A deep-learning-based model for assessment of autoimmune hepatitis from histology: AI(H)

Caner Ercan; Kattayoun Kordy; Anna Knuuttila; Xiaofei Zhou; Darshan Kumar; Ville Koponen; Peter Mesenbrink; Serenella Eppenberger-Castori; Parisa Amini; Marcos C Pedrosa; Luigi M Terracciano

doi:10.1007/s00428-024-03841-5

A deep-learning-based model for assessment of autoimmune hepatitis from histology: AI(H)

Virchows Arch. 2024 Jun 15. doi: 10.1007/s00428-024-03841-5. Online ahead of print.

Authors

Affiliations

¹ Institute of Pathology and Medical Genetics, University Hospital Basel, University of Basel, Schönbeinstrasse 40 4056, Basel, Switzerland. caner.ercan@unibas.ch.
² Novartis Pharmaceuticals Corporation, East Hanover, NJ, USA.
³ Aiforia Technologies PLC, Helsinki, Finland.
⁴ Institute of Pathology and Medical Genetics, University Hospital Basel, University of Basel, Schönbeinstrasse 40 4056, Basel, Switzerland.
⁵ Novartis Institutes for BioMedical Research, Basel, Switzerland.
⁶ Novartis Pharma AG, Basel, Switzerland.
⁷ Department of Biomedical Sciences, Humanitas University, Pieve Emanuele, Milan, Italy.
⁸ IRCCS Humanitas Research Hospital, Rozzano, Milan, Italy.

^# Contributed equally.

PMID: 38879691
DOI: 10.1007/s00428-024-03841-5

Abstract

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 aim to develop a deep learning tool (artificial intelligence for autoimmune hepatitis [AI(H)]) that analyzes the liver biopsies and provides reproducible, quantifiable, and interpretable results directly from routine pathology slides. A total of 123 pre-treatment liver biopsies, whole-slide images with confirmed AIH diagnosis from the archives of the Institute of Pathology at University Hospital Basel, were used to train several convolutional neural network models in the Aiforia artificial intelligence (AI) platform. The performance of AI models was evaluated on independent test set slides against pathologist's manual annotations. The AI models were 99.4%, 88.0%, 83.9%, 81.7%, and 79.2% accurate (ratios of correct predictions) for tissue detection, liver microanatomy, necroinflammation features, bile duct damage detection, and portal inflammation detection, respectively, on hematoxylin and eosin-stained slides. Additionally, the immune cells model could detect and classify different immune cells (lymphocyte, plasma cell, macrophage, eosinophil, and neutrophil) with 72.4% accuracy. On Sirius red-stained slides, the test accuracies were 99.4%, 94.0%, and 87.6% for tissue detection, liver microanatomy, and fibrosis detection, respectively. Additionally, AI(H) showed bile duct injury in 81 AIH cases (68.6%). The AI models were found to be accurate and efficient in predicting various morphological components of AIH biopsies. The computational analysis of biopsy slides provides detailed spatial and density data of immune cells in AIH landscape, which is difficult by manual counting. AI(H) can aid in improving the reproducibility of AIH biopsy assessment and bring new descriptive and quantitative aspects to AIH histology.

Keywords: AIH; Artificial intelligence; Autoimmune hepatitis; Computational pathology; Deep-learning.