Quantitative Analysis of Distinct Colon Crypt Branching Modes Using Interpretable Machine Learning.
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 tw...
| Publicado en: | Inflammatory Bowel Diseases Vol. 31; no. 12; pp. 3407 - 3417 |
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| Autores principales: | , , , , , , , , |
| Formato: | pictorial research tables/charts Journal Article |
| Publicado: |
Oxford University Press / USA
Dec2025
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=191016754&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 191016754 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 10780998 N0V jtl: Inflammatory Bowel Diseases issn: 10780998 maglogo: N pubinfo: dt: Dec2025 vid: 31 iid: 12 pid: 622 pub: Oxford University Press / USA artinfo: ui: 191016754 191016754 191016754 10.1093/ibd/izaf230 191016754 ppf: 3407 ppct: 10 formats: tig: atl: Quantitative Analysis of Distinct Colon Crypt Branching Modes Using Interpretable Machine Learning. aug: au: Firmbach, Daniel Lang-Schwarz, Corinna Rubio, Carlos A Hartmann, Arndt Vieth, Michael Reitsam, Nic Grosser, Bianca Eckstein, Markus Matek, Christian affil: Institute of Pathology, Universitätsklinikum Erlangen, Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU), Erlangen, GermanyComprehensive Cancer Center Erlangen-EMN, Universitätsklinikum Erlangen, Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU), Erlangen, Germany sug: subj: Machine Learning Inflammatory Bowel Diseases Classification Colon Pathology Intestinal Mucosa Pathology Colon Anatomy and Histology Human Quantitative Studies Interrater Reliability Deep Learning Biological Markers Correlational Studies Intraclass Correlation Coefficient Funding Source ab: 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. pubtype: Academic Journal doctype: pictorial research tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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