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Automatic Diagnosis of High-Resolution Esophageal Manometry Using Artificial Intelligence.

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Artificial intelligence (AI) can now automatically diagnose esophageal motility disorders (EMDs) using high-resolution esophageal manometry (HREM) images. This AI system achieved over 93% accuracy, offering a more efficient diagnostic approach for EMDs.

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Area of Science:

  • Gastroenterology
  • Medical Imaging
  • Artificial Intelligence

Background:

  • High-resolution esophageal manometry (HREM) is the standard for diagnosing esophageal motility disorders (EMDs).
  • Interpreting HREM images can be subjective, highlighting the need for objective diagnostic tools.
  • Artificial intelligence (AI) offers potential for automated, accurate EMD diagnosis from HREM data.

Purpose of the Study:

  • To develop and evaluate an AI-based system for the automatic diagnosis of EMDs.
  • The system utilizes neural networks to analyze raw HREM images from single wet swallows.

Main Methods:

  • Retrospective analysis of HREM recordings by experienced gastroenterologists to confirm diagnoses.
  • Training an artificial neural network (Inception V3 CNN) on 1570 HREM images across 10 EMD categories (Chicago Classification v3.0).
  • Image preprocessing included cropping, binarization, and splitting into training, testing, and validation sets.

Main Results:

  • The AI algorithm achieved high accuracy in classifying HREM images.
  • Overall precision of the automated diagnostic system exceeded 93%.
  • The neural network successfully categorized HREM images into specific EMD diagnoses.

Conclusions:

  • An AI-powered approach using HREM images enables accurate, automated diagnosis of EMDs.
  • This technology has the potential to improve the efficiency and objectivity of EMD diagnosis.