Functional neurobehaviour of the human fetus: a comprehensive framework for prenatal assessment using 4D ultrasound and AI.
Authors
Affiliations (16)
Affiliations (16)
- Fetomaternal Division, Department of Obstetrics and Gynecology, Medical Faculty of Diponegoro University, Dr. Kariadi Hospital, Semarang, Indonesia.
- Fetomaternal Division, Women Health Center, Department of Obstetrics and Gynecology, Ekahospital BSD City, Tangerang, Banten, Indonesia.
- Fetomaternal Division, Department of Obstetrics and Gynecology, Medical Faculty of Sebelas Maret University, Dr. Moewardi Hospital, Surakarta, Indonesia.
- Fetomaternal Division, Department of Obstetrics and Gynecology, Faculty of Medicine, Hasanuddin University of Makassar, Makassar, Indonesia.
- Maternal-Fetal Medicine Division, Department of Obstetrics and Gynecology, Faculty of Medicine, Udayana University, Prof. Dr. I.G.N.G Ngoerah General Hospital, Bali, Indonesia.
- Maternal-Fetal Medicine Division, Department of Obstetrics and Gynecology, Faculty of Medicine, Airlangga University, Dr. Soetomo Hospital, Surabaya, Indonesia.
- Fetomaternal Division, Department of Obstetrics and Gynecology, Faculty of Medicine, Sumatera Utara University, H. Adam Malik General Hospital, Medan, North Sumatera, Indonesia.
- Fetomaternal Division, Department of Obstetrics and Gynecology, Faculty of Medicine, Maranatha Christian University, Bandung, West Java, Indonesia.
- Maternal-Fetal Medicine Division, Department of Obstetrics and Gynecology, Faculty of Medicine, Padjadjaran University, Hasan Sadikin General Hospital, Bandung, West Java, Indonesia.
- Maternal-Fetal Medicine Division, Department of Obstetrics and Gynecology, Faculty of Medicine Syiah Kuala University, Dr. Zainoel Abidin General Hospital, Aceh, Indonesia.
- Maternal-Fetal Medicine Division, Department of Obstetrics and Gynecology, Faculty of Medicine Sriwijaya University, Dr. Mohammad Hoesin General Hospital, Palembang, Indonesia.
- Department of Medicine, Faculty of Medicine, Diponegoro University, Semarang, Central Java, Indonesia.
- Department of Medicine, Undergraduate Program in Medical Science, Faculty of Medicine, Padjajaran University, Bandung, West Java, Indonesia.
- Undergraduate Program in Medical Science, Faculty of Medicine, Gadjah Mada University, Special Region of Yogyakarta, Yogyakarta, Indonesia.
- Department of Neonatology and Rare Diseases, Medical University of Warsaw, Warsaw, Poland.
- Department of Obstetrics and Gynecology, Medical School University of Zagreb, Zagreb, Croatia.
Abstract
To evaluate current evidence on prenatal neurobehavioural assessment using four-dimensional ultrasound (4D-US), structured scoring systems, and emerging artificial intelligence (AI) platforms, and to develop an integrated framework for early identification of neurodevelopmental vulnerability. A narrative review of the literature from 2000 to 2025 was conducted following PRISMA-guided organisational principles to enhance transparency. Studies were eligible if they used 3D/4D ultrasound to assess fetal neurobehaviour, including spontaneous motor activity, facial expressions, or behavioural transitions. Specific tools examined included the Kurjak Antenatal Neurodevelopmental Test (KANET), prenatal General Movements Assessment (GMA), and AI-assisted behavioural analysis systems. Methodological quality was appraised using the Newcastle-Ottawa Scale and the Joanna Briggs Institute checklist. Data extraction focused on imaging protocols, behavioural parameters, scoring systems, and associations with neonatal neurological outcomes. Fifty eligible studies demonstrated that fetal motor sequences, movement variability, and facial expressions exhibit hierarchical maturation consistent with developmental progression of brainstem, subcortical, and cortical neural circuits. KANET parameters showed reproducible scoring and meaningful correlation with neonatal neurodevelopment, particularly in high-risk pregnancies. Prenatal general movement patterns displayed continuity with postnatal repertoires and contributed to early neurological prediction. AI-based classifiers provided objective quantification of fetal movement and facial activity, supporting automated or semi-automated assessment workflows. Functional neurobehavioural assessment using 4D-US, structured scoring tools, and AI-enhanced analysis is feasible, reproducible, and clinically informative. Integrating behavioural markers with neurosonographic findings and computational modelling may strengthen early detection of neurological risk and improve long-term neurodevelopmental care pathways.