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Related Experiment Video

Updated: Nov 28, 2025

A Method to Estimate Cadaveric Femur Cortical Strains During Fracture Testing Using Digital Image Correlation
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A Human-Algorithm Integration System for Hip Fracture Detection on Plain Radiography: System Development and

Chi-Tung Cheng1, Chih-Chi Chen2, Fu-Jen Cheng3

  • 1Department of Trauma and Emergency Surgery, Linkou Chang Gung Memorial Hospital, Chang Gung University, Taoyuan, Taiwan.

JMIR Medical Informatics
|November 27, 2020
PubMed
Summary

A new human-algorithm integration (HAI) system significantly improves hip fracture diagnosis accuracy in elderly patients. This deep learning tool enhances physician performance, proving feasible for emergency departments.

Keywords:
algorithmsartificial intelligencecomputerdeep learningdiagnosiship fracturehuman augmentationneural network

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Area of Science:

  • Radiology
  • Medical Imaging
  • Artificial Intelligence in Medicine

Background:

  • Hip fractures are a common injury in the elderly population.
  • Deep learning (DL) algorithms show promise for improving hip fracture diagnosis from plain pelvic radiographs (PXRs).
  • The clinical efficacy of DL for hip fracture detection remains to be fully determined.

Purpose of the Study:

  • To develop and validate a human-algorithm integration (HAI) system for enhanced hip fracture diagnosis.
  • To assess the system's performance in a real-world clinical setting.
  • To improve diagnostic accuracy in emergency departments.

Main Methods:

  • A deep learning algorithm was trained on trauma registry data and 3605 PXRs.
  • A 34-physician cohort evaluated diagnostic performance with and without HAI system assistance.
  • Subgroup analyses examined performance by physician specialty and experience.
  • The HAI system was deployed in emergency departments for real-world validation.

Main Results:

  • Physician diagnostic performance significantly improved with HAI assistance (sensitivity: 95% to 99%, specificity: 90% to 95%, accuracy: 90% to 96%).
  • Human-algorithm agreement increased substantially (κ: 0.69 to 0.80).
  • Both experienced and less-experienced physicians benefited, achieving performance comparable to consulting physicians.
  • In clinical deployment, the HAI system demonstrated high sensitivity (97%), specificity (95.7%), and accuracy (96.08%).

Conclusions:

  • Human-algorithm integration (HAI) is impacting healthcare delivery.
  • Integrating AI-powered diagnostic tools into emergency departments is feasible.
  • The developed HAI system effectively enhances physicians' accuracy in diagnosing hip fractures.