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

Artificial Intelligence (1408) Machine Learning (1489) Decision Support for Health Professionals (1201) Clinical Informatics (1042) Clinical Information and Decision Making (1424)

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 (15) Tweetations (3) Metrics

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 2024 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