TY - JOUR
T1 - Artificial Intelligence Tools for Improving Manometric Diagnosis of Esophageal Dysmotility
AU - Fass, Ofer
AU - Rogers, Benjamin D.
AU - Gyawali, C. Prakash
N1 - Publisher Copyright:
© The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2024.
PY - 2024/4
Y1 - 2024/4
N2 - 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. Summary: 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.
AB - 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. Summary: 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.
KW - Artificial intelligence
KW - Functional lumen imaging probe
KW - High resolution manometry
KW - Machine learning
UR - http://www.scopus.com/inward/record.url?scp=85184875734&partnerID=8YFLogxK
U2 - 10.1007/s11894-024-00921-z
DO - 10.1007/s11894-024-00921-z
M3 - Article
C2 - 38324172
AN - SCOPUS:85184875734
SN - 1522-8037
VL - 26
SP - 115
EP - 123
JO - Current gastroenterology reports
JF - Current gastroenterology reports
IS - 4
ER -