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Endoscopic Innovations in Gastrointestinal Diseases: From Diagnosis to Therapy

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Dr. Mattia Brigida

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Gastroenterology Unit, Department of Experimental Medicine, Università del Salento, 73100 Lecce, Italy
Interests: digestive endoscopy; interventional endoscopy; gastrointestinal neoplasia; endoscopic submucosal dissection; luminal gastroenterology/endoscopy; eosinophilic esophagitis

Dr. Gianluca Andrisani

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Digestive Endoscopy Unit, University Campus Bio-Medico, 00128 Rome, Italy
Interests: digestive endoscopy; interventional endoscopy; gastrointestinal neoplasia; endoscopic submucosal dissection; luminal gastroenterology/endoscopy

Special Issue Information

Dear Colleagues,

Endoscopy has revolutionized the field of gastroenterology, transforming both the diagnosis and treatment of gastrointestinal diseases. Continuous technological progress—from high-definition imaging and artificial intelligence–assisted endoscopy to advanced therapeutic tools—is reshaping clinical practice and patient outcomes.

Novel diagnostic approaches, including virtual chromoendoscopy and confocal laser endomicroscopy, allow real-time histologic assessment and earlier detection of neoplastic and inflammatory lesions. On the therapeutic side, innovations such as endoscopic submucosal dissection, full-thickness resection, endoscopic bariatric techniques, and endoscopic ultrasound-guided interventions are expanding the frontiers of minimally invasive therapy, reducing the need for surgery and improving recovery.

Moreover, the integration of digital platforms, robotics, and AI-driven analytics is paving the way toward personalized endoscopic strategies, with improved precision and safety.

The objective of this Special Issue, “Endoscopic Innovations in Gastrointestinal Diseases: From Diagnosis to Therapy,” is to provide an updated overview of the latest technological and clinical advances in diagnostic and therapeutic endoscopy, highlighting how innovation continues to expand the horizons of patient care across the gastrointestinal tract.

Dr. Mattia Brigida
Dr. Gianluca Andrisani
Guest Editors

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13 pages, 1796 KB

Open AccessArticle

Real-World Evaluation of an AI-Assisted Diagnostic Support System for Early Gastric Cancer: Diagnostic Performance, Confidence Stratification, and Determinants of False-Positive Diagnosis

by Satoshi Osawa, Takanori Yamada, Wataru Inui, Tomoyuki Niwa, Kenichi Takahashi, Takatoshi Egami, Keisuke Inagaki, Tomohiro Takebe, Tatsuhiro Ito, Satoru Takahashi, Shunya Onoue, Yusuke Asai, Kiichi Sugiura, Tomoharu Matsuura, Natsuki Ishida, Mihoko Yamade, Moriya Iwaizumi, Yasushi Hamaya and Ken Sugimoto

J. Clin. Med. 2026, 15(7), 2609; https://doi.org/10.3390/jcm15072609 - 29 Mar 2026

Viewed by 513

Abstract

Background/Objectives: Artificial intelligence (AI)-assisted endoscopy has shown high sensitivity for early gastric cancer detection; however, false-positive diagnoses remain a clinical challenge. This study aimed to evaluate the real-world diagnostic performance of a commercially available AI system and to identify factors associated with false-positive diagnoses, focusing on repeated AI evaluations and confidence stratification. Methods: This single-center retrospective study included 47 patients with 89 localized gastric lesions evaluated between March 2024 and March 2025. Endoscopic examinations were performed under white-light, non-magnified observation with repeated AI assessments of each lesion. The rates of “Consider biopsy” (B) judgments were calculated. Lesions with a B judgment rate of ≥50% were defined as AI-positive and classified into four AI confidence categories. Diagnostic performance was assessed using sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV). Factors associated with false-positive diagnoses were analyzed using penalized logistic regression. Results: The AI system demonstrated a sensitivity of 97.6% and an NPV of 95.7%, with a specificity of 45.8%. Pathology-positive rates decreased stepwise across the four AI confidence categories (p < 0.001). Among AI-positive lesions, low regional reproducibility, lesion size ≥ 30 mm, scar, and erosion were independently associated with false-positive diagnoses. In analyses restricted to non-neoplastic lesions, lesion size ≥ 30 mm remained significantly associated with false-positive diagnosis. Conclusions: In real-world clinical practice, a commercially available AI system provides high sensitivity for early gastric cancer detection. Incorporating confidence stratification and regional reproducibility into clinical decision-making may enhance the effective use of AI-assisted endoscopic diagnosis beyond binary interpretations. Full article

(This article belongs to the Special Issue Endoscopic Innovations in Gastrointestinal Diseases: From Diagnosis to Therapy)

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