We found a match
Your institution may have access to this item. Find your institution then sign in to continue.
- Title
Artificial Intelligence in Obstetric Anomaly Scan: Heart and Brain.
- Authors
Enache, Iuliana-Alina; Iovoaica-Rămescu, Cătălina; Ciobanu, Ștefan Gabriel; Berbecaru, Elena Iuliana Anamaria; Vochin, Andreea; Băluță, Ionuț Daniel; Istrate-Ofițeru, Anca Maria; Comănescu, Cristina Maria; Nagy, Rodica Daniela; Iliescu, Dominic Gabriel
- Abstract
Background: The ultrasound scan represents the first tool that obstetricians use in fetal evaluation, but sometimes, it can be limited by mobility or fetal position, excessive thickness of the maternal abdominal wall, or the presence of post-surgical scars on the maternal abdominal wall. Artificial intelligence (AI) has already been effectively used to measure biometric parameters, automatically recognize standard planes of fetal ultrasound evaluation, and for disease diagnosis, which helps conventional imaging methods. The usage of information, ultrasound scan images, and a machine learning program create an algorithm capable of assisting healthcare providers by reducing the workload, reducing the duration of the examination, and increasing the correct diagnosis capability. The recent remarkable expansion in the use of electronic medical records and diagnostic imaging coincides with the enormous success of machine learning algorithms in image identification tasks. Objectives: We aim to review the most relevant studies based on deep learning in ultrasound anomaly scan evaluation of the most complex fetal systems (heart and brain), which enclose the most frequent anomalies.
- Subjects
ARTIFICIAL intelligence; DEEP learning; MACHINE learning; FETAL heart; DIAGNOSTIC imaging; FETAL ultrasonic imaging; MEDICAL personnel
- Publication
Life (2075-1729), 2024, Vol 14, Issue 2, p166
- ISSN
2075-1729
- Publication type
Article
- DOI
10.3390/life14020166