Publicação

Artificial intelligence driven diagnosis of motility patterns in high-resolution esophageal manometry: A multicentric multidevice study

Detalhes bibliográficos
Resumo:	INTRODUCTION: Esophageal motility disorders (EMDs) are common in clinical practice, with a high symptomatic burden and significant impact on the patients' quality of life. High-resolution esophageal manometry (HREM) is the gold standard for the evaluation of functional esophageal disorders. The Chicago Classification offers a standardized approach to HREM. However, HREM remains a complex procedure, both in data analysis and in accessibility. This study aimed to develop and validate machine learning (ML) models to detect EMDs according to the Chicago Classification. METHODS: We retrospectively analyzed 618 HREM examinations from 3 centers (Spain and the United States) using 2 recording systems. Labels were assigned by expert consensus as either disorder present or absent for 2 categories: esophagogastric junction outflow disorders and peristalsis disorders. Several ML models were trained and evaluated. ML classifiers were developed using an 80/20 patient-level stratified split for training/validation and testing. Model selection was guided by internal evaluation through repeated 10-fold cross-validation. Model performance was assessed by accuracy and area under the receiver-operating characteristic curve (AUC-ROC). RESULTS: The GradientBoostingClassifier model outperformed the remaining ML models with an accuracy of 0.942 ± 0.015 and an AUC-ROC of 0.921 ± 0.041 for identifying disorders of esophagogastric junction outflow. The xGBClassifier model detected disorders of peristalsis with an accuracy of 0.809 ± 0.029 and an AUC-ROC of 0.871 ± 0.027. Performance was consistent across repeated validations, demonstrating model robustness and generalization. DISCUSSION: This multicenter, multidevice study demonstrates that ML models can accurately detect EMDs in HREM. Artificial intelligence-driven HREM may improve diagnosis by standardizing interpretation and reducing interobserver variability. Abstract
Autores principais:	Mascarenhas, M.
Outros Autores:	Mota, J.; Cordeiro, J. R.; Mendes, F.; Martins, M.; Cardoso, P.; Almeida, M. J.; Pinto da Costa, A.; Hajra Martinez, I.; Matallana Royo, V.; Niland, B.; Di Palma, J.; Ferreira, J.; Macedo, G.; Santander, C.
Assunto:	Artificial intelligence High-resolution esophageal manometry Machine learning Esophageal motility disorders
Ano:	2025
País:	Portugal
Tipo de documento:	artigo
Tipo de acesso:	acesso aberto
Instituição associada:	ISCTE
Idioma:	inglês
Origem:	Repositório ISCTE

Descrição
Resumo:	INTRODUCTION: Esophageal motility disorders (EMDs) are common in clinical practice, with a high symptomatic burden and significant impact on the patients' quality of life. High-resolution esophageal manometry (HREM) is the gold standard for the evaluation of functional esophageal disorders. The Chicago Classification offers a standardized approach to HREM. However, HREM remains a complex procedure, both in data analysis and in accessibility. This study aimed to develop and validate machine learning (ML) models to detect EMDs according to the Chicago Classification. METHODS: We retrospectively analyzed 618 HREM examinations from 3 centers (Spain and the United States) using 2 recording systems. Labels were assigned by expert consensus as either disorder present or absent for 2 categories: esophagogastric junction outflow disorders and peristalsis disorders. Several ML models were trained and evaluated. ML classifiers were developed using an 80/20 patient-level stratified split for training/validation and testing. Model selection was guided by internal evaluation through repeated 10-fold cross-validation. Model performance was assessed by accuracy and area under the receiver-operating characteristic curve (AUC-ROC). RESULTS: The GradientBoostingClassifier model outperformed the remaining ML models with an accuracy of 0.942 ± 0.015 and an AUC-ROC of 0.921 ± 0.041 for identifying disorders of esophagogastric junction outflow. The xGBClassifier model detected disorders of peristalsis with an accuracy of 0.809 ± 0.029 and an AUC-ROC of 0.871 ± 0.027. Performance was consistent across repeated validations, demonstrating model robustness and generalization. DISCUSSION: This multicenter, multidevice study demonstrates that ML models can accurately detect EMDs in HREM. Artificial intelligence-driven HREM may improve diagnosis by standardizing interpretation and reducing interobserver variability. Abstract