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Patch-Wise Approach with Vision Transformer for Detecting Implant Failure in Spinal Radiography.

Keum San Chun1, Sungwon Lee2,3, Hyeondeok Choi1,4

  • 1Visual Analysis and Learning for Improved Diagnostics (VALID) Lab, College of Medicine, The Catholic University of Korea, 222 Banpo-daero, Seocho-gu, Seoul, 06591, Republic of Korea.

Journal of Imaging Informatics in Medicine
|November 3, 2025
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Summary
This summary is machine-generated.

A new deep learning model using vision transformers can detect spinal implant fractures on radiographs with high sensitivity. This AI tool significantly improves radiologist performance, aiding in early detection and surveillance of spinal implant failures.

Keywords:
Artificial intelligence in spinal radiologyDeep learning in MSK radiologyImplant breakageSpinal hardware fractureSpinal implant failure

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Area of Science:

  • Orthopedics
  • Radiology
  • Artificial Intelligence

Background:

  • Spinal implant failures are increasing due to more surgeries and an aging population.
  • Early detection of spinal implant fractures on radiographs is challenging due to subtle signs and limited radiologist availability.
  • Current diagnostic methods for spinal implant integrity often miss early signs of failure.

Purpose of the Study:

  • To develop and validate a vision transformer-based deep learning model for detecting spinal implant fractures.
  • To assess the impact of this AI tool on radiologist performance in identifying spinal implant failures.
  • To evaluate the model's utility as a triage tool for spinal implant surveillance.

Main Methods:

  • A DINOv2-based vision transformer model was trained on 9924 spinal radiographs to detect implants and analyze 224x224 pixel patches for fractures.
  • The model was tested on 1538 images, and its performance was compared to three radiologists reviewing with and without AI assistance.
  • Radiologist performance was measured using accuracy, F1 score, precision, recall, and generalized estimating equation (GEE) analysis.

Main Results:

  • The AI model achieved a recall of 0.94, precision of 0.37, F1 score of 0.54, and accuracy of 0.83.
  • Radiologist recall improved from 0.70 to 0.95 with AI assistance, with the most significant improvement in the least experienced reader (0.49 to 0.92).
  • GEE analysis showed a significant diagnostic improvement (OR = 2.82, p < 0.001), especially for rod fractures.

Conclusions:

  • The patch-wise transformer-based approach demonstrates high sensitivity for detecting spinal implant fractures.
  • AI assistance significantly enhances radiologist diagnostic performance, particularly for less experienced readers.
  • This AI model shows promise as an effective triage tool for spinal implant surveillance, improving early detection rates.