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

Formative Evaluation of Digital Health Interventions (2918) Artificial Intelligence (1838) New Methods (1057) Clinical Information and Decision Making (1723) Health Services Research (527) Machine Learning (1827)

Published on in Vol 7 (2023)

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/42788, first published .
Predicting the Risk of Total Hip Replacement by Using A Deep Learning Algorithm on Plain Pelvic Radiographs: Diagnostic Study

Predicting the Risk of Total Hip Replacement by Using A Deep Learning Algorithm on Plain Pelvic Radiographs: Diagnostic Study

Predicting the Risk of Total Hip Replacement by Using A Deep Learning Algorithm on Plain Pelvic Radiographs: Diagnostic Study

Authors of this article:

Chih-Chi Chen1 Author Orcid Image ;   Cheng-Ta Wu2 Author Orcid Image ;   Carl P C Chen1 Author Orcid Image ;   Chia-Ying Chung1 Author Orcid Image ;   Shann-Ching Chen3 Author Orcid Image ;   Mel S Lee4 Author Orcid Image ;   Chi-Tung Cheng5 Author Orcid Image ;   Chien-Hung Liao5 Author Orcid Image
Article Authors Cited by (4) Tweetations (2) Metrics

Journals

  1. Kenig N, Monton Echeverria J, Muntaner Vives A. Artificial Intelligence in Surgery: A Systematic Review of Use and Validation. Journal of Clinical Medicine 2024;13(23):7108 View
  2. Longo U, De Salvatore S, Piccolomini A, Ullman N, Salvatore G, D'Hooghe M, Saccomanno M, Samuelsson K, Papalia R, Pareek A. Revolutionizing total hip arthroplasty: The role of artificial intelligence and machine learning. Journal of Experimental Orthopaedics 2025;12(1) View
  3. Moon J, Jadhav P, Choi S. Deep learning analysis for rheumatologic imaging: current trends, future directions, and the role of human. Journal of Rheumatic Diseases 2025;32(2):73 View
  4. Musbahi O, Ahmed A, Hall T, Kutyla K, Sadiq M, Cobb J, Bottle A, Van Arkel R, Jones G. Deep learning classification models demonstrate high accuracy and clinical potential in radiograph interpretation in the arthroplasty clinical pathway: A systematic review and meta‐analysis. Journal of Experimental Orthopaedics 2025;12(3) View