Autonomous fetal morphology scan: deep learning + clustering merger – the second pair of eyes behind the doctor

• Published in: BMC medical informatics and decision making Vol. 24; no. 1; pp. 102 - 14
• Main Author: Belciug, Smaranda
• Format: Journal Article
• Language: English
• Published: London BioMed Central 19.04.2024; BioMed Central Ltd; Springer Nature B.V; BMC
• Subjects: Abdomen; Accuracy; Algorithms; Anomalies; Artificial intelligence; Birth defects; Children; Clustering; Congenital anomalies; Congenital defects; Congenital diseases; Datasets; Decision support systems; Deep learning; Design of experiments; Fetal morphology; Fetuses; Genetic aspects; Health Informatics; Information Systems and Communication Service; Labels; Machine learning; Management of Computing and Information Systems; Medicine; Medicine & Public Health; Methods; Morbidity; Morphology; Neural networks; Organs; Pattern recognition; Prenatal diagnosis; Prevention; Risk factors; Segmentation; Statistical analysis; Ultrasonic imaging; Romania; Deep learning; Segmentation; Fetal morphology; Clustering
• ISSN: 1472-6947; 1472-6947
• DOI: 10.1186/s12911-024-02505-3

The main cause of fetal death, of infant morbidity or mortality during childhood years is attributed to congenital anomalies. They can be detected through a fetal morphology scan. An experienced sonographer (with more than 2000 performed scans) has the detection rate of congenital anomalies around 52%. The rates go down in the case of a junior sonographer, that has the detection rate of 32.5%. One viable solution to improve these performances is to use Artificial Intelligence. The first step in a fetal morphology scan is represented by the differentiation process between the view planes of the fetus, followed by a segmentation of the internal organs in each view plane. This study presents an Artificial Intelligence empowered decision support system that can label anatomical organs using a merger between deep learning and clustering techniques, followed by an organ segmentation with YOLO8. Our framework was tested on a fetal morphology image dataset that regards the fetal abdomen. The experimental results show that the system can correctly label the view plane and the corresponding organs on real-time ultrasound movies. Trial registration The study is registered under the name “Pattern recognition and Anomaly Detection in fetal morphology using Deep Learning and Statistical Learning (PARADISE)”, project number 101PCE/2022, project code PN-III-P4-PCE-2021–0057. Trial registration: ClinicalTrials.gov, unique identifying number NCT05738954, date of registration 02.11.2023.