Digestive motility · FLIP panometry
From complex signals to clearer clinical decisions.
AI-based identification and classification of esophageal motility patterns during Functional Lumen Imaging Probe panometry examinations.
FLIP / ESOPHAGEAL RESPONSE ANALYSIS ACTIVE
01 · Intended use
AI assistance that begins after signal acquisition.
DigestiFlow AI® automatically identifies and classifies esophageal motility patterns during Functional Lumen Imaging Probe panometry examinations.
It turns an acquired physiological signal into structured information for review—without interrupting the clinical procedure.
AI
The clinical challenge
Variability can obscure the pattern.
FLIP panometry can involve high interobserver variability and a lack of standardized methodology for procedure execution and pattern classification.
“The goal is not more data. It is a clearer path from signal to decision.”
02
How it works
One continuous path from examination to report.
The interface follows the same logic as the technology: information progressively resolves as it moves through the analysis.
Signal acquisition
FLIP panometry examination data is captured for post-procedural review.
Pattern analysis
The AI reads the pressure and geometry signals across the full examination.
Segmentation
Relevant motility patterns are isolated from complex physiological variability.
Classification
Patterns are automatically identified and classified with consistent criteria.
Structured report
Findings are translated into a standardized AI-driven diagnostic report.
AI capabilities
Designed to make interpretation more consistent.
>90%
Diagnostic accuracy
Automatic identification
Detects and classifies esophageal motility patterns from FLIP panometry examinations.
Post-procedural analysis
Supports careful review without interrupting the procedure or acquisition workflow.
>90% diagnostic accuracy
Validated performance designed to support more confident pattern interpretation.
Standardized reporting
Turns complex signal data into a clear and repeatable diagnostic output.
Device interoperability
Designed to integrate across devices and existing clinical environments.
03
Scientific validation
Developed around clinical evidence.
Artificial Intelligence and FLIP Panometry—Automated Classification of Esophageal Motility Patterns
Mascarenhas M, et al. · Journal of Clinical Medicine
Read publication ↗Clinical benefits
Faster and more definitive clinical decisions.
01
Earlier and more accurate identification of esophageal motility patterns
↗02
Consistent methodology for classification and retrospective review
↗03
Fewer unnecessary procedures through clearer diagnostic information
↗04
Greater workflow efficiency with standardized AI-assisted reporting
↗DigestiFlow AI®
Bring clarity to esophageal motility analysis.
Talk to our team ↗The products and technologies showcased are part of our advanced research and development pipeline and may be pending regulatory approval in your market.