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Enhancing Chicago Classification diagnoses with functional lumen imaging probe-mechanics (FLIP-MECH)
Sourav Halder1, Jun Yamasaki2, Xinyi Liu3
1Kenneth C. Griffin Esophageal Center of Northwestern Medicine, Division of Gastroenterology and Hepatology, Department of Medicine, Feinberg School of Medicine, Northwestern University, Chicago, Illinois, USA.
This study introduces a novel deep learning approach to combine esophageal manometry and imaging probe data, improving diagnosis of motility disorders by analyzing "mechanical health". The method effectively segregates patients based on esophagogastric junction function.
Area of Science:
- Gastroenterology
- Medical Imaging
- Computational Biology
Background:
- Esophageal motility disorders lack a unified diagnostic approach combining high-resolution manometry (HRM) and functional lumen imaging probe (FLIP) data.
- Existing diagnostic metrics for these modalities are not systematically integrated.
- A need exists for a method to bridge the diagnostic gap between HRM and FLIP.
Purpose of the Study:
- To develop a formal approach integrating HRM and FLIP measurements using deep learning and biomechanics.
- To create improved diagnostic metrics for esophageal motility disorders.
- To establish a systematic method for analyzing esophageal mechanical health.
Main Methods:
- Analyzed the
Main Results:
- Subjects formed distinct clusters in the virtual disease landscape (VDL) based on esophageal dysfunction type and severity.
- The VDL effectively separated subjects into normal esophagogastric junction (EGJ) opening and EGJ obstruction groups.
- HRM and FLIP diagnoses showed strong correlation within these identified groups.
Conclusions:
- Mechanics-based parameters derived from FLIP measurements accurately estimate esophageal health.
- The 3-D VDL effectively segregates subjects, aligning well with motility diagnoses from HRM and FLIP.
- This approach offers a promising method for synergistic analysis of esophageal function.
