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

Machine Learning (1501) Artificial Intelligence (1421) Imaging Informatics (200)

Published on in Vol 8, No 11 (2020): November

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/19416, first published .
A Human-Algorithm Integration System for Hip Fracture Detection on Plain Radiography: System Development and Validation Study

A Human-Algorithm Integration System for Hip Fracture Detection on Plain Radiography: System Development and Validation Study

A Human-Algorithm Integration System for Hip Fracture Detection on Plain Radiography: System Development and Validation Study

Authors of this article:

Chi-Tung Cheng1 Author Orcid Image ;   Chih-Chi Chen2 Author Orcid Image ;   Fu-Jen Cheng3 Author Orcid Image ;   Huan-Wu Chen4 Author Orcid Image ;   Yi-Siang Su1 Author Orcid Image ;   Chun-Nan Yeh5 Author Orcid Image ;   I-Fang Chung6, 7, 8 Author Orcid Image ;   Chien-Hung Liao1 Author Orcid Image
Article Authors Cited by (20) Tweetations (2) Metrics

Journals

  1. Smets J, Shevroja E, Hügle T, Leslie W, Hans D. Machine Learning Solutions for Osteoporosis—A Review. Journal of Bone and Mineral Research 2020;36(5):833 View
  2. Sato Y, Takegami Y, Asamoto T, Ono Y, Hidetoshi T, Goto R, Kitamura A, Honda S. Artificial intelligence improves the accuracy of residents in the diagnosis of hip fractures: a multicenter study. BMC Musculoskeletal Disorders 2021;22(1) View
  3. Ukai K, Rahman R, Yagi N, Hayashi K, Maruo A, Muratsu H, Kobashi S. Detecting pelvic fracture on 3D-CT using deep convolutional neural networks with multi-orientated slab images. Scientific Reports 2021;11(1) View
  4. Kuo R, Harrison C, Curran T, Jones B, Freethy A, Cussons D, Stewart M, Collins G, Furniss D. Artificial Intelligence in Fracture Detection: A Systematic Review and Meta-Analysis. Radiology 2022;304(1):50 View
  5. Cohen J, McInnes M. Deep Learning Algorithms to Detect Fractures: Systematic Review Shows Promising Results but Many Limitations. Radiology 2022;304(1):63 View
  6. Cha Y, Kim J, Park C, Kim J, Lee S, Yoo J. Artificial intelligence and machine learning on diagnosis and classification of hip fracture: systematic review. Journal of Orthopaedic Surgery and Research 2022;17(1) View
  7. Zhang X, Yang Y, Shen Y, Zhang K, Jiang Z, Ma L, Ding C, Wang B, Meng Y, Liu H. Diagnostic accuracy and potential covariates of artificial intelligence for diagnosing orthopedic fractures: a systematic literature review and meta-analysis. European Radiology 2022;32(10):7196 View
  8. Cheng C, Hsu C, Ooyang C, Chou C, Lin N, Lin J, Ku Y, Lin H, Kao S, Chen H, Wu Y, Liao C. Evaluation of ensemble strategy on the development of multiple view ankle fracture detection algorithm. The British Journal of Radiology 2023;96(1145) View
  9. Miller A, Vattigunta S, Sharma S, Ahmed A, Jenkins S, Thakkar S, Srikumaran U. Does Additional Review of Initial Radiographs by Musculoskeletal Specialists Limit Additional Advanced Imaging in Suspected Hip Fractures?. The Journal of Hip Surgery 2023;07(01):030 View
  10. Ye P, Li S, Wang Z, Tian S, Luo Y, Wu Z, Zhuang Y, Zhang Y, Grzegorzek M, Hou Z. Development and validation of a deep learning-based model to distinguish acetabular fractures on pelvic anteroposterior radiographs. Frontiers in Physiology 2023;14 View
  11. Tang Y, Ranschaert E, Verstraete K. Performantie van AI bij fractuurdetectie en effect op de prestaties van artsen: een systematische review. Tijdschrift voor Geneeskunde 2023 View
  12. Jung J, Dai J, Liu B, Wu Q, Frasch M. Artificial intelligence in fracture detection with different image modalities and data types: A systematic review and meta-analysis. PLOS Digital Health 2024;3(1):e0000438 View
  13. Cheng C, Ooyang C, Kang S, Liao C. Applications of Deep Learning in Trauma Radiology: A Narrative Review. Biomedical Journal 2024:100743 View
  14. Bachmann R, Gunes G, Hangaard S, Nexmann A, Lisouski P, Boesen M, Lundemann M, Baginski S. Improving traumatic fracture detection on radiographs with artificial intelligence support: a multi-reader study. BJR|Open 2023;6(1) View
  15. Nowroozi A, Salehi M, Shobeiri P, Agahi S, Momtazmanesh S, Kaviani P, Kalra M. Artificial intelligence diagnostic accuracy in fracture detection from plain radiographs and comparing it with clinicians: a systematic review and meta-analysis. Clinical Radiology 2024;79(8):579 View
  16. Dasegowda G, Sato J, Elton D, Garza-Frias E, Schultz T, Bridge C, Bizzo B, Kalra M, Dreyer K. No code machine learning: validating the approach on use-case for classifying clavicle fractures. Clinical Imaging 2024;112:110207 View
  17. Akbarian E, Mohammadi M, Tiala E, Ljungberg O, Sharif Razavian A, Magnéli M, Gordon M. Development and validation of an artificial intelligence model for the classification of hip fractures using the AO-OTA framework. Acta Orthopaedica 2024;95:340 View
  18. Ruitenbeek H, Oei E, Visser J, Kijowski R. Artificial intelligence in musculoskeletal imaging: realistic clinical applications in the next decade. Skeletal Radiology 2024;53(9):1849 View
  19. Lo Mastro A, Grassi E, Berritto D, Russo A, Reginelli A, Guerra E, Grassi F, Boccia F. Artificial intelligence in fracture detection on radiographs: a literature review. Japanese Journal of Radiology 2024 View

Books/Policy Documents

  1. Nguyen H, Nghiem T, Dang N. Advances in Information and Communication Technology. View