Enhancing Chicago Classification diagnoses with functional lumen imaging probe—mechanics (FLIP‐MECH)

• Published in: Neurogastroenterology and motility Vol. 36; no. 8; pp. e14841 - n/a
• Main Authors: Halder, Sourav, Yamasaki, Jun, Liu, Xinyi, Carlson, Dustin A., Kou, Wenjun, Kahrilas, Peter J., Pandolfino, John E., Patankar, Neelesh A.
• Format: Journal Article
• Language: English
• Published: England Wiley Subscription Services, Inc 01.08.2024
• Subjects: achalasia; Adult; Deep Learning; dysphagia; esophageal biomechanics; esophageal motility disorders; Esophageal Motility Disorders - classification; Esophageal Motility Disorders - diagnosis; Esophageal Motility Disorders - physiopathology; Esophagus; Esophagus - diagnostic imaging; Esophagus - physiopathology; Female; generative AI; Humans; Male; Manometry - methods; Mechanics; Middle Aged; Motility; Synergism; dysphagia; achalasia; generative AI; esophageal motility disorders; esophageal biomechanics
• ISSN: 1350-1925; 1365-2982; 1365-2982
• DOI: 10.1111/nmo.14841

Background Esophageal 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. Methods The “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. Results Subjects 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. Conclusion Mechanics‐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. Esophageal motility disorders present a diagnostic challenge, as current methods such as high‐resolution manometry (HRM) and the functional lumen imaging probe (FLIP) operate independently without a unified strategy to enhance diagnostic accuracy. This work provides a structured methodology leveraging deep learning and biomechanics to enhance the HRM‐based Chicago Classification using FLIP measurements.