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AI bone lesion classifier with sensitivity-driven optimization for radiographs.

B Rinott1, C Z Dekel2, A Ilivitzki2

  • 1Rambam Health Care Campus, Medical Imaging Division, Haifa, Israel; Department of Medical Imaging Sciences, Faculty of Social Welfare and Health Sciences, Haifa University, Haifa, Israel.

Clinical Radiology
|September 28, 2025
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This summary is machine-generated.

This study developed an AI tool for detecting bone lesions in radiographs, achieving high sensitivity (96.6%) to improve early diagnosis and patient care.

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Area of Science:

  • Radiology
  • Artificial Intelligence
  • Medical Imaging

Background:

  • Early detection of primary bone lesions is crucial for effective treatment.
  • Radiographic interpretation requires specialized expertise and can be time-consuming.
  • AI offers potential to augment diagnostic capabilities in medical imaging.

Purpose of the Study:

  • To develop and evaluate a deep learning classifier for detecting primary bone lesions on radiographs.
  • To prioritize high sensitivity in the model to minimize missed diagnoses.
  • To ensure the developed AI tool is clinically usable.

Main Methods:

  • A dataset of 1,177 extremity radiographs (310 patients) was curated and labeled by radiologists.
  • The MobileNetV2 architecture was employed, trained with a sensitivity-focused approach.
  • Model performance was assessed on a hold-out test set, with attention maps generated for interpretability.

Main Results:

  • The deep learning model achieved a sensitivity of 96.6% on the test set.
  • The model demonstrated a specificity of 82.2% and an overall accuracy of 87.9%.
  • The area under the curve (AUC) was calculated to be 0.94, indicating strong discriminative ability.

Conclusions:

  • AI-based tools can be effectively deployed for radiographic detection of bone tumors.
  • Sensitivity-focused optimization of AI models is feasible and beneficial for clinical applications.
  • These AI tools hold potential to improve diagnostic accuracy, reduce delays, and support public health.