Artificial Intelligence Driven Diagnosis of Motility Patterns in High-Resolution Esophageal Manometry: A Multicentric Multidevice Study
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.
Authors
- João Ferreira (ORCID: https://orcid.org/0000-0003-4310-2915)
- Ignacio Martínez Ruiz (ORCID: https://orcid.org/0000-0002-2887-2105)
- Cecílio Santander (ORCID: https://orcid.org/0000-0001-5492-2535)
- Maria João Almeida (ORCID: https://orcid.org/0000-0003-4061-8924)
- João V. Cordeiro (ORCID: https://orcid.org/0000-0003-4605-1615)
- Miguel A. Martins (ORCID: https://orcid.org/0000-0002-7294-6204)
- Benjamin Niland (ORCID: https://orcid.org/0009-0007-4456-9820)
- Miguel Mascarenhas (ORCID: https://orcid.org/0000-0002-0340-0830)
- Francisco Mendes (ORCID: https://orcid.org/0000-0002-5890-7049)
- Jack Di Palma
- Virginia Matallana Royo
- Guilherme Macedo (ORCID: https://orcid.org/0000-0002-9387-9872)
- Joana Mota (ORCID: https://orcid.org/0000-0002-8835-852X)
- Antonio Pinto da Costa
- Pedro Cardoso (ORCID: https://orcid.org/0000-0002-5892-9424)
Institutions
- Universidade do Porto (PT)
- Hospital de São João (PT)
- Iscte – Instituto Universitário de Lisboa (PT)
- Hospital Universitario Puerta de Hierro Majadahonda (ES)
- Instituto de Telecomunicações (PT)
- Hospital Universitario de La Princesa (ES)
- University of South Alabama (US)
Publication Details
- Journal
- Clinical and Translational Gastroenterology
- Published
- 2025-10-23
- DOI
- https://doi.org/10.14309/ctg.0000000000000941
- Citations
- 1
- Primary Topic
- Gastroesophageal reflux and treatments
- Type
- article
- Field-Weighted Citation Impact
- 1.81