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Updated: Sep 18, 2025

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Using the Deep Learning Algorithm to Determine the Presence of Sacroiliitis from Pelvic Radiographs.

Ming Xing Wang1, Jeoung Kun Kim2, Donghwi Park3

  • 1College of Economics and Management, Wenzhou University of Technology, Wenzhou 325000, China.

Life (Basel, Switzerland)
|June 26, 2025
PubMed
Summary

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A deep learning model effectively detects sacroiliitis from pelvic X-rays. This AI tool shows high accuracy, aiding in early diagnosis of this inflammatory condition.

Area of Science:

  • Radiology
  • Artificial Intelligence
  • Medical Imaging

Background:

  • Deep learning (DL) excels at pattern recognition in medical images.
  • DL is increasingly used for disease diagnosis and prediction in clinical settings.

Purpose of the Study:

  • To develop and validate a DL model for sacroiliitis detection using pelvic anteroposterior (AP) radiographs.
  • To assess the diagnostic performance of the DL model compared to computed tomography (CT) confirmation.

Main Methods:

  • Retrospective analysis of 1853 patients with pelvic AP radiographs (3706 sacroiliac joints).
  • DL model trained on pelvic AP radiographs, with CT-confirmed sacroiliitis as the reference standard.
  • Dataset split into 70% training and 30% validation sets.
Keywords:
computed tomographyconvolutional neural networkdeep learningradiographsacroiliac jointsacroiliitis

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Main Results:

  • The DL model achieved areas under the curve (AUC) of 0.871 for left and 0.869 for right sacroiliac joints.
  • Diagnostic accuracies were 85.4% for the left and 86.3% for the right sacroiliac joints.
  • The model demonstrated strong performance in identifying sacroiliitis.

Conclusions:

  • A DL model trained on pelvic AP radiographs can effectively aid in sacroiliitis diagnosis.
  • The model shows promising results for clinical application in diagnosing sacroiliitis.
  • CT-confirmed diagnoses were crucial for training and validating the DL model.