AIMC Topic: Manometry

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AI in Esophageal Motility Disorders: Systematic Review of High-Resolution Manometry Studies.

Journal of medical Internet research
BACKGROUND: High-resolution esophageal manometry (HRM) is essential for diagnosing esophageal motility disorders, affecting 10%-15% of patients with dysphagia. Current interpretation via the Chicago Classification remains challenging, with interobser...

Artificial Intelligence Model for Time Series Classification: Prediction of Delayed Balloon Expulsion Test Using High-Resolution Anorectal Manometry Data and Time-Series Integrated Pressurized Volume.

Neurogastroenterology and motility
BACKGROUND: We previously demonstrated the novel concept of using the integrated pressurized volume (IPV) with high-resolution anorectal manometry (HRAM) and found that it was predictive of delayed balloon expulsion (BE) test results. However, previo...

Esophageal Intelligence: Implementing Artificial Intelligence Into the Diagnostics of Esophageal Motility and Impedance pH Monitoring.

Neurogastroenterology and motility
Esophageal motility disorders (EMDs) encompass a range of functional abnormalities, including achalasia, ineffective esophageal motility (IEM), esophagogastric junction outflow obstruction (EGJOO), and distal esophageal spasm (DES). Diagnostic modali...

Artificial intelligence as a transforming factor in motility disorders-automatic detection of motility patterns in high-resolution anorectal manometry.

Scientific reports
High-resolution anorectal manometry (HR-ARM) is the gold standard for anorectal functional disorders' evaluation, despite being limited by its accessibility and complex data analysis. The London Protocol and Classification were developed to standardi...

Comparison of measurement of integrated relaxation pressure by esophageal manometry with analysis of swallowing sounds with artificial intelligence in patients with achalasia.

Neurogastroenterology and motility
BACKGROUND: Esophageal motility disorders are mainly evaluated with high-resolution manometry (HRM) which is a time-consuming and uncomfortable procedure with potential adverse events. Acoustic characterization of the swallowing has the potential to ...

Gemini-Assisted Deep Learning Classification Model for Automated Diagnosis of High-Resolution Esophageal Manometry Images.

Medicina (Kaunas, Lithuania)
To develop a deep learning model for esophageal motility disorder diagnosis using high-resolution manometry images with the aid of Gemini. Gemini assisted in developing this model by aiding in code writing, preprocessing, model optimization, and tr...

A new methodology for determining the central pressure waveform from peripheral measurement using Fourier-based machine learning.

Artificial intelligence in medicine
Radial applanation tonometry is a well-established technique for hemodynamic monitoring and is becoming popular in affordable non-invasive wearable healthcare electronics. To assess the central aortic pressure using radial-based measurements, there i...

Achalasia phenotypes and prediction of peroral endoscopic myotomy outcomes using machine learning.

Digestive endoscopy : official journal of the Japan Gastroenterological Endoscopy Society
OBJECTIVES: High-resolution manometry (HRM) and esophagography are used for achalasia diagnosis; however, achalasia phenotypes combining esophageal motility and morphology are unknown. Moreover, predicting treatment outcomes of peroral endoscopic myo...

Artificial Intelligence and Anorectal Manometry: Automatic Detection and Differentiation of Anorectal Motility Patterns-A Proof-of-Concept Study.

Clinical and translational gastroenterology
INTRODUCTION: Anorectal manometry (ARM) is the gold standard for the evaluation of anorectal functional disorders, prevalent in the population. Nevertheless, the accessibility to this examination is limited, and the complexity of data analysis and re...

Video-Based Deep Learning to Detect Dyssynergic Defecation with 3D High-Definition Anorectal Manometry.

Digestive diseases and sciences
BACKGROUND: We developed a deep learning algorithm to evaluate defecatory patterns to identify dyssynergic defecation using 3-dimensional high definition anal manometry (3D-HDAM).