Rapid and Efficient Screening of <i>Helicobacter pylori</i> 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 reli...
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MDPI AG
2025-04-01
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| Series: | Diagnostics |
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| Online Access: | https://www.mdpi.com/2075-4418/15/9/1085 |
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| author | José Aneiros-Fernández Pedro Montero Pavón Natalia García Gómez Rosa María Palo Prian Ismael Sánchez García Ana Isabel Romero Ortiz Rodrigo López Castro César Casado-Sánchez Víctor Sánchez Turrión Antonio Luna Manuel Álvaro Berbís |
| author_facet | José Aneiros-Fernández Pedro Montero Pavón Natalia García Gómez Rosa María Palo Prian Ismael Sánchez García Ana Isabel Romero Ortiz Rodrigo López Castro César Casado-Sánchez Víctor Sánchez Turrión Antonio Luna Manuel Álvaro Berbís |
| author_sort | José Aneiros-Fernández |
| collection | DOAJ |
| description | <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. |
| format | Article |
| id | doaj-art-e2611e1ca9e84f2494a6ce131cd4becc |
| institution | OA Journals |
| issn | 2075-4418 |
| language | English |
| publishDate | 2025-04-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Diagnostics |
| spelling | doaj-art-e2611e1ca9e84f2494a6ce131cd4becc2025-08-20T02:30:46ZengMDPI AGDiagnostics2075-44182025-04-01159108510.3390/diagnostics15091085Rapid and Efficient Screening of <i>Helicobacter pylori</i> in Gastric Samples Stained with Warthin–Starry Using Deep LearningJosé Aneiros-Fernández0Pedro Montero Pavón1Natalia García Gómez2Rosa María Palo Prian3Ismael Sánchez García4Ana Isabel Romero Ortiz5Rodrigo López Castro6César Casado-Sánchez7Víctor Sánchez Turrión8Antonio Luna9Manuel Álvaro Berbís10Department of Pathology, University Hospital Complex of Granada, 18014 Granada, SpainDepartment of Anatomical Pathology, Hospital San Juan de la Cruz, 23400 Úbeda, Jaén, SpainDepartment of Histology, Faculty of Medicine, University of Cádiz, 11003 Cádiz, SpainPathology Department, University Hospital Virgen de las Nieves, 18014 Granada, SpainCells IA Technologies, 28006 Madrid, SpainPathology Department, University Hospital Virgen de las Nieves, 18014 Granada, SpainPathology Department, San Cecilio University Clinic Hospital, 18016 Granada, SpainFaculty of Medicine, Autonomous University of Madrid, 28029 Madrid, SpainFaculty of Medicine, Autonomous University of Madrid, 28029 Madrid, SpainDepartment of Integrated Diagnostics, HT Médica, Clínica Las Nieves, 23007 Jaén, SpainCells IA Technologies, 28006 Madrid, Spain<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.https://www.mdpi.com/2075-4418/15/9/1085artificial intelligencegastroenterology<i>Helicobacter pylori</i>deep learningwhole-slide imagingdigital pathology |
| spellingShingle | José Aneiros-Fernández Pedro Montero Pavón Natalia García Gómez Rosa María Palo Prian Ismael Sánchez García Ana Isabel Romero Ortiz Rodrigo López Castro César Casado-Sánchez Víctor Sánchez Turrión Antonio Luna Manuel Álvaro Berbís Rapid and Efficient Screening of <i>Helicobacter pylori</i> in Gastric Samples Stained with Warthin–Starry Using Deep Learning Diagnostics artificial intelligence gastroenterology <i>Helicobacter pylori</i> deep learning whole-slide imaging digital pathology |
| title | Rapid and Efficient Screening of <i>Helicobacter pylori</i> in Gastric Samples Stained with Warthin–Starry Using Deep Learning |
| title_full | Rapid and Efficient Screening of <i>Helicobacter pylori</i> in Gastric Samples Stained with Warthin–Starry Using Deep Learning |
| title_fullStr | Rapid and Efficient Screening of <i>Helicobacter pylori</i> in Gastric Samples Stained with Warthin–Starry Using Deep Learning |
| title_full_unstemmed | Rapid and Efficient Screening of <i>Helicobacter pylori</i> in Gastric Samples Stained with Warthin–Starry Using Deep Learning |
| title_short | Rapid and Efficient Screening of <i>Helicobacter pylori</i> in Gastric Samples Stained with Warthin–Starry Using Deep Learning |
| title_sort | rapid and efficient screening of i helicobacter pylori i in gastric samples stained with warthin starry using deep learning |
| topic | artificial intelligence gastroenterology <i>Helicobacter pylori</i> deep learning whole-slide imaging digital pathology |
| url | https://www.mdpi.com/2075-4418/15/9/1085 |
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