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The Histological Detection of Ulcerative Colitis Using a No-Code Artificial Intelligence Model.

Yuichiro Hamamoto1,2, Michihiro Kawamura3, Hiroki Uchida3

  • 1Department of Diagnostic Pathology, Kinki Central Hospital of Mutual Aid Association of Public School Teachers, Itami, Hyogo, Japan.

International Journal of Surgical Pathology
|October 26, 2023
PubMed
Summary
This summary is machine-generated.

A novel artificial intelligence (AI) model accurately identifies ulcerative colitis (UC) histological patterns. This AI tool aids in diagnosing UC and other colonic conditions, addressing pathologist shortages.

Keywords:
adenocarcinomaartificial intelligenceinflammatory bowel diseasemachine learningulcerative colitis

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Area of Science:

  • Gastroenterology
  • Pathology
  • Artificial Intelligence

Background:

  • Ulcerative colitis (UC) is a chronic condition primarily affecting young adults, necessitating accurate histological diagnosis.
  • A shortage of diagnostic pathologists poses a challenge to timely and precise UC diagnosis.
  • Histological examination is critical for differentiating UC from other colonic diseases and normal tissue.

Purpose of the Study:

  • To develop and evaluate an artificial intelligence (AI) model for classifying histological images of ulcerative colitis (UC).
  • To assess the AI model's ability to distinguish UC from non-UC coloproctitis, adenocarcinoma, and control samples.
  • To demonstrate the utility of a no-code AI platform for complex diagnostic tasks in pathology.

Main Methods:

  • Utilized the no-code AI platform 'Teachable Machine' to train a classification model.
  • Trained the model on 5100 histological images, including UC, non-UC coloproctitis, adenocarcinoma, and control.
  • Validated the model's performance using an independent test set of 900 histological images.

Main Results:

  • The AI model achieved high accuracy rates across all categories: 0.99 for UC, 1.00 for non-UC coloproctitis, 0.99 for adenocarcinoma, and 0.99 for control.
  • The model demonstrated a strong capability to recognize the distinctive histological features of UC.
  • This study represents the first application of a no-code AI platform for comprehensive histological pattern recognition in UC.

Conclusions:

  • A no-code AI platform can effectively be trained to accurately differentiate histological images of ulcerative colitis.
  • The developed AI model shows significant potential to support pathologists in diagnosing UC and related conditions.
  • This approach offers a scalable and accessible solution to aid in the diagnosis of intractable gastrointestinal diseases.