Machine Learning-Based Detection of Intestinal Metaplasia Identifies Clinically Missed Cases.

CONTEXT.—: Gastric intestinal metaplasia is a recognized precursor and risk factor to gastric cancer, and correct diagnosis is required for clinical decision-making. OBJECTIVE.—: To evaluate potential missed intestinal metaplasia diagnoses and develop automated approaches to quantification, we developed an artificial intelligence pipeline for review of both whole slide images and pathology reports. DESIGN.—: A patch-based classifier was trained and applied to 1935 gastric biopsy specimens. Specimens with disagreement between the artificial intelligence pipeline and the clinical pathology report were reviewed by 3 pathologists who were blinded to the diagnoses. RESULTS.—: Following review, 30 of 297 apparently "false-positive" artificial intelligence detections were determined to represent undetected intestinal metaplasia. The artificial intelligence quantification of intestinal metaplasia strongly agreed with human scoring (Spearman rank correlation coefficient, 0.90; P < .001). Finally, a large language model-based evaluation of pathology report text showed near perfect ability to categorize reports as positive or negative for intestinal metaplasia (99.3% of specimens) with a few reports evaluated as equivocal (0.7%). CONCLUSIONS.—: An artificial intelligence-based quality assurance pipeline could detect missed intestinal metaplasia in as many as 1.5% of gastric biopsy specimens, highlighting a potentially addressable diagnostic gap. An automated pipeline could additionally provide quantitative information that could be informative for management.

Duke Scholars

Author Katherine Schuver Garman Medicine, Gastroenterology

Author William Richard Jeck Pathology

Author Avani Anil Pendse Pathology

Author Wei Chen Pathology

Author Fengming Chen Pathology