Evaluating the efficacy and diagnostic reliability of computer vision artificial intelligence algorithm in automating anatomical classification for second‐trimester fetal anomaly ultrasound scans: A prospective cohort study
BioticsAI-anatomyUNet-0.1-2022 demonstrated high diagnostic accuracy and improved workflow efficiency in anomaly scan interpretation. Its performance was robust across BMI levels, though caution is advised for complex planes such as 3VT and craniofacial views.
Authors
- Somayya M. Sadek (ORCID: https://orcid.org/0000-0003-3833-1369)
- Hytham Atia (ORCID: https://orcid.org/0000-0002-4013-9491)
- Hisham Elgammal (ORCID: https://orcid.org/0000-0003-3461-0563)
- Reda A. Ahmad (ORCID: https://orcid.org/0000-0002-8324-8778)
- Tamer M. Abdul Aziz
Institutions
- Zagazig University (EG)
- Armed Forces Hospital (SA)
- Alexandria University (EG)
Publication Details
- Journal
- International Journal of Gynecology & Obstetrics
- Published
- 2025-12-31
- DOI
- https://doi.org/10.1002/ijgo.70779
- Primary Topic
- Fetal and Pediatric Neurological Disorders
- Type
- article
- Field-Weighted Citation Impact
- 0.00