AI triage of duodenal biopsies improves workflow

Clinical Snapshot

Key Findings

• P Value: p<0.005 for both coeliac disease and non-neoplastic abnormalities

• Effect Size: 4-day reduction for coeliac disease (10 vs 6 days), 3-day reduction for non-neoplastic abnormalities (10 vs 7 days)

• Primary Outcome: Reporting turnaround times for different pathology categories

• Nnt Or Sensitivity: Sensitivity: normal bowel 99.6%, coeliac disease 86.7%, gastric heterotopia 84.6%, adenoma 88.9%, adenocarcinoma 50.0%

• Confidence Interval: Not reported

Clinical Application

Demonstrated feasibility in NHS setting with reasonable costs and 70-second processing time per slide Highly relevant to Australian public hospital pathology services facing similar workforce pressures and turnaround time targets. Implementation would require TGA consideration for AI medical devices and integration with existing laboratory information systems Public hospital histopathology laboratories processing duodenal biopsies, particularly those with workflow bottlenecks

Abstract

AIMS: To develop, deploy and evaluate artificial intelligence (AI) for triaging duodenal biopsies within a National Health Service (NHS) histopathology laboratory, with the aim of improving reporting turnaround times for clinically significant diagnoses. METHODS: The pathway was developed in the UK in an NHS laboratory. Rule-based automation software was used to find all newly scanned duodenal biopsy slides. Those with case numbers ending in an odd number were exported for AI triage and if they had significant AI predicted abnormalities they were prioritised for reporting. The cases with even numbers followed the routine reporting pathway. RESULTS: 313 cases (517 duodenal slides) were processed by the routine pathway, and 329 cases (533 duodenal slides) were processed by the AI triage pathway. AI processing took about 70 s per slide. The AI classifier had a sensitivity and positive predictive value (PPV) as follows: normal small bowel: 99.6%, 95.7%; coeliac disease: 86.7%, 100%; gastric heterotopia: 84.6%, 95.7%; adenoma: 88.9%, 88.9%; adenocarcinoma: 50.0%, 100%. In the AI triage workstream coeliac disease, and non-neoplastic abnormalities as a group, were reported quicker than in the standard workstream (6 days vs 10 days and 7 days vs 10 days, respectively, both p<0.005), but neoplastic lesions were not reported quicker. The cost of deployment and operation was reasonable. CONCLUSIONS: An NHS histopathology laboratory successfully developed and implemented an AI-based triage system for duodenal biopsies, achieving high diagnostic accuracy and significantly improving turnaround times for coeliac disease and non-neoplastic abnormalities as a group. This study demonstrates the feasibility and clinical value of locally developed AI tools within routine diagnostic practice.

References

1. 1.Mayall, F. G., Mayall, C., Bodger, I., & Mayall, H. (2026). AI triage of duodenal biopsies improves workflow. Journal of Clinical Pathology. https://doi.org/10.1136/jcp-2026-210658