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Mascarenhas, M., Mendes, F., Cordeiro, J. R., Mota, J., Martins, M., Almeida, M. J....Santander, C. (2026). Artificial intelligence and FLIP panometry: Automated classification of esophageal motility patterns. Journal of Clinical Medicine. 15 (1)

M. Mascarenhas et al.,  "Artificial intelligence and FLIP panometry: Automated classification of esophageal motility patterns", in Journal of Clinical Medicine, vol. 15, no. 1, 2026

@article{mascarenhas2026_1784169883960,
	author = "Mascarenhas, M. and Mendes, F. and Cordeiro, J. R. and Mota, J. and Martins, M. and Almeida, M. J. and Araujo, C. and Frias, J. and Cardoso, P. and El Hajra, I. and Pinto da Costa, A. and Matallana, V. and Ciriza de Los Rios, C. and Ferreira, J. and Saraiva, M. M. and Macedo, G. and Niland, B. and Santander, C.",
	title = "Artificial intelligence and FLIP panometry: Automated classification of esophageal motility patterns",
	journal = "Journal of Clinical Medicine",
	year = "2026",
	volume = "15",
	number = "1",
	doi = "10.3390/jcm15010401",
	url = "https://www.mdpi.com/journal/jcm"
}

TY  - JOUR
TI  - Artificial intelligence and FLIP panometry: Automated classification of esophageal motility patterns
T2  - Journal of Clinical Medicine
VL  - 15
IS  - 1
AU  - Mascarenhas, M.
AU  - Mendes, F.
AU  - Cordeiro, J. R.
AU  - Mota, J.
AU  - Martins, M.
AU  - Almeida, M. J.
AU  - Araujo, C.
AU  - Frias, J.
AU  - Cardoso, P.
AU  - El Hajra, I.
AU  - Pinto da Costa, A.
AU  - Matallana, V.
AU  - Ciriza de Los Rios, C.
AU  - Ferreira, J.
AU  - Saraiva, M. M.
AU  - Macedo, G.
AU  - Niland, B.
AU  - Santander, C.
PY  - 2026
SN  - 2077-0383
DO  - 10.3390/jcm15010401
UR  - https://www.mdpi.com/journal/jcm
AB  - Functional lumen imaging probe (FLIP) panometry allows real-time assessment of the esophagogastric junction opening and esophageal body contractile activity during an endoscopic procedure. Despite the development of the Dallas Consensus, FLIP panometry analysis remains complex. Artificial intelligence (AI) models have proven their benefit in high-resolution esophageal manometry; however, data on their role in FLIP panometry are scarce. This study aims to develop an AI model for automatic classification of motility patterns during a FLIP panometry exam. Methods: A total of 105 exams from five centers from both the European and American continents were included. Several machine learning models were trained and evaluated for detection of FLIP panometry patterns. Each exam was classified with an expert consensus-based decision according to the Dallas Consensus, with division into a training and testing dataset in a patient-split design. Models’ performance was evaluated through their accuracy and area under the receiver-operating characteristic curve (AUC-ROC). Results: Pathological planimetry patterns were identified by an AdaBoost Classifier with 84.9% accuracy and a mean AUC-ROC of 0.92. Random Forest identified disorders of the esophagogastric junction opening with 86.7% accuracy and an AUC-ROC of 0.973. The Gradient Boosting Classifier identified disorders of the contractile response with 86.0% accuracy and an AUC-ROC of 0.933. Conclusions: In this study, integrating exams with different probe sizes and demographic contexts, a machine learning model accurately classified FLIP panometry exams according to the Dallas Consensus. AI-driven FLIP panometry could revolutionize the approach to this exam during an endoscopic procedure, optimizing exam accuracy, standardization, and accessibility, and transforming patient management.
ER  -