Objectives: Develop an interpretable AI algorithm to rule out normal large bowel endoscopic biopsies saving pathologist resources. Design: Retrospective study. Setting: One UK NHS site was used for model training and internal validation. External validation conducted on data from two other NHS sites and one site in Portugal. Participants: 6,591 whole-slides images of endoscopic large bowel biopsies from 3,291 patients (54% Female, 46% Male). Main outcome measures: Area under the receiver operating characteristic and precision recall curves (AUC-ROC and AUC-PR), measuring agreement between consensus pathologist diagnosis and AI generated classification of normal versus abnormal biopsies. Results: A graph neural network was developed incorporating pathologist domain knowledge to classify the biopsies as normal or abnormal using clinically driven interpretable features. Model training and internal validation were performed on 5,054 whole slide images of 2,080 patients from a single NHS site resulting in an AUC-ROC of 0.98 (SD=0.004) and AUC-PR of 0.98 (SD=0.003). The predictive performance of the model was consistent in testing over 1,537 whole slide images of 1,211 patients from three independent external datasets with mean AUC-ROC = 0.97 (SD=0.007) and AUC-PR = 0.97 (SD=0.005). Our analysis shows that at a high sensitivity threshold of 99%, the proposed model can, on average, reduce the number of normal slides to be reviewed by a pathologist by 55%. A key advantage of IGUANA is its ability to provide an explainable output highlighting potential abnormalities in a whole slide image as a heatmap overlay in addition to numerical values associating model prediction with various histological features. Example results with interpretable features can be viewed online at https://iguana.dcs.warwick.ac.uk/. Conclusions: An interpretable AI model was developed to screen abnormal cases for review by pathologists. The model achieved consistently high predictive accuracy on independent cohorts showing its potential in optimising increasingly scarce pathologist resources and for achieving faster time to diagnosis. Explainable predictions of IGUANA can guide pathologists in their diagnostic decision making and help boost their confidence in the algorithm, paving the way for future clinical adoption.

SG, DS and NR are co-founders of Histofy Ltd. DS reports personal fees from Royal Philips, outside the submitted work. NR and FM report research funding from GlaxoSmithKline. All other authors declare no competing interests.

All authors would like to acknowledge the support from the PathLAKE digital pathology consortium which is funded by the Data to Early Diagnosis and Precision Medicine strand of the government Industrial Strategy Challenge Fund managed and delivered by UK Research and Innovation (UKRI). FM acknowledges funding from EPSRC grant EP/W02909X/1.

I confirm all relevant ethical guidelines have been followed, and any necessary IRB and/or ethics committee approvals have been obtained.

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The details of the IRB/oversight body that provided approval or exemption for the research described are given below:

This study was conducted under Health Research Authority National Research Ethics approval 15/NW/0843; IRAS 189095 and the Pathology image data Lake for Analytics, Knowledge and Education (PathLAKE) research ethics committee approval (REC reference 19/SC/0363, IRAS project ID 257932, South Central - Oxford C Research Ethics Committee)

I confirm that all necessary patient/participant consent has been obtained and the appropriate institutional forms have been archived, and that any patient/participant/sample identifiers included were not known to anyone (e.g., hospital staff, patients or participants themselves) outside the research group so cannot be used to identify individuals.

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I understand that all clinical trials and any other prospective interventional studies must be registered with an ICMJE-approved registry, such as ClinicalTrials.gov. I confirm that any such study reported in the manuscript has been registered and the trial registration ID is provided (note: if posting a prospective study registered retrospectively, please provide a statement in the trial ID field explaining why the study was not registered in advance).

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I have followed all appropriate research reporting guidelines and uploaded the relevant EQUATOR Network research reporting checklist(s) and other pertinent material as supplementary files, if applicable.

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Original WSIs from University Hospitals Coventry and Warwickshire NHS Trust, East Suffolk and North Essex NHS Foundation Trust and South Warwickshire NHS Foundation Trust will be made available upon completion of the PathLAKE project. Relevant information on obtaining the data from the IMP cohort can be found in the original publication. Graph data will be made available upon request, under a non-commercial Creative Commons license, to enable reproduction and improvement of the results obtained in this paper.