Journal of basic and clinical physiology and pharmacology · 2025
Artificial intelligence applied to achalasia: an emerging frontier in precision motility care? State of the art and future prospects.
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Fernicola Agostino, Parmeggiani Domenico, Crocetto Felice, Shafeea Murtaja Satea, Cece Alessio, Calogero Armando, Cicatiello Annunziata Gaetana, Benassai Giacomo, Quarto Gennaro, Santangelo Michele
Аннотация
INTRODUCTION: Esophageal achalasia is a rare motility disorder characterized by impaired lower esophageal sphincter relaxation and absent peristalsis. Diagnostic tools such as high-resolution manometry (HRM) and functional lumen imaging probe (FLIP) have improved disease recognition; however, interpretation remains complex and highly operator dependent. Artificial intelligence (AI) has emerged as a promising approach to automate data analysis and enhance diagnostic accuracy, but its specific role in achalasia is not yet clearly defined.
CONTENT: A narrative review was conducted using PubMed, Scopus, and Web of Science, searching for studies published up to June 2025 that investigated AI applications in esophageal motility disorders, with particular attention to achalasia. Search terms included "artificial intelligence," "machine learning," "achalasia," "esophageal motility," and "high-resolution manometry." Although no prospective or interventional studies directly evaluating AI in achalasia were identified, several retrospective proof-of-concept studies applied AI algorithms to HRM and FLIP data. These studies demonstrated the feasibility of automated classification of esophageal motility disorders, with high accuracy in differentiating motility subtypes potentially applicable to achalasia. Exploratory research on AI-assisted imaging and outcome prediction also showed encouraging results.
SUMMARY: Current evidence suggests that AI-based models can accurately analyze complex esophageal motility data and reduce interobserver variability. While direct clinical evidence in achalasia remains limited, existing studies provide a solid methodological foundation for AI-assisted diagnosis, classification, and clinical decision support in this condition.
OUTLOOK: Future research should focus on prospective validation, multicenter data collection, and multimodal integration of clinical, physiologic, and imaging data. With targeted development and ethical governance, AI has the potential to enhance diagnostic precision, support personalized treatment strategies, and advance precision motility care in patients with achalasia.
CONTENT: A narrative review was conducted using PubMed, Scopus, and Web of Science, searching for studies published up to June 2025 that investigated AI applications in esophageal motility disorders, with particular attention to achalasia. Search terms included "artificial intelligence," "machine learning," "achalasia," "esophageal motility," and "high-resolution manometry." Although no prospective or interventional studies directly evaluating AI in achalasia were identified, several retrospective proof-of-concept studies applied AI algorithms to HRM and FLIP data. These studies demonstrated the feasibility of automated classification of esophageal motility disorders, with high accuracy in differentiating motility subtypes potentially applicable to achalasia. Exploratory research on AI-assisted imaging and outcome prediction also showed encouraging results.
SUMMARY: Current evidence suggests that AI-based models can accurately analyze complex esophageal motility data and reduce interobserver variability. While direct clinical evidence in achalasia remains limited, existing studies provide a solid methodological foundation for AI-assisted diagnosis, classification, and clinical decision support in this condition.
OUTLOOK: Future research should focus on prospective validation, multicenter data collection, and multimodal integration of clinical, physiologic, and imaging data. With targeted development and ethical governance, AI has the potential to enhance diagnostic precision, support personalized treatment strategies, and advance precision motility care in patients with achalasia.