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Mascarenhas, M., Mendes, F., Mota, J., Ribeiro, T., Cardoso, P., Martins, M....Santander, C. (2025). Artificial intelligence as a transforming factor in motility disorders–automatic detection of motility patterns in high-resolution anorectal manometry. Scientific Reports. 15 (1)

M. Mascarenhas et al.,  "Artificial intelligence as a transforming factor in motility disorders–automatic detection of motility patterns in high-resolution anorectal manometry", in Scientific Reports, vol. 15, no. 1, 2025

@article{mascarenhas2025_1765919731664,
	author = "Mascarenhas, M. and Mendes, F. and Mota, J. and Ribeiro, T. and Cardoso, P. and Martins, M. and Almeida, M. J. and Cordeiro, J. R. and Ferreira, J. and Macedo, G. and Santander, C.",
	title = "Artificial intelligence as a transforming factor in motility disorders–automatic detection of motility patterns in high-resolution anorectal manometry",
	journal = "Scientific Reports",
	year = "2025",
	volume = "15",
	number = "1",
	doi = "10.1038/s41598-024-83768-8",
	url = "https://www.nature.com/srep/"
}

TY  - JOUR
TI  - Artificial intelligence as a transforming factor in motility disorders–automatic detection of motility patterns in high-resolution anorectal manometry
T2  - Scientific Reports
VL  - 15
IS  - 1
AU  - Mascarenhas, M.
AU  - Mendes, F.
AU  - Mota, J.
AU  - Ribeiro, T.
AU  - Cardoso, P.
AU  - Martins, M.
AU  - Almeida, M. J.
AU  - Cordeiro, J. R.
AU  - Ferreira, J.
AU  - Macedo, G.
AU  - Santander, C.
PY  - 2025
SN  - 2045-2322
DO  - 10.1038/s41598-024-83768-8
UR  - https://www.nature.com/srep/
AB  - High-resolution anorectal manometry (HR-ARM) is the gold standard for anorectal functional disorders’ evaluation, despite being limited by its accessibility and complex data analysis. The London Protocol and Classification were developed to standardize anorectal motility patterns classification. This proof-of-concept study aims to develop and validate an artificial intelligence model for identification and differentiation of disorders of anal tone and contractility in HR-ARM. A dataset of 701 HR-ARM exams from a tertiary center, classified according to London Classification, was used to develop and test multiple machine learning (ML) algorithms. The exams were divided in a training and testing dataset with a 80/20% ratio. The testing dataset was used for models’ evaluation through its accuracy, sensitivity, specificity, positive and negative predictive values and area under the receiving-operating characteristic curve. LGBM Classifier had the best performance, with an accuracy of 87.0% for identifying disorders of anal tone and contractility. Different ML models excelled in distinguishing specific disorders of anal tone and contractility, with accuracy over 90.0%. This is the first worldwide study proving the accuracy of a ML model for differentiation of motility patterns in HR-ARM, demonstrating the value of artificial intelligence models in optimizing HR-ARM availability while reducing interobserver variability and increasing accuracy.
ER  -