Histological AI-Assisted Analysis Outperforms Pathologist Assessment in Predicting Disease Outcomes in PSC Patients.

Anna M Salonen^1,2, Iiris Nyholm², Linn Tähti³, Matilda Sinkko³, Darshan Kumar³, Martti Färkkilä⁴, Johanna Arola¹, Mikko P Pakarinen², Sonja Boyd¹, Nelli Sjöblom⁵

¹Department of Pathology, Helsinki University and Helsinki University Hospital (HUH), HUS Diagnostic Center, Helsinki, Finland.

²Section of Pediatric Surgery, Pediatric Liver and Gut Research Group, New Children's Hospital, University of Helsinki and Helsinki University Hospital, Helsinki, Finland.

³Aiforia Technologies Plc, Helsinki, Finland.

⁴Helsinki University and Helsinki University Hospital, Abdominal Center, Helsinki, Finland.

⁵Department of Dermatopathology, University of Helsinki, Helsinki University Hospital (HUH), Helsinki, Finland.

PubMed DOI

Abstract

AIMS: Liver biopsy has shown prognostic significance in primary sclerosing cholangitis (PSC). However, histology is typically assessed manually based on coarse grading scales, making precise quantification difficult. We developed a neural network model to address the key histological features of liver tissue, including portal tracts, fibrosis, biliary epithelium, portal inflammation, and vasculatu…

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