NONINVASIVE MONITORING OF CRITICALLY ILL CHILD

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DEEP LEARNING APPLICATIONS FOR NONCONTACT MONITORING IN HEALTHCARE

Ufuk Bal1 Ebubekir Akkuş2 Özge Taylan Moral3

1Osmaniye Korkut Ata University, Faculty of Engineering and Natural Sciences, Department of Electrical and Electronics Engineering, Osmaniye, Türkiye
2Aydın Adnan Menderes University, Germencik Yamantürk Vocational School, Department of Electronics and Automation, Aydın, Türkiye
3İstanbul University-Cerrahpasa, Vocational School of Technical Sciences, Department of Electronics and Automation, İstanbul, Türkiye

Bal U, Akkuş E, Taylan Moral Ö. Deep Learning Applications For Noncontact Monitoring In Healthcare. In: Bal A, editor. Noninvasive Monitoring of Critically Ill Child. 1st ed. Ankara: Türkiye Klinikleri; 2025. p.135-146.

ABSTRACT

This chapter explores the transformative potential of deep learning in revolutionizing the care of criti- cally ill pediatric patients. By leveraging data from optical sensors and video recordings, Deep learning algorithms enable accurate, contactless estimation of vital signs, facilitating early disease detection, personalized treatment, and remote monitoring. This technology holds significant promise for improv- ing outcomes, particularly for vulnerable populations such as preterm infants and children with chronic conditions. The chapter examines the technical foundations of various deep learning models, including convolutional neural networks, recurrent neural networks, and graph neural networks. Additionally, it addresses the ethical implications of artificial intelligence in healthcare, highlighting the importance of data privacy, algorithmic bias, and explainability to build trust and encourage widespread adoption. By overcoming these challenges, deep learning has the potential to revolutionize pediatric care, leading to improved patient outcomes, reduced healthcare costs, and an enhanced quality of life for children and their families.

Keywords: Deep learning; Non-contact monitoring; Pediatric healthcare; Predictive analytics; Personalized medicine.

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