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Development and Validation of an Artificial Intelligence Model for Detecting Rib Fractures on Chest Radiographs.

Kaehong Lee1, Sunhee Lee1, Ji Soo Kwak1

  • 1Department of Anesthesiology and Pain Medicine, Korea University College of Medicine, Seoul 02841, Republic of Korea.

Journal of Clinical Medicine
|July 13, 2024
PubMed
Summary
This summary is machine-generated.

An artificial intelligence (AI) model was developed to detect rib fractures on chest radiographs, achieving diagnostic accuracy comparable to physicians even with limited training data.

Keywords:
Detectron2artificial intelligence (AI)chest radiographconvolutional neural network (CNN)deep learning modelradiograph classificationrib fracture AI modelrib fracture detectionrib fracture localizationrib fractures

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Area of Science:

  • Medical Imaging
  • Artificial Intelligence in Radiology
  • Machine Learning for Diagnostics

Background:

  • Chest radiography is the primary method for diagnosing rib fractures.
  • Current diagnostic accuracy for rib fractures can be variable.
  • There is a need for automated tools to improve fracture detection.

Purpose of the Study:

  • To develop an AI model for identifying rib fractures on chest radiographs.
  • To achieve precise localization of fractures using AI.
  • To match the diagnostic performance of experienced physicians with limited data.

Main Methods:

  • A retrospective study using 540 chest radiographs (270 normal, 270 with fractures).
  • Development of an AI model using Detectron2 with a faster region-based convolutional neural network (R-CNN) and feature pyramid network (FPN).
  • Comparison of AI model performance against 12 physicians (anesthesiologists and residents) via an observer performance test.

Main Results:

  • AI model achieved sensitivity of 0.87, specificity of 0.83, and AUROC of 0.89 for radiographic classification.
  • For fracture detection, the AI model showed sensitivity of 0.62, false-positive rate of 0.3, and JAFROC FOM of 0.76.
  • The AI model's performance was not statistically different from 10 or 11 out of 12 physicians.

Conclusions:

  • An AI model was successfully developed for rib fracture detection using a limited dataset.
  • The AI model demonstrates diagnostic performance comparable to experienced physicians.
  • This AI tool has the potential to aid in the radiological diagnosis of rib fractures.