ArticleSurgical endoscopy2022
Artificial intelligence for diagnosing microvessels of precancerous lesions and superficial esophageal squamous cell carcinomas: a multicenter study.
Article in Surgical endoscopy, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 16 papers, 1 of them a synthesis that pooled it.
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Who cites it
16 citing papers in PubMed, 1 synthesis or guideline pooled it, 21 citations in OpenAlex.
- The role of artificial intelligence in the endoscopic diagnosis of esophageal cancer: a systematic review and meta-analysis.Diseases of the esophagus : official journal of the International Society for Diseases of the Esophagus · 2023Pooled it
- Artificial Intelligence for Esophageal Precancerous Lesions and Esophageal Cancer.The Korean journal of helicobacter and upper gastrointestinal research · 2026Review
- Review
- Opportunities and challenges of artificial intelligence-assisted endoscopy and high-quality data for esophageal squamous cell carcinoma.World journal of gastrointestinal oncology · 2026Review
- Artificial intelligence-assisted endoscopic diagnosis of esophageal squamous cell carcinoma.Frontiers in artificial intelligence · 2026Review
- Role of artificial intelligence in the detection and characterization of gastrointestinal premalignant and early malignant lesions.World journal of gastroenterology · 2025Review
- Artificial intelligence in endoscopic diagnosis of esophageal squamous cell carcinoma and precancerous lesions.Chinese medical journal · 2025Review
- Artificial intelligence-aided optical biopsy improves the diagnosis of esophageal squamous neoplasm.World journal of gastroenterology · 2025Article
- Effect of computer aided detection system on esophageal neoplasm diagnosis in varied levels of endoscopists.NPJ digital medicine · 2025Article
- A review of deep learning methods for gastrointestinal diseases classification applied in computer-aided diagnosis system.Medical & biological engineering & computing · 2025Review
- Artificial intelligence enhances the management of esophageal squamous cell carcinoma in the precision oncology era.World journal of gastroenterology · 2024Review
- Efficiency of endoscopic artificial intelligence in the diagnosis of early esophageal cancer.Thoracic cancer · 2024Article
- Novel milestones for early esophageal carcinoma: From bench to bed.World journal of gastrointestinal oncology · 2024Article
- Latest Advances in Endoscopic Detection of Oesophageal and Gastric Neoplasia.Diagnostics (Basel, Switzerland) · 2024Review
- Exploring the challenge of early gastric cancer diagnostic AI system face in multiple centers and its potential solutions.Journal of gastroenterology · 2023Article
- The current state of artificial intelligence in endoscopic diagnosis of early esophageal squamous cell carcinoma.Frontiers in oncology · 2023Review
Corrections and comments
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Authors and funding
16 authors at 5 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundIntrapapillary capillary loop (IPCL) is an important factor for predicting invasion depth of esophageal squamous cell carcinoma (ESCC). The invasion depth is closely related to the selection of treatment strategy. However, diagnosis of IPCLs is complicated and subject to interobserver variability. This study aimed to develop an artificial intelligence (AI) system to predict IPCLs subtypes of precancerous lesions and superficial ESCC.
methodsImages of magnifying endoscopy with narrow band imaging from three hospitals were collected retrospectively. IPCLs subtypes were annotated on images by expert endoscopists according to Japanese Endoscopic Society classification. The performance of the AI system was evaluated using internal and external validation datasets (IVD and EVD) and compared with that of the 11 endoscopists.
resultsA total of 7094 images from 685 patients were used to train and validate the AI system. The combined accuracy of the AI system for diagnosing IPCLs subtypes in IVD and EVD was 91.3% and 89.8%, respectively. The AI system achieved better performance than endoscopists in predicting IPCLs subtypes and invasion depth. The ability of junior endoscopists to diagnose IPCLs subtypes (combined accuracy: 84.7% vs 78.2%, P < 0.0001) and invasion depth (combined accuracy: 74.4% vs 67.9%, P < 0.0001) were significantly improved with AI system assistance. Although there was no significant differences, the performance of senior endoscopists was slightly elevated.
conclusionsThe proposed AI system could improve the diagnostic ability of endoscopists to predict IPCLs classification of precancerous lesions and superficial ESCC.
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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.