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Enhancing the Predictions of Cytomegalovirus Infection in Severe Ulcerative Colitis Using a Deep Learning Ensemble

Jeong Heon Kim1, A Reum Choe2, Ju Ran Byeon2

  • 1Department of Medicine, Yonsei University College of Medicine, Seoul, Republic of Korea.

JMIR Medical Informatics
|July 1, 2025
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Summary
This summary is machine-generated.

Deep learning models can now differentiate Cytomegalovirus (CMV) reactivation from severe ulcerative colitis (UC) using endoscopic images. This noninvasive approach aids early diagnosis and improves patient outcomes.

Keywords:
classificationcytomegalovirusdeep learningendoscopyulcerative colitis

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Area of Science:

  • Gastroenterology
  • Medical Imaging
  • Artificial Intelligence

Background:

  • Cytomegalovirus (CMV) reactivation in severe ulcerative colitis (UC) patients worsens outcomes.
  • Early detection of CMV reactivation is difficult due to reliance on invasive biopsy procedures.

Purpose of the Study:

  • To explore deep learning for differentiating CMV from severe UC using endoscopic imaging.
  • To develop a potential noninvasive diagnostic tool for CMV reactivation in UC.

Main Methods:

  • Analysis of 86 endoscopic images using an ensemble of deep learning models (DenseNet-121).
  • Application of advanced preprocessing and test-time augmentation (TTA) for performance optimization.
  • Evaluation using accuracy, precision, recall, F1-score, and AUC.

Main Results:

  • The ensemble model with TTA achieved high performance: 0.836 accuracy, 0.850 precision, 0.904 recall, and 0.875 F1-score.
  • Test-time augmentation significantly improved classification performance compared to models without TTA.

Conclusions:

  • Deep learning models effectively distinguish CMV from severe UC in endoscopic images.
  • This approach offers a pathway for early, noninvasive diagnosis.
  • Improved diagnostic capabilities can lead to better patient care for UC patients with potential CMV reactivation.