ArticleModern pathology : an official journal of the United States and Canadian Academy of Pathology, Inc2022
Assessment of deep learning assistance for the pathological diagnosis of gastric cancer.
Article in Modern pathology : an official journal of the United States and Canadian Academy of Pathology, Inc, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 29 papers.
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Who cites it
29 citing papers in PubMed, 75 citations in OpenAlex.
- Development of an artificial intelligence-based computer-aided detection system for routine gastric biopsy diagnosis.Journal of pathology informatics · 2026Article
- AI caption generation model for digital pathology of adenocarcinoma in endoscopic histopathology using multi-instance attention mechanisms.Scientific reports · 2026Article
- Artificial intelligence for biomarker prediction in gastric cancer: from histopathology to multimodal integration.Frontiers in oncology · 2026Review
- Current Role of Artificial Intelligence in the Management of Gastric Cancer.Biomedicines · 2025Review
- Rapid detection of mouse spermatogenic defects by testicular cellular composition analysis via enhanced deep learning model.Andrology · 2025Article
- GastritisMIL: An interpretable deep learning model for the comprehensive histological assessment of chronic gastritis.Patterns (New York, N.Y.) · 2025Article
- Development and application of deep learning-based diagnostics for pathologic diagnosis of gastric endoscopic submucosal dissection specimens.Gastric cancer : official journal of the International Gastric Cancer Association and the Japanese Gastric Cancer Association · 2025Article
- Significant reduction in manual annotation costs in ultrasound medical image database construction through step by step artificial intelligence pre-annotation.PLOS digital health · 2025Article
- The application of artificial intelligence in upper gastrointestinal cancers.Journal of the National Cancer Center · 2025Review
- Review
- The artificial intelligence revolution in gastric cancer management: clinical applications.Cancer cell international · 2025Review
- Deep learning model targeting cancer surrounding tissues for accurate cancer diagnosis based on histopathological images.Journal of translational medicine · 2025Article
- Article
- Big Data and Artificial Intelligence in Drug Discovery for Gastric Cancer: Current Applications and Future Perspectives.Current medicinal chemistry · 2025Review
- Harbor seal whiskers optimization algorithm with deep learning-based medical imaging analysis for gastrointestinal cancer detection.Health information science and systems · 2024Article
- Smartphone-based machine learning model for real-time assessment of medical kidney biopsy.Journal of pathology informatics · 2024Article
- SenseCare: a research platform for medical image informatics and interactive 3D visualization.Frontiers in radiology · 2024Article
- Applications of artificial intelligence in digital pathology for gastric cancer.Frontiers in oncology · 2024Review
- AI-Powered Segmentation of Invasive Carcinoma Regions in Breast Cancer Immunohistochemical Whole-Slide Images.Cancers · 2023Article
- Landscape of Innovative Methods for Early Diagnosis of Gastric Cancer: A Systematic Review.Diagnostics (Basel, Switzerland) · 2023Review
Corrections and comments
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Authors and funding
12 authors at 6 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Previous studies on deep learning (DL) applications in pathology have focused on pathologist-versus-algorithm comparisons. However, DL will not replace the breadth and contextual knowledge of pathologists; rather, only through their combination may the benefits of DL be achieved. A fully crossed multireader multicase study was conducted to evaluate DL assistance with pathologists' diagnosis of gastric cancer. A total of 110 whole-slide images (WSI) (50 malignant and 60 benign) were interpreted by 16 board-certified pathologists with or without DL assistance, with a washout period between sessions. DL-assisted pathologists achieved a higher area under receiver operating characteristic curve (ROC-AUC) (0.911 vs. 0.863, P = 0.003) than unassisted in interpreting the 110 WSIs. Pathologists with DL assistance demonstrated higher sensitivity in detection of gastric cancer than without (90.63% vs. 82.75%, P = 0.010). No significant difference was observed in specificity with or without deep learning assistance (78.23% vs. 79.90%, P = 0.468). The average review time per WSI was shortened with DL assistance than without (22.68 vs. 26.37 second, P = 0.033). Our results demonstrated that DL assistance indeed improved pathologists' accuracy and efficiency in gastric cancer diagnosis and further boosted the acceptance of this new technique.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.