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Title: Enhancing Chicago Classification diagnoses with functional lumen imaging probe—mechanics (FLIP‐MECH)
Abstract BackgroundEsophageal motility disorders can be diagnosed by either high‐resolution manometry (HRM) or the functional lumen imaging probe (FLIP) but there is no systematic approach to synergize the measurements of these modalities or to improve the diagnostic metrics that have been developed to analyze them. This work aimed to devise a formal approach to bridge the gap between diagnoses inferred from HRM and FLIP measurements using deep learning and mechanics. MethodsThe “mechanical health” of the esophagus was analyzed in 740 subjects including a spectrum of motility disorder patients and normal subjects. The mechanical health was quantified through a set of parameters including wall stiffness, active relaxation, and contraction pattern. These parameters were used by a variational autoencoder to generate a parameter space called virtual disease landscape (VDL). Finally, probabilities were assigned to each point (subject) on the VDL through linear discriminant analysis (LDA), which in turn was used to compare with FLIP and HRM diagnoses. ResultsSubjects clustered into different regions of the VDL with their location relative to each other (and normal) defined by the type and severity of dysfunction. The two major categories that separated best on the VDL were subjects with normal esophagogastric junction (EGJ) opening and those with EGJ obstruction. Both HRM and FLIP diagnoses correlated well within these two groups. ConclusionMechanics‐based parameters effectively estimated esophageal health using FLIP measurements to position subjects in a 3‐D VDL that segregated subjects in good alignment with motility diagnoses gleaned from HRM and FLIP studies.  more » « less
Award ID(s):
1931372 1450374
PAR ID:
10532584
Author(s) / Creator(s):
 ;  ;  ;  ;  ;  ;  ;  
Publisher / Repository:
Wiley-Blackwell
Date Published:
Journal Name:
Neurogastroenterology & Motility
Volume:
36
Issue:
8
ISSN:
1350-1925
Format(s):
Medium: X
Sponsoring Org:
National Science Foundation
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