Recent advances in machine learning for precision diagnosis and treatment of esophageal disorders

Abstract

The complex pathophysiology and diverse manifestations of esophageal disorders pose challenges in clinical practice, particularly in achieving accurate early diagnosis and risk stratification. While traditional approaches rely heavily on subjective interpretations and variable expertise, machine learning (ML) has emerged as a transformative tool in healthcare. We conducted a comprehensive review of published literature on ML applications in esophageal diseases, analyzing technical approaches, validation methods, and clinical outcomes. ML demonstrates superior performance: In gastroesophageal reflux disease, ML models achieve 80%-90% accuracy in potential of hydrogen-impedance analysis and endoscopic grading; for Barrett’s esophagus, ML-based approaches show 88%-95% accuracy in invasive diagnostics and 77%-85% accuracy in non-invasive screening. In esophageal cancer, ML improves early detection and survival prediction by 6%-10% compared to traditional methods. Novel applications in achalasia and esophageal varices demonstrate promising results in automated diagnosis and risk stratification, with accuracy rates exceeding 85%. While challenges persist in data standardization, model interpretability, and clinical integration, emerging solutions in federated learning and explainable artificial intelligence offer promising pathways forward. The continued evolution of these technologies, coupled with rigorous validation and thoughtful implementation, may fundamentally transform our approach to esophageal disease management in the era of precision medicine.

Keywords: Esophageal disorders; Machine learning; Gastroesophageal reflux disease; Esophageal cancer; Barrett’s esophagus; Achalasia; Clinical decision support system

Core Tip: This review synthesizes machine learning (ML) applications in esophageal disorders, emphasizing three critical advances: (1) Automated analysis of multimodal diagnostic data achieving accuracy rates of 80%-95% across different conditions; (2) Integration of deep learning with endoscopic imaging enabling real-time assistance in diagnosis and risk stratification; and (3) Development of novel non-invasive screening approaches through ML-based biomarker identification. The convergence of artificial intelligence with clinical medicine demonstrates transformative potential in addressing current diagnostic challenges and enabling precision medicine in esophageal disease management.