BAA of AI and reference standards was 0.997, between physician 1, 2, 3 and reference standards was 0.994, 0.996 and 0.997, respectively. BAA time of AI was significantly shorter than that of three physicians (all P<0.05). Conclusion: Using AI BAA system can fast estimate carpal bone age with high accuracy.
