Estimating Probability for Esophageal Obstruction: A Diagnostic Decision Support Tool Applying Machine Learning to Functional Lumen Imaging Probe Panometry

Discriminating "impaired" from "disordered" contractile response on FLIP panometry by utilizing pressure measurement

BACKGROUND

Functional lumen imaging probe (FLIP) panometry evaluates esophageal motility, including the contractile response to distension, that is, secondary peristalsis. Impaired/disordered contractile response (IDCR) is an abnormal, but nonspecific contractile response that can represent either hypomotility or spastic motor disorders on high-resolution manometry (HRM). We hypothesized that FLIP pressure could be incorporated to clarify IDCR and aimed to determine its utility in a cohort of symptomatic esophageal motility patients.

METHODS

173 adult patients that had IDCR on FLIP panometry and HRM with a conclusive Chicago Classification v4.0 (CCv4.0) diagnosis were included and analyzed as development (n = 118) and validation (n = 55) cohorts. FLIP pressure values were assessed for prediction of either hypomotility or spasm, defined on HRM/CCv4.0.

KEY RESULTS

HRM/CCv4.0 diagnoses were normal motility in 48 patients (28%), "hypomotility" (ineffective esophageal motility, absent contractility, or Type I or II achalasia) in 89 (51%), and "spasm" (Type III achalasia, distal esophageal spasm, or hypercontractile esophagus) in 36 (21%). The pressure at esophagogastric junction-distensibility index (DI) (60 mL) was lower in hypomotility (median [interquartile range] 34 [28-42] mmHg) than in spasm (49 [40-62] mmHg; p < 0.001) and had an area under the receiver operating characteristic curve of 0.80 (95% CI 0.73-0.88) for hypomotility and 0.76 (0.69-0.83) for spasm. For "spasm" on HRM, a threshold FLIP pressure of >35 mmHg provided 90% sensitivity (47% specificity) while >55 mmHg provided 93% specificity (40% sensitivity).

CONCLUSION & INFERENCES

Pressure on FLIP panometry can help clarify the significance of IDCR, with low-pressure IDCR associated with hypomotility and high-pressure IDCR suggestive of spastic motor disorders.

Artificial Intelligence Tools for Improving Manometric Diagnosis of Esophageal Dysmotility

PURPOSE OF REVIEW

Artificial intelligence (AI) is a broad term that pertains to a computer's ability to mimic and sometimes surpass human intelligence in interpretation of large datasets. The adoption of AI in gastrointestinal motility has been slower compared to other areas such as polyp detection and interpretation of histopathology.

RECENT FINDINGS

Within esophageal physiologic testing, AI can automate interpretation of image-based tests, especially high resolution manometry (HRM) and functional luminal imaging probe (FLIP) studies. Basic tasks such as identification of landmarks, determining adequacy of the HRM study and identification from achalasia from non-achalasia patterns are achieved with good accuracy. However, existing AI systems compare AI interpretation to expert analysis rather than to clinical outcome from management based on AI diagnosis. The use of AI methods is much less advanced within the field of ambulatory reflux monitoring, where challenges exist in assimilation of data from multiple impedance and pH channels. There remains potential for replication of the AI successes within esophageal physiologic testing to HRM of the anorectum, and to innovative and novel methods of evaluating gastric electrical activity and motor function. The use of AI has tremendous potential to improve detection of dysmotility within the esophagus using esophageal physiologic testing, as well as in other regions of the gastrointestinal tract. Eventually, integration of patient presentation, demographics and alternate test results to individual motility test interpretation will improve diagnostic precision and prognostication using AI tools.