AI-Enabled Early Detection of Fetal Gestational Age and CNS Anomalies in the First Trimester through Ultrasound to Support Rural Doctors in India

International Journal of Electronics and Communication Engineering

© 2025 by SSRG - IJECE Journal

Volume 12 Issue 7

Year of Publication : 2025

Authors : Vinita Gaikwad, Anamika Dhawan, Padma Nilesh Mishra, M. Kumarasamy

10.14445/23488549/IJECE-V12I7P113

How to Cite?

Vinita Gaikwad, Anamika Dhawan, Padma Nilesh Mishra, M. Kumarasamy, "AI-Enabled Early Detection of Fetal Gestational Age and CNS Anomalies in the First Trimester through Ultrasound to Support Rural Doctors in India," SSRG International Journal of Electronics and Communication Engineering, vol. 12,  no. 7, pp. 174-183, 2025. Crossref, https://doi.org/10.14445/23488549/IJECE-V12I7P113

Abstract:

Identification of Central Nervous System (CNS) abnormalities in fetuses is serious for timely medical intervention and improved perinatal outcomes. India stands as the most populous country in the world today, with a population of more than 1.484 billion. Of this 68.8% population is living in rural India, where the healthcare infrastructure facilities are sparse. Access to specialized fetal medicine experts and advanced diagnostic tools remains a significant challenge in rural India. According to a report published through NGO Transform Rural India besides Development Intelligence Unit, the doctor-patient proportion in India is approximately 1:1456, which remains under the World Health Organization (WHO) recommended ratio of 1:1000. Thus, the report also revealed a lack of diagnostic facilities in the form of a shortage of trained personnel. The report indicated that only 39% of survey respondents had access to a diagnostic facility within commutable distance. Ultrasonography in the first trimester of pregnancy aims to confirm viability, gestational age, position, and implantation of the gestational sac, as well as fetal anatomy, amongst other factors. Structural brain abnormalities are comparatively common and can be spotted in the first trimester. The study explores the potential of Artificial Intelligence in enhancing the initial Identification of central nervous system anomalies during first-trimester sonography, particularly to support rural healthcare providers. Thus, the reading concludes that a Deep Learning model of AI succeeded with an accuracy of 88% training and 87.76% in testing in detecting CNS abnormalities using the Head Circumference parameter in cm of the fetus in the first trimester. Henceforth, the AI-based diagnostic support can help as a transformative tool in linking the healthcare gap aimed at rural populations in India.

Keywords:

Artificial Intelligence, CNS anomalies, Fetal US sonography, First trimester, Rural healthcare, Early detection.

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