Deep Learning and Minimally Invasive Endoscopy: Panendoscopic Detection of Pleomorphic Lesions
We developed the first AI model for panendoscopic automatic detection of pleomorphic lesions in both CE and CCE from multiple brands, solving a critical interoperability technological challenge. Deep learning-based tools may change the landscape of minimally invasive capsule panendoscopy.
Authors
- Tiago Ribeiro
- Miguel Martins (ORCID: https://orcid.org/0000-0002-0484-4804)
- Guilherme Macedo (ORCID: https://orcid.org/0000-0002-9878-4901)
- João L. Afonso (ORCID: https://orcid.org/0000-0001-9195-1239)
- Patrícia Andrade (ORCID: https://orcid.org/0000-0003-1334-0947)
- Miguel Mascarenhas (ORCID: https://orcid.org/0000-0002-0340-0830)
- João Ferreira (ORCID: https://orcid.org/0000-0002-4384-8533)
- Francisco Mendes
- Hélder Cardoso
- Pedro Cardoso (ORCID: https://orcid.org/0000-0002-5892-9424)
Institutions
- Universidade do Porto (PT)
- Hospital de São João (PT)
- Artificial Intelligence in Medicine (Canada) (CA)
Publication Details
- Journal
- GE Portuguese Journal of Gastroenterology
- Published
- 2024-08-21
- DOI
- https://doi.org/10.1159/000539837
- Citations
- 3
- Primary Topic
- Gastrointestinal Bleeding Diagnosis and Treatment
- Type
- article
- Field-Weighted Citation Impact
- 1.39
Funders
- Nvidia