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Quantitative Analysis of Distinct Colon Crypt Branching Modes Using Interpretable Machine Learning.

Daniel Firmbach1,2, Corinna Lang-Schwarz2,3,4, Carlos A Rubio5

  • 1Institute of Pathology, Universitätsklinikum Erlangen, Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU), Erlangen, Germany.

Inflammatory Bowel Diseases
|October 29, 2025
PubMed
Summary
This summary is machine-generated.

Machine learning models can now differentiate between symmetric and asymmetric colon crypt branching patterns in inflammatory bowel disease (IBD). This classification aids in better understanding IBD subtypes and improving diagnostic accuracy.

Keywords:
biomarkersimagingpathology

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Area of Science:

  • Gastroenterology
  • Computational Pathology
  • Histology

Background:

  • Colon crypt branching is a key histological feature of inflammatory bowel disease (IBD).
  • Observed branching patterns include symmetric and asymmetric modes, indicating distinct mucosal responses.
  • Classifying these patterns is crucial for quantitative analysis and characterizing IBD subtypes.

Purpose of the Study:

  • To develop and validate a machine learning model for classifying colon crypt branching patterns.
  • To compare the performance of machine learning models against expert pathologists.
  • To enhance the histological characterization of IBD.

Main Methods:

  • Manually crafted morphological features were used to describe crypt branching.
  • A machine learning model was developed and trained on expert-annotated data.
  • A multirater survey assessed interrater agreement between the model and pathologists.

Main Results:

  • A classic ensemble model achieved 0.80 balanced accuracy, while a deep learning model achieved 0.79.
  • The ensemble model demonstrated moderate agreement with senior pathologists.
  • The study successfully classified distinct crypt branching modes.

Conclusions:

  • A machine learning model can effectively distinguish between symmetric and asymmetric crypt branching.
  • The use of hand-crafted features provides interpretability, unlike black-box models.
  • This approach offers a transparent and reliable tool for IBD histological analysis.