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

Artificial Intelligence (2129) mHealth for Rehabilitation (352) Decision Support for Health Professionals (1539) mHealth for Diagnosis (81) Clinical Informatics (1325)

Published on in Vol 26 (2024)

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/50631, first published .
Use of Artificial Intelligence in Cobb Angle Measurement for Scoliosis: Retrospective Reliability and Accuracy Study of a Mobile App

Use of Artificial Intelligence in Cobb Angle Measurement for Scoliosis: Retrospective Reliability and Accuracy Study of a Mobile App

Use of Artificial Intelligence in Cobb Angle Measurement for Scoliosis: Retrospective Reliability and Accuracy Study of a Mobile App

Authors of this article:

Haodong Li1 Author Orcid Image ;   Chuang Qian1 Author Orcid Image ;   Weili Yan1 Author Orcid Image ;   Dong Fu1 Author Orcid Image ;   Yiming Zheng1 Author Orcid Image ;   Zhiqiang Zhang1 Author Orcid Image ;   Junrong Meng1 Author Orcid Image ;   Dahui Wang1 Author Orcid Image
Article Authors Cited by (9) Tweetations Metrics

Journals

  1. Moon J, Cho Y. Reliability analysis of automated Cobb angle measurement using artificial intelligence models in scoliosis patients. Medical Biological Science and Engineering 2025;8(1):14 View
  2. Díaz Moreno A, Cano Alonso R, Fernández Alfonso A, Álvarez Vázquez A, Carrascoso Arranz J, López Alcolea J, García Castellanos D, Sanabria Greciano L, Recio Rodríguez M, Andreu-Vázquez C, Thuissard Vasallo I, Martínez De Vega V. Diagnostic Performance of an Artificial Intelligence Software for the Evaluation of Bone X-Ray Examinations Referred from the Emergency Department. Diagnostics 2025;15(4):491 View
  3. Bottini M, Ryu S, Terander A, Voglis S, Maldaner N, Bellut D, Regli L, Serra C, Staartjes V. The Ever-Evolving Regulatory Landscape Concerning Development and Clinical Application of Machine Intelligence: Practical Consequences for Spine Artificial Intelligence Research. Neurospine 2025;22(1):134 View
  4. Oettl F, Zsidai B, Oeding J, Hirschmann M, Feldt R, Fendrich D, Kraeutler M, Winkler P, Szaro P, Samuelsson K. Artificial intelligence‐assisted analysis of musculoskeletal imaging—A narrative review of the current state of machine learning models. Knee Surgery, Sports Traumatology, Arthroscopy 2025;33(8):3032 View
  5. Fazeli Veisari S, Bidari S, Barati K, Atlasi R, Komeili A. Wearable Devices in Scoliosis Treatment: A Scoping Review of Innovations and Challenges. Bioengineering 2025;12(7):696 View
  6. Kumar R, Dougherty C, Sporn K, Khanna A, Ravi P, Prabhakar P, Zaman N. Intelligence Architectures and Machine Learning Applications in Contemporary Spine Care. Bioengineering 2025;12(9):967 View
  7. Reyes-Barreto J, Rodríguez-Brilla M. Artificial intelligence in the context of the surgical treatment of scoliosis in adults, with an emphasis on applications, outcomes, and ethical implications: A systematic review. Revista Colombiana de Ortopedia y Traumatología 2025;39:e542 View
  8. Wu S, Miao Y, Mei J, Xiong S. The Rise of Artificial Intelligence in Orthopedics: A Bibliometric and Visualization Analysis. Journal of Multidisciplinary Healthcare 2025;Volume 18:6037 View

Conference Proceedings

  1. Bhawana K, Amritanjali , Mukherjee I. 2025 International Conference on Computing, Intelligence, and Application (CIACON). Spinal Analysis Metrics and Dataset Evaluation for Scoliosis Detection View