AI-enabled workflow for automated classification and analysis of feto-placental Doppler images
The developed models proved to be highly accurate in classifying Doppler views and extracting essential measurements from Doppler images. The integration of this AI-enabled workflow holds significant promise in reducing the manual workload and enhancing the efficiency of feto-placental Doppler image analysis, even for non-trained readers.
Authors
- Lea Testa
- Babar Hasan (ORCID: https://orcid.org/0000-0002-9141-7578)
- Gabriel Bernardino (ORCID: https://orcid.org/0000-0001-8741-2566)
- R. Castellani (ORCID: https://orcid.org/0000-0002-8386-4167)
- F. Crispi (ORCID: https://orcid.org/0000-0002-7422-5240)
- Devyani Chowdhury (ORCID: https://orcid.org/0000-0003-3380-3929)
- Zahra Hoodbhoy (ORCID: https://orcid.org/0000-0002-0439-8293)
- Ainhoa M. Aguado (ORCID: https://orcid.org/0000-0003-3281-4298)
- Shazia Mohsin (ORCID: https://orcid.org/0000-0003-1003-4848)
- Bart Bijnens (ORCID: https://orcid.org/0000-0003-3130-6937)
- Guillermo Jiménez-Pérez (ORCID: https://orcid.org/0000-0002-2281-107X)
- Sergio Sánchez Martínez (ORCID: https://orcid.org/0000-0002-6382-8142)
- Josa Prats-Valero
Institutions
- Aga Khan University (PK)
- Institució Catalana de Recerca i Estudis Avançats (ES)
- Clinic for Special Children (US)
- Universitat Pompeu Fabra (ES)
- Sindh Institute of Urology and Transplantation (PK)
- Centre for Biomedical Network Research on Rare Diseases (ES)
- Consorci Institut D'Investigacions Biomediques August Pi I Sunyer (ES)
- Universitat de Barcelona (ES)
Publication Details
- Journal
- Frontiers in Digital Health
- Published
- 2024-10-16
- DOI
- https://doi.org/10.3389/fdgth.2024.1455767
- Citations
- 5
- Primary Topic
- Fetal and Pediatric Neurological Disorders
- Type
- article
- Field-Weighted Citation Impact
- 5.55