Background: Osteoporosis is one of the diseases that requires early screening and detection for its management. Common clinical tools and machine-learning (ML) models for screening osteoporosis have been developed, but they show limitations such as low accuracy. Moreover, these methods are confined to limited risk factors and lack individualized explanation. Objective: The aim of this study was to develop an interpretable deep-learning (DL) model for osteoporosis risk screening with clinical features. Clinical interpretation with individual explanations of feature contributions is provided using an explainable artificial intelligence (XAI) technique. Methods: We used two separate data sets: the National Health and Nutrition Examination Survey data sets from the United States (NHANES) and South Korea (KNHANES) with 8274 and 8680 respondents, respectively. The study population was classified according to the T-score of bone mineral density at the femoral neck or total femur. A DL model for osteoporosis diagnosis was trained on the data sets and significant risk factors were investigated with local interpretable model-agnostic explanations (LIME). The performance of the DL model was compared with that of ML models and conventional clinical tools. Additionally, contribution ranking of risk factors and individualized explanation of feature contribution were examined. Results: Our DL model showed area under the curve (AUC) values of 0.851 (95% CI 0.844-0.858) and 0.922 (95% CI 0.916-0.928) for the femoral neck and total femur bone mineral density, respectively, using the NHANES data set. The corresponding AUC values for the KNHANES data set were 0.827 (95% CI 0.821-0.833) and 0.912 (95% CI 0.898-0.927), respectively. Through the LIME method, significant features were induced, and each feature’s integrated contribution and interpretation for individual risk were determined. Conclusions: The developed DL model significantly outperforms conventional ML models and clinical tools. Our XAI model produces high-ranked features along with the integrated contributions of each feature, which facilitates the interpretation of individual risk. In summary, our interpretable model for osteoporosis risk screening outperformed state-of-the-art methods.
|Journal||Journal of medical Internet research|
|Publication status||Published - 2023|
Bibliographical noteFunding Information:
This work was supported by a National Research Foundation of Korea grant funded by the Korean government (MSIT) (2020R1A2C4001842) and the Korea Institute of Science and Technology intramural grants (2E31570). This work was also supported by the Institute of Information & Communications Technology Planning & Evaluation grant funded by the Korean government (MSIT) (RS-2022-00155966, Artificial Intelligence Convergence Innovation Human Resources Development; Ewha Womans University). This article includes a portion of the materials and results from HK’s PhD thesis at Ewha Womans University.
©Bogyeong Suh, Heejin Yu, Hyeyeon Kim, Sanghwa Lee, Sunghye Kong, Jin-Woo Kim, Jongeun Choi.
All Science Journal Classification (ASJC) codes
- Health Informatics