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Esophageal discoid foreign body detection and classification using artificial intelligence.

Bradley S Rostad1,2, Edward J Richer3,4, Erica L Riedesel3,4

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This summary is machine-generated.

Artificial intelligence accurately detects and classifies esophageal button batteries and coins in radiographs. This technology shows promise for improving the triage of these dangerous foreign bodies in children.

Keywords:
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Area of Science:

  • Medical Imaging
  • Artificial Intelligence in Healthcare
  • Pediatric Radiology

Background:

  • Radiographic diagnosis is crucial for managing radio-opaque esophageal foreign bodies in children.
  • Button batteries and coins are common and dangerous esophageal foreign bodies.
  • Artificial intelligence (AI) may aid in triaging radiographs for these foreign bodies.

Purpose of the Study:

  • To train an AI object detector to identify esophageal button batteries and coins.
  • To train an AI image classifier to differentiate between button batteries and coins.
  • To evaluate the AI models' performance in detecting and classifying these foreign bodies.

Main Methods:

  • Trained an object detector on 57 radiographs (button battery, coin, or no foreign body).
  • Trained an image classifier on 38 cropped images of button batteries and coins.
  • Tested both models on 206 radiographs (103 with foreign bodies, 103 without).

Main Results:

  • Object detector achieved 100% sensitivity and specificity for detecting esophageal foreign bodies.
  • Image classifier correctly identified 100% of button batteries and 97.9% of coins.
  • AI models demonstrated high accuracy in detecting and classifying common pediatric esophageal foreign bodies.

Conclusions:

  • AI models show significant potential for detecting and classifying esophageal discoid foreign bodies.
  • AI could streamline radiologist interpretation by triaging relevant radiographs.
  • This AI approach may improve the diagnostic workflow for esophageal foreign bodies.