Rapid and Efficient Screening of Helicobacter pylori in Gastric Samples Stained with Warthin–Starry Using Deep Learning

<b>Background/Objectives:</b> <i>Helicobacter pylori</i> is a major risk factor for gastric cancer. The incidence and prevalence of the pathogen are increasing worldwide, urging novel approaches to reduce detection turnaround times. <i>H. pylori</i> diagnosis relies on histological examination of gastric biopsies, but interobserver variability considerably impacts its identification. We present an algorithm combining a feature pyramid network and a ResNet architecture for automatic and rapid <i>H. pylori</i> detection in digitized Warthin-Starry-stained gastric biopsies. <b>Methods</b>: Whole-slide images were segmented into manually annotated smaller patches and segments containing stomach tissue were analyzed for the presence of Gram-negative bacteria. Patches classified as positive were examined to confirm the presence/absence of bacteria in contact with the gastric epithelial surface (<i>H. pylori</i>). <b>Results</b>: The algorithm exhibited 0.923 average precision and 0.982 average recall. The conducted efficiency study demonstrated that algorithm utilization significantly decreased (<i>p</i> < 0.001) diagnostic turnaround times for all participants (two pathologists, a pathology resident, a pathology technician, and a biotechnologist), observing an 88.13-91.76% time reduction. Implementation of the algorithm also improved diagnostic accuracy for the resident, technician, and biotechnologist, indicating that the tool remarkably supports less experienced personnel. <b>Conclusions</b>: We believe that the incorporation of our algorithm into pathology workflows will help standardize diagnostic protocols and drastically reduce <i>H. pylori</i> diagnostic turnaround times.