ArticlePloS one2025
Early detection of esophageal cancer: Evaluating AI algorithms with multi-institutional narrowband and white-light imaging data.
Article in PloS one, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
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Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
6 citing papers in PubMed.
- Artificial Intelligence for Esophageal Precancerous Lesions and Esophageal Cancer.The Korean journal of helicobacter and upper gastrointestinal research · 2026Review
- Narrow-Band-Imaging-Derived Mean Optical Intensity: A Potential Biomarker for Monitoring the Progression of Oral Squamous Cell Carcinoma.Biomedicines · 2026Article
- The impact of AI on modern oncology from early detection to personalized cancer treatment.NPJ precision oncology · 2026Review
- Comparison of the diagnostic value of white light endoscopic, narrow band imaging, and iodine staining individually and in combination for early esophageal cancer and precancerous lesions.Frontiers in oncology · 2026Article
- Role of artificial intelligence in the detection and characterization of gastrointestinal premalignant and early malignant lesions.World journal of gastroenterology · 2025Review
- A YOLO Ensemble Framework for Detection of Barrett's Esophagus Lesions in Endoscopic Images.Diagnostics (Basel, Switzerland) · 2025Article
Corrections and comments
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Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Esophageal cancer is one of the most common cancers worldwide, especially esophageal squamous cell carcinoma, which is often diagnosed at a late stage and has a poor prognosis. This study aimed to develop an algorithm to detect tumors in esophageal endoscopy images using innovative artificial intelligence (AI) techniques for early diagnosis and detection of esophageal cancer. We used white light and narrowband imaging data collected from Gachon University Gil Hospital, and applied YOLOv5 and RetinaNet detection models to detect lesions. The models demonstrated high performance, with RetinaNet achieving a precision of 98.4% and sensitivity of 91.3% in the NBI dataset, and YOLOv5 attaining a precision of 93.7% and sensitivity of 89.9% in the WLI dataset. The generalizability of these models was further validated using external data from multiple institutions. This study demonstrates an effective method for detecting esophageal tumors through AI-based esophageal endoscopic image analysis. These efforts are expected to significantly reduce misdiagnosis rates, enhance the effective diagnosis and treatment of esophageal cancer, and promote the standardization of medical services.
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Registered trials
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