Artificial intelligence as a transforming factor in motility disorders–automatic detection of motility patterns in high-resolution anorectal manometry
Abstract 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 classifica...
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Nature Portfolio
2025-01-01
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| Online Access: | https://doi.org/10.1038/s41598-024-83768-8 |
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| author | Miguel Mascarenhas Francisco Mendes Joana Mota Tiago Ribeiro Pedro Cardoso Miguel Martins Maria João Almeida João Rala Cordeiro João Ferreira Guilherme Macedo Cecilio Santander |
| author_facet | Miguel Mascarenhas Francisco Mendes Joana Mota Tiago Ribeiro Pedro Cardoso Miguel Martins Maria João Almeida João Rala Cordeiro João Ferreira Guilherme Macedo Cecilio Santander |
| author_sort | Miguel Mascarenhas |
| collection | DOAJ |
| description | Abstract 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. |
| format | Article |
| id | doaj-art-db2d2f220935423394505488f5176659 |
| institution | Kabale University |
| issn | 2045-2322 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Scientific Reports |
| spelling | doaj-art-db2d2f220935423394505488f51766592025-01-19T12:21:09ZengNature PortfolioScientific Reports2045-23222025-01-011511810.1038/s41598-024-83768-8Artificial intelligence as a transforming factor in motility disorders–automatic detection of motility patterns in high-resolution anorectal manometryMiguel Mascarenhas0Francisco Mendes1Joana Mota2Tiago Ribeiro3Pedro Cardoso4Miguel Martins5Maria João Almeida6João Rala Cordeiro7João Ferreira8Guilherme Macedo9Cecilio Santander10Department of Gastroenterology, Precision Medicine Unit, Centro Hospitalar Universitário São JoãoDepartment of Gastroenterology, Precision Medicine Unit, Centro Hospitalar Universitário São JoãoDepartment of Gastroenterology, Precision Medicine Unit, Centro Hospitalar Universitário São JoãoDepartment of Gastroenterology, Precision Medicine Unit, Centro Hospitalar Universitário São JoãoDepartment of Gastroenterology, Precision Medicine Unit, Centro Hospitalar Universitário São JoãoDepartment of Gastroenterology, Precision Medicine Unit, Centro Hospitalar Universitário São JoãoDepartment of Gastroenterology, Precision Medicine Unit, Centro Hospitalar Universitário São JoãoDepartment of Information Science and Technology, University Institute of LisbonFaculty of Engineering of the University of PortoDepartment of Gastroenterology, Precision Medicine Unit, Centro Hospitalar Universitário São JoãoHospital Universitario La PrincesaAbstract 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.https://doi.org/10.1038/s41598-024-83768-8Anorectal disordersAnorectal manometryArtificial intelligenceGastroenterologyMachine learning |
| spellingShingle | Miguel Mascarenhas Francisco Mendes Joana Mota Tiago Ribeiro Pedro Cardoso Miguel Martins Maria João Almeida João Rala Cordeiro João Ferreira Guilherme Macedo Cecilio Santander Artificial intelligence as a transforming factor in motility disorders–automatic detection of motility patterns in high-resolution anorectal manometry Scientific Reports Anorectal disorders Anorectal manometry Artificial intelligence Gastroenterology Machine learning |
| title | Artificial intelligence as a transforming factor in motility disorders–automatic detection of motility patterns in high-resolution anorectal manometry |
| title_full | Artificial intelligence as a transforming factor in motility disorders–automatic detection of motility patterns in high-resolution anorectal manometry |
| title_fullStr | Artificial intelligence as a transforming factor in motility disorders–automatic detection of motility patterns in high-resolution anorectal manometry |
| title_full_unstemmed | Artificial intelligence as a transforming factor in motility disorders–automatic detection of motility patterns in high-resolution anorectal manometry |
| title_short | Artificial intelligence as a transforming factor in motility disorders–automatic detection of motility patterns in high-resolution anorectal manometry |
| title_sort | artificial intelligence as a transforming factor in motility disorders automatic detection of motility patterns in high resolution anorectal manometry |
| topic | Anorectal disorders Anorectal manometry Artificial intelligence Gastroenterology Machine learning |
| url | https://doi.org/10.1038/s41598-024-83768-8 |
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