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Maternal and neonatal healthcare faces significant challenges due to delayed diagnosis, limited accessibility, and inadequate real-time monitoring, especially in low-resource settings. This project leverages Artificial Intelligence (AI), Internet of Things (IoT), and Blockchain to provide early detection and personalized care for maternal and neonatal health. Deep learning models analyze ultrasound images to predict preterm births, while machine learning algorithms detect gestational diabetes, anemia, and fetal distress. Wearable IoT sensors monitor maternal and fetal vitals in real-time, ensuring proactive interventions. Blockchain technology secures and decentralizes medical records, enhancing data privacy and patient control. Additionally, an AI-powered chatbot with Natural Language Processing (NLP) and sentiment analysis detects postpartum depression (PPD) and stress, offering mental health support. Smartphone-based AI diagnostics enable low-cost detection of anemia, glucose levels, and infections, making healthcare more affordable and accessible in remote areas. This innovative system aims to bridge the gap in maternal and neonatal care, ensuring safer pregnancies, improved newborn health, and enhanced healthcare accessibility.
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Cite Article:
"Intelligent Maternal and Neonatal Health: AI-Powered Innovations For Early Detection And Personalized Care", International Journal of Science & Engineering Development Research (www.ijrti.org), ISSN:2455-2631, Vol.10, Issue 4, page no.b734-b739, April-2025, Available :http://www.ijrti.org/papers/IJRTI2504192.pdf
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ISSN:
2456-3315 | IMPACT FACTOR: 8.14 Calculated By Google Scholar| ESTD YEAR: 2016
An International Scholarly Open Access Journal, Peer-Reviewed, Refereed Journal Impact Factor 8.14 Calculate by Google Scholar and Semantic Scholar | AI-Powered Research Tool, Multidisciplinary, Monthly, Multilanguage Journal Indexing in All Major Database & Metadata, Citation Generator