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Peptic Ulcer Disease III: Clinical Manifestations and Diagnostic Studies01:28

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Related Experiment Video

Updated: Aug 22, 2025

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PHF3 Technique: A Pyramid Hybrid Feature Fusion Framework for Severity Classification of Ulcerative Colitis Using

Jing Qi1, Guangcong Ruan2, Jia Liu1

  • 1Department of Digital Medicine, School of Biomedical Engineering and Imaging Medicine, Army Medical University (Third Military Medical University), Chongqing 400038, China.

Bioengineering (Basel, Switzerland)
|November 10, 2022
PubMed
Summary

This study introduces a new AI tool, the PHF3 model, to help classify ulcerative colitis (UC) severity using endoscopic images. The model shows high accuracy, aiding in more reliable patient assessments.

Keywords:
Mayo endoscopic subscoredeep learningfeature fusionhybrid architectureulcerative colitis

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Area of Science:

  • Medical Imaging
  • Artificial Intelligence
  • Gastroenterology

Background:

  • Accurate ulcerative colitis (UC) severity assessment via Mayo endoscopic subscore (MES) is vital for treatment.
  • Inter-class similarities and intra-class differences in UC lesions challenge manual MES classification.
  • Endoscopist inexperience and fatigue impact MES evaluation reliability and repeatability.

Purpose of the Study:

  • To develop an AI-powered auxiliary diagnostic tool for objective and reliable UC severity classification.
  • To enhance the accuracy and consistency of MES evaluations using a novel deep learning framework.

Main Methods:

  • Proposed a pyramid hybrid feature fusion (PHF3) framework integrating ResNet50 and pyramid vision Transformer (PvT).
  • Employed a dual-branch architecture to extract local and global intestinal features.
  • Utilized a feature fusion module (FFM) and second-order pooling (SOP) for enhanced classification.

Main Results:

  • The PHF3 model achieved high performance in classifying MES grades.
  • Area under the ROC curve (AUC) values were 0.996 (MES 0), 0.972 (MES 1), 0.967 (MES 2), and 0.990 (MES 3).
  • Overall classification accuracy reached 88.91%.

Conclusions:

  • The PHF3 framework demonstrates significant potential as an auxiliary tool for UC severity assessment.
  • This AI approach can improve the reliability and repeatability of MES evaluations in clinical practice.
  • The developed system offers a valuable advancement for objective UC severity classification.