Inflammatory bowel diseases · 2025
Quantitative Analysis of Distinct Colon Crypt Branching Modes Using Interpretable Machine Learning.
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Firmbach Daniel, Lang-Schwarz Corinna, Rubio Carlos A, Hartmann Arndt, Vieth Michael, Reitsam Nic, Grosser Bianca, Eckstein Markus, Matek Christian
Аннотация
BACKGROUND: Branching of colon crypts represents a histological hallmark of inflammatory bowel disease (IBD). The branching of the crypt has been observed to occur both symmetrically and asymmetrically, suggesting two distinct reaction patterns of the colon mucosa. Accurate classification of these two patterns can contribute to improved quantitative description and histologic characterization of IBD subtypes.
METHODS: We describe the morphology of branching crypts using manually crafted morphological features. Using a dataset annotated by an expert, we developed and implemented an machine learning model capable of classifying individual crypts based on these features. A multirater survey was conducted to compare interrater agreement between experts and our model.
RESULTS: A classic ensemble model utilizing our manually crafted features achieved a mean balanced accuracy of 0.80, while a deep learning-based model using the segmentation masks achieved a value of 0.79. The survey also showed moderate agreement between the classic ensemble model and senior pathologists.
CONCLUSIONS: We present a machine learning model capable of distinguishing both modes of crypt branching patterns. Furthermore, using a hand-crafted feature approach allowed us to directly interpret the classification criteria of our algorithm, rendering it more transparent and interpretable than black box classification models.
METHODS: We describe the morphology of branching crypts using manually crafted morphological features. Using a dataset annotated by an expert, we developed and implemented an machine learning model capable of classifying individual crypts based on these features. A multirater survey was conducted to compare interrater agreement between experts and our model.
RESULTS: A classic ensemble model utilizing our manually crafted features achieved a mean balanced accuracy of 0.80, while a deep learning-based model using the segmentation masks achieved a value of 0.79. The survey also showed moderate agreement between the classic ensemble model and senior pathologists.
CONCLUSIONS: We present a machine learning model capable of distinguishing both modes of crypt branching patterns. Furthermore, using a hand-crafted feature approach allowed us to directly interpret the classification criteria of our algorithm, rendering it more transparent and interpretable than black box classification models.