ArticleEsophagus : official journal of the Japan Esophageal Society2020
Application of artificial intelligence using convolutional neural networks in determining the invasion depth of esophageal squamous cell carcinoma.
Article in Esophagus : official journal of the Japan Esophageal Society, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 59 papers, 4 of them syntheses that pooled it.
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Who cites it
59 citing papers in PubMed, 4 syntheses or guidelines pooled it, 113 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
- Systematic review with meta-analysis: artificial intelligence in the diagnosis of oesophageal diseases.Alimentary pharmacology & therapeutics · 2022Pooled it
- Accuracy of artificial intelligence-assisted detection of esophageal cancer and neoplasms on endoscopic images: A systematic review and meta-analysis.Journal of digestive diseases · 2021Pooled it
- Convolutional neural network-based artificial intelligence for the diagnosis of early esophageal cancer based on endoscopic images: A meta-analysis.Saudi journal of gastroenterology : official journal of the Saudi Gastroenterology AssociationPooled it
- AI in esophageal cancer: advances, barriers to clinical translation, and perspectives for digital health.Journal of translational medicine · 2026Review
- Artificial Intelligence in Gastrointestinal Endoscopy and Hemostatic Decision-Making: Current Evidence, Clinical Implications and Implementation Barriers.Life (Basel, Switzerland) · 2026Review
- Artificial intelligence-assisted endoscopic diagnosis of esophageal squamous cell carcinoma.Frontiers in artificial intelligence · 2026Review
- Application of convolutional neural networks for automated segmentation and classification in esophageal diseases.Frontiers in medicine · 2026Article
- Role of artificial intelligence in the detection and characterization of gastrointestinal premalignant and early malignant lesions.World journal of gastroenterology · 2025Review
- Unraveling the characteristics of early esophageal neuroendocrine carcinoma using multi-model endoscopy: A retrospective study of serial cases.World journal of gastrointestinal oncology · 2025Article
- Artificial Intelligence in Upper Gastrointestinal Endoscopy: Current Evidence, Practice, and Future Directions.Cureus · 2025Review
- Translational artificial intelligence in gastrointestinal and hepatic disorders: Advancing intelligent clinical decision-making for diagnosis, treatment, and prognosis.World journal of gastroenterology · 2025Review
- Artificial intelligence in endoscopic diagnosis of esophageal squamous cell carcinoma and precancerous lesions.Chinese medical journal · 2025Review
- The role of nanomedicine and artificial intelligence in cancer health care: individual applications and emerging integrations-a narrative review.Discover oncology · 2025Review
- Artificial Intelligence-Powered Quality Assurance: Transforming Diagnostics, Surgery, and Patient Care-Innovations, Limitations, and Future Directions.Life (Basel, Switzerland) · 2025Review
- The application of artificial intelligence in upper gastrointestinal cancers.Journal of the National Cancer Center · 2025Review
- Attention-Enhanced Multi-Task Deep Learning Model for Classification and Segmentation of Esophageal Lesions.ACS omega · 2025Article
- Efficiency of endoscopic artificial intelligence in the diagnosis of early esophageal cancer.Thoracic cancer · 2024Article
- Computer-aided endoscopic diagnostic system modified with hyperspectral imaging for the classification of esophageal neoplasms.Frontiers in oncology · 2024Article
- Artificial Intelligence Model for a Distinction between Early-Stage Gastric Cancer Invasive Depth T1a and T1b.Journal of Cancer · 2024Article
Corrections and comments
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Authors and funding
14 authors at 7 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
objectivesIn Japan, endoscopic resection (ER) is often used to treat esophageal squamous cell carcinoma (ESCC) when invasion depths are diagnosed as EP-SM1, whereas ESCC cases deeper than SM2 are treated by surgical operation or chemoradiotherapy. Therefore, it is crucial to determine the invasion depth of ESCC via preoperative endoscopic examination. Recently, rapid progress in the utilization of artificial intelligence (AI) with deep learning in medical fields has been achieved. In this study, we demonstrate the diagnostic ability of AI to measure ESCC invasion depth.
methodsWe retrospectively collected 1751 training images of ESCC at the Cancer Institute Hospital, Japan. We developed an AI-diagnostic system of convolutional neural networks using deep learning techniques with these images. Subsequently, 291 test images were prepared and reviewed by the AI-diagnostic system and 13 board-certified endoscopists to evaluate the diagnostic accuracy.
resultsThe AI-diagnostic system detected 95.5% (279/291) of the ESCC in test images in 10 s, analyzed the 279 images and correctly estimated the invasion depth of ESCC with a sensitivity of 84.1% and accuracy of 80.9% in 6 s. The accuracy score of this system exceeded those of 12 out of 13 board-certified endoscopists, and its area under the curve (AUC) was greater than the AUCs of all endoscopists.
conclusionsThe AI-diagnostic system demonstrated a higher diagnostic accuracy for ESCC invasion depth than those of endoscopists and, therefore, can be potentially used in ESCC diagnostics.
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