International Journal of Science, Engineering and TechnologyAuthors: A. Naveen, N. Shahul Hameed, R. Thilagar, S. Vishwabharathi, Dr. S. Sutha
Abstract: Neonatal healthcare is a critical domain in modern medicine, focusing on the survival, growth, and development of newborn infants during the first few weeks of life. Early detection of neonatal disorders such as jaundice, dehydration, and neurological abnormalities plays a vital role in preventing long-term complications and reducing infant mortality rates. However, conventional diagnostic techniques primarily rely on blood-based tests, which are invasive, time-consuming, and often require well-equipped laboratory facilities and trained personnel. These limitations make such methods less suitable for continuous monitoring and challenging to implement in rural or low-resource settings. In recent years, there has been growing interest in the development of non-invasive diagnostic approaches that can provide accurate and real-time health assessment without causing discomfort to the patient. Among various biological fluids, saliva has emerged as a promising diagnostic medium due to its ease of collection, safety, and ability to reflect physiological and biochemical conditions of the body. Saliva contains a wide range of biomarkers, including enzymes, proteins, hormones, and metabolites, which can be used to detect various health conditions. The non-invasive nature of saliva collection is particularly beneficial for neonatal applications, where minimizing pain and stress is of utmost importance. The proposed system introduces a smart multimodal neonatal screening approach that utilizes salivary biomarkers for disease detection. By integrating microfluidic technology, optical sensing, and artificial intelligence, the system aims to provide a comprehensive and efficient diagnostic solution. Microfluidic chips enable precise handling of small sample volumes and facilitate controlled biochemical reactions, while optical sensors detect colour changes corresponding to biomarker concentrations. These signals are processed using embedded systems and analysed using machine learning algorithms to classify neonatal conditions accurately. Furthermore, the incorporation of smartphone-based imaging and mobile applications enhances the accessibility and usability of the system. The ability to store and transmit data through cloud platforms enables remote monitoring and telemedicine applications, making the system highly suitable for deployment in rural and underserved areas. Overall, the proposed system represents a significant advancement in neonatal healthcare by combining non-invasive diagnostics with modern technological innovations.
