A deep learning approach to case prioritisation of colorectal biopsies

Aims To create and validate a weakly supervised artificial intelligence (AI) model for detection of abnormal colorectal histology, including dysplasia and cancer, and prioritise biopsies according to clinical significance (severity of diagnosis). Materials and methods Triagnexia Colorectal , a weakly supervised deep learning model, was developed for the classification of colorectal samples from haematoxylin and eosin (H&E)‐stained whole slide images. The model was trained on 24 983 digitised images and assessed by multiple pathologists in a simulated digital pathology environment. The AI application was implemented as part of a point and click graphical user interface to streamline decision‐making. Pathologists assessed the accuracy of the AI tool, its value, ease of use and integration into the digital pathology workflow. Results Validation of the model was conducted on two cohorts: the first, on 100 single‐slide cases, achieved micro‐average model specificity of 0.984, micro‐average model sensitivity of 0.949 and micro‐average model F1 score of 0.949 across all classes. A secondary multi‐institutional validation cohort, of 101 single‐slide cases, achieved micro‐average model specificity of 0.978, micro‐average model sensitivity of 0.931 and micro‐average model F1 score of 0.931 across all classes. Pathologists reflected their positive impressions on the overall accuracy of the AI in detecting colorectal pathology abnormalities. Conclusions We have developed a high‐performing colorectal biopsy AI triage model that can be integrated into a routine digital pathology workflow to assist pathologists in prioritising cases and identifying cases with dysplasia/cancer versus non‐neoplastic biopsies.