> For the complete documentation index, see [llms.txt](https://www.parapathology.com/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://www.parapathology.com/clippings/a-deep-learning-based-model-for-assessment-of-autoimmune-hepatitis-from-histology-ai-h.md). # A deep-learning-based model for assessment of autoimmune hepatitis from histology: AI(H) Histological assessment of autoimmune hepatitis (AIH) is challenging. As one of the possible results of these challenges, nonclassical features such as bile-duct injury stays understudied in AIH. We a ### Summary Autoimmune hepatitis (AIH) has no pathognomonic lesion. Its biopsies show the elementary lesions of any chronic hepatitis, graded semi-quantitatively with poor inter-observer agreement, and non-classical features such as bile duct injury are understudied. **AI(H)** is a stack of convolutional networks trained on the Aiforia platform. Each model targets one part of AIH histology: * on H\&E: tissue, liver microanatomy, necroinflammation, portal inflammation, immune-cell types and bile duct damage; * on Sirius red: fibrosis. The models were trained on pre-treatment biopsies from University Hospital Basel. Against pathologist annotations, test accuracy ranged from 72% (immune-cell classification) to 99% (tissue detection). The tool found bile duct injury in about two-thirds of AIH biopsies. > The abstract in the `description:` frontmatter is the paper's own, verbatim. The sections below are my own-words digest of the abstract and the open-access full text in PubMed Central. ### Citation Ercan C, Kordy K, Knuuttila A, Zhou X, Kumar D, Koponen V, et al. A deep-learning-based model for assessment of autoimmune hepatitis from histology: AI(H). *Virchows Arch*. 2024;485(6):1095–1105. Epub 2024 Jun 15. doi: [10.1007/s00428-024-03841-5](https://doi.org/10.1007/s00428-024-03841-5). PMID: [38879691](https://pubmed.ncbi.nlm.nih.gov/38879691/). PMCID: [PMC11666607](https://pmc.ncbi.nlm.nih.gov/articles/PMC11666607/). * **Platform:** Aiforia (cloud-based, supervised deep learning) * **Access:** open access, full text in PubMed Central ### Study at a glance | Item | Detail | | ---------------- | ------------------------------------------------------------------------------------------------------------------------------------ | | Cohort | 116 adult AIH patients (94 women, 22 men; mean age 59), Basel archive 1996–2020; 123 paired H\&E and Sirius red pre-treatment slides | | Split | Random 80/20: 99 training slides, 24 test slides | | Annotations | Immune cells by two pathologists (7,868 immune-cell annotations in total); all other classes by one pathologist | | Clinical grading | Ishak grading and staging by two pathologists in consensus, plus International AIH Pathology Group criteria | | Scanner | 3DHISTECH Pannoramic SCAN II, 40× (0.24 µm/px) | ### Key findings | Model | Stain | Test accuracy | | -------------------------------------------------------------------------- | ----------------- | -------------------------------------- | | Tissue detection | H\&E / Sirius red | 99.4% / 99.4% | | Liver microanatomy (portal, parenchyma, central vein) | H\&E / Sirius red | 88.0% / 94.0% | | Necroinflammation (interface hepatitis, focal and confluent necrosis) | H\&E | 83.9% | | Bile duct damage | H\&E | 81.7% | | Portal inflammation (mild / moderate / severe) | H\&E | 79.2% | | Immune cells (lymphocyte, plasma cell, macrophage, eosinophil, neutrophil) | H\&E | 72.4% | | Fibrosis | Sirius red | 87.6% (88.0% in the full-text Results) | * **Bile duct injury** appeared in 68.6% of biopsies (81 cases). That is closer to one earlier report (72%) than to others (24%, 83%). * **Tracking the pathologists.** AI(H) counts of focal necrosis, the extent of interface hepatitis and the moderate portal-inflammation ratio all rose in step with the pathologists' Ishak scores, and so did the densities of all five immune-cell types. * **Toward the consensus criteria.** Fed into the International AIH Pathology Group criteria, AI(H) outputs sorted biopsies into "likely" and "possible" AIH with 88.2% agreement with the pathologist. Most errors came from overcalling interface hepatitis and from faded archival slides. * **Other hepatitides.** Exploratory runs on a handful of drug-induced, HBV and HCV biopsies recognised the same elementary lesions, with some errors. ### Limitations noted by the authors * Single institution, with most annotations by one pathologist. Consensus grading meant inter-observer variability could not be measured. * Accuracy fell in heavily necroinflamed or architecturally disrupted biopsies, and immune-cell detection struggled in dense infiltrates. * The tool is image-only: no clinical or laboratory data, and other liver diseases were excluded, so it is not a diagnostic tool. * Fibrosis used Sirius red only. There was no elastic stain to separate recent collapse from established fibrosis. ### On the lecture slide From a lecture slide titled *"Top quantitative models in GI/liver path"*: | Disease | Model | Output | Training set | Reference | | -------------------- | ------------- | ----------------------------- | ------------ | -------------------------------- | | Autoimmune hepatitis | AI(H) (Ercan) | Immune-cell density, necrosis | 99 WSIs | Ercan et al. Virchows Arch. 2024 | "99 WSIs" **matches** the training split (99 training, 24 test, of 123 slides).