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

Journals

  1. Pisaruk A, Grygorieva N, Dubetska H, Koshel N, Shatylo V. Method for assessment of the biological age of the musculoskeletal system. Ageing & Longevity 2023;(2 2023):27 View
  2. Lis-Studniarska D, Lipnicka M, Studniarski M, Irzmański R. Applications of Artificial Intelligence Methods for the Prediction of Osteoporotic Fractures. Life 2023;13(8):1738 View
  3. Tu J, Liao W, Liu W, Gao X. Using machine learning techniques to predict the risk of osteoporosis based on nationwide chronic disease data. Scientific Reports 2024;14(1) View
  4. Sushmitha , Kanthi M, Nayak S, Thalengala A, Bhat S. Quantitative Analysis of Age-Associated Bone Mineral Density Variations via Automated Segmentation: Using CT Scans and Radon Transform to Accurately Examine and Assess the Vertebrae. IEEE Access 2024;12:48165 View
  5. Li G, Wu N, Zhang J, Song Y, Ye T, Zhang Y, Zhao D, Yu P, Wang L, Zhuang C. Proximal humeral bone density assessment and prediction analysis using machine learning techniques: An innovative approach in medical research. Heliyon 2024;10(15):e35451 View
  6. Feng L, Lu K, Li C, Xu M, Ye Y, Yin Y, Shan H. Association of apolipoprotein A1 levels with lumbar bone mineral density and β-CTX in osteoporotic fracture individuals: a cross-sectional investigation. Frontiers in Medicine 2024;11 View
  7. Xie H, Gu C, Zhang W, Zhu J, He J, Huang Z, Zhu J, Xu Z. A few-shot learning framework for the diagnosis of osteopenia and osteoporosis using knee X-ray images. Journal of International Medical Research 2024;52(9) View
  8. Gatineau G, Shevroja E, Vendrami C, Gonzalez-Rodriguez E, Leslie W, Lamy O, Hans D. Development and reporting of artificial intelligence in osteoporosis management. Journal of Bone and Mineral Research 2024;39(11):1553 View
  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
  10. Tian C, Lv G, Ye L, Zhao X, Chen M, Ye Q, Li Q, Zhao J, Zhu X, Pan X. Efficacy and Mechanism of Highly Active Umbilical Cord Mesenchymal Stem Cells in the Treatment of Osteoporosis in Rats. Current Stem Cell Research & Therapy 2025;20(1):91 View
  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
  12. Huang W, Chen I, Yu H, Chen C, Wu F, Hsu C, Wu P. A simple and user-friendly machine learning model to detect osteoporosis in health examination populations in Southern Taiwan. Bone Reports 2025;24:101826 View
  13. Opee S, Eva A, Noor A, Hasan S, Mridha M. ELW‐CNN: An extremely lightweight convolutional neural network for enhancing interoperability in colon and lung cancer identification using explainable AI. Healthcare Technology Letters 2025;12(1) View
  14. Zhang Y, Guo X, Sun K, Wang L, Huang S, Gan Y, Qin J, Liu Q, Li Y, Jin Z, Zhu L, Wei X. Exploring the classification and treatment of osteoporosis from the perspectives of natural medicines, molecular targets, and symptom clusters. Scientific Reports 2025;15(1) View
  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