This paper is in the following e-collection/theme issue:

Artificial Intelligence (1779) Machine Learning (1770) Decision Support for Health Professionals (1361) Clinical Informatics (1176) Clinical Information and Decision Making (1688)

Published on in Vol 25 (2023)

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/40179, first published .
Interpretable Deep-Learning Approaches for Osteoporosis Risk Screening and Individualized Feature Analysis Using Large Population-Based Data: Model Development and Performance Evaluation

Interpretable Deep-Learning Approaches for Osteoporosis Risk Screening and Individualized Feature Analysis Using Large Population-Based Data: Model Development and Performance Evaluation

Interpretable Deep-Learning Approaches for Osteoporosis Risk Screening and Individualized Feature Analysis Using Large Population-Based Data: Model Development and Performance Evaluation

Authors of this article:

Bogyeong Suh1 Author Orcid Image ;   Heejin Yu1 Author Orcid Image ;   Hyeyeon Kim2 Author Orcid Image ;   Sanghwa Lee2 Author Orcid Image ;   Sunghye Kong3 Author Orcid Image ;   Jin-Woo Kim4 Author Orcid Image ;   Jongeun Choi1 Author Orcid Image
Article Authors Cited by (24) Tweetations (3) Metrics

Journals

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  9. Suh B, Yu H, Cha J, Choi J, Kim J. Explainable Deep Learning Approaches for Risk Screening of Periodontitis. Journal of Dental Research 2025;104(1):45 View
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  11. Scarpato N, Ferroni P, Guadagni F. XAI Unveiled: Revealing the Potential of Explainable AI in Medicine - A Systematic Review. IEEE Access 2024:1 View
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  15. Jin W, Xu L, Yue C, Hu L, Wang Y, Fu Y, Guo Y, Bai F, Yang Y, Zhao X, Luo Y, Wu X, Sheng Z. Development and validation of explainable machine learning models for female hip osteoporosis using electronic health records. International Journal of Medical Informatics 2025;199:105889 View
  16. Alshraideh M, Al-Dhaqm A, Alshammari A, Alshraideh A, Alshraideh B, Mohamed Al-Fayoumi B, Nasser M. Advanced Prediction of Recurrent Fragility Fractures Using Large Language Models. IEEE Access 2025;13:71503 View
  17. Yasin P, Ding L, Mamat M, Guo W, Song X. Machine Learning-Based Interpretable Screening for Osteoporosis in Tuberculosis Spondylitis Patients Using Blood Test Data: Development and External Validation of a Novel Web-Based Risk Calculator with Explainable Artificial Intelligence (XAI). Infection and Drug Resistance 2025;Volume 18:2797 View

Books/Policy Documents

  1. Ponni S, Sabarivani A, Janney J. Proceedings of International Conference on Intelligent Vision and Computing (ICIVC 2023). View

Conference Proceedings

  1. Neeta T, Poonia R. 2023 IEEE International Conference on Contemporary Computing and Communications (InC4). Pertaining analysis of fracture risk in Osteoporotic patients using Machine Learning Techniques View
  2. Shetty S, Shetty S, Masurekar A, Sarbhukan V, Pawar R. 2023 14th International Conference on Computing Communication and Networking Technologies (ICCCNT). Neural Network-Based Model for Osteoporosis Prediction: A Novel Approach for Enhanced Accuracy View
  3. Kaur S, Singh J. 2024 International Conference on Communication, Computer Sciences and Engineering (IC3SE). Femur Osteoporosis Risk Assessment through Deep Learning Models View
  4. Bhoite A, Patil V, Bhinge S, Babu P, Bharat P, Agrawal R. 2024 International Conference on Healthcare Innovations, Software and Engineering Technologies (HISET). Efficiency of Machine Learning (ML) Models and Medical Imaging in Disease Detection View
  5. Apetorgbor M, Dulewale N, Dhawale C. 2024 Parul International Conference on Engineering and Technology (PICET). Smart Healthcare Solutions: Leveraging Internet of Things Technologies for Advanced Computing, Communications and Applied Informatics View
  6. Sultana R, Zaman M, Ivković N, Cengiz K, Rafaqat I, Akhunzada A. 2024 International Conference on Computer and Applications (ICCA). Efficient Detection of Knee Osteoporosis Using the Swin Transformer on X-Ray Images View