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Related Experiment Video

Updated: Apr 10, 2026

Automated Joint Space Detection Improves Bone Segmentation Accuracy
06:45

Automated Joint Space Detection Improves Bone Segmentation Accuracy

Published on: November 28, 2025

283

Multicenter deep learning for multi-abnormality screening on hip radiographs: development, external validation, and

Shenghao Xu1, Chaohui Guo2, Qibo Xu3

  • 1Department of Orthopedics, The Second Hospital of Jilin University, Changchun, Jilin, China; Joint International Research Laboratory of Ageing Active Strategy and Bionic Health in Northeast Asia of the Ministry of Education, Jilin University, Changchun, Jilin, China.

Journal of Advanced Research
|April 8, 2026
PubMed
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A new deep learning (DL) model can screen multiple hip abnormalities from pelvic radiographs, improving diagnostic accuracy and aiding orthopedic surgeons. This AI tool assists in identifying high-risk cases for faster review.

Area of Science:

  • Artificial Intelligence in Radiology
  • Deep Learning for Medical Imaging
  • Orthopedic Diagnostics

Background:

  • Hip abnormalities cause significant pain and functional impairment, with missed diagnoses being common due to clinical workload and interobserver variability.
  • Current deep learning (DL) models often focus on single pathologies, limiting their use for comprehensive hip condition screening.

Purpose of the Study:

  • To develop and validate a DL model for the simultaneous screening of multiple hip conditions.
  • Utilized a large, multicenter dataset of pelvic radiographs for model development and testing.

Main Methods:

  • A ResNet-50 based DL model was trained and validated on 25,908 hips, incorporating enhancements for improved performance.
  • The model classified eight hip conditions: normal, osteoarthritis, osteonecrosis, femoral neck fracture (FNF), intertrochanteric fracture (ITF), developmental dysplasia, total hip arthroplasty, and internal fixation.
Keywords:
Computer-aided diagnosisDeep learningHip abnormalitiesMulti-class classificationRadiography

Related Experiment Videos

Last Updated: Apr 10, 2026

Automated Joint Space Detection Improves Bone Segmentation Accuracy
06:45

Automated Joint Space Detection Improves Bone Segmentation Accuracy

Published on: November 28, 2025

283
  • External validation was performed on 4,600 hips, and the model's performance was compared against orthopedic surgeons.

    • Main Results:

    • The DL model achieved high accuracy (93.93% internal, 90.11% external) and macro-F1 scores (90.66% internal, 87.29% external).
    • Demonstrated high sensitivity for acute fractures (FNF: 95.61%, ITF: 95.31%).
    • The model outperformed residents and attendings, and model assistance improved surgeon accuracy and inter-rater agreement.

    Conclusions:

    • An externally validated DL model for automated screening of multiple common hip abnormalities has been developed.
    • The system is suitable for triage workflows, flagging high-risk cases for expedited review.
    • The DL model can serve as a supportive second reader in high-volume or resource-limited settings.