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Related Experiment Video

Updated: Aug 6, 2025

Machine Learning Algorithms for Early Detection of Bone Metastases in an Experimental Rat Model
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Improving detection of impacted animal bones on lateral neck radiograph using a deep learning artificial intelligence

Yueh-Sheng Chen1, Sheng-Dean Luo2, Chi-Hsun Lee3

  • 1Department of Diagnostic Radiology, Kaohsiung Chang Gung Memorial Hospital, Chang Gung University College of Medicine, 123 Ta-Pei Road, Niao-Sung, Kaohsiung, 83305, Taiwan.

Insights Into Imaging
|March 17, 2023
PubMed
Summary
This summary is machine-generated.

A new deep learning artificial intelligence (AI) algorithm effectively detects impacted animal bones on lateral neck radiographs. This AI tool enhances diagnostic accuracy and workflow efficiency for physicians interpreting these images.

Keywords:
Animal bone impactionArtificial intelligenceLateral neck radiographRetrospective studies

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Area of Science:

  • Radiology
  • Artificial Intelligence
  • Medical Imaging

Background:

  • Animal bone impaction in the neck requires accurate detection on lateral radiographs.
  • Interpretation of these radiographs can be challenging, potentially leading to delayed diagnosis.

Purpose of the Study:

  • To develop a deep learning artificial intelligence (AI) algorithm for detecting impacted animal bones on lateral neck radiographs.
  • To evaluate the AI algorithm's effectiveness in improving radiograph interpretation.

Main Methods:

  • A deep learning algorithm was developed using 1733 lateral neck radiographs.
  • The AI model's performance was assessed independently and in conjunction with human readers (radiologists, residents, emergency physicians, ENT physicians).
  • Clinical application was simulated by comparing AI performance with radiologists' reports on a separate cohort.

Main Results:

  • The AI model achieved 96% sensitivity, 90% specificity, and 93% accuracy in the testing set.
  • AI-assisted reading significantly improved accuracy for all physician groups compared to unassisted reading.
  • The AI model identified 3 additional positive cases of animal bones compared to initial radiologists' reports in the simulation set.

Conclusions:

  • The deep learning AI model demonstrates high sensitivity for detecting animal bone impaction on lateral neck radiographs without increasing false positives.
  • Clinical implementation of this AI tool can expedite diagnosis, streamline workflow, and potentially reduce the need for CT scans.