Dr. Chinmay Chakraborty

Chronic wounds and ulcers present a significant clinical challenge due to their prolonged healing time, high risk of infection, and frequent need for specialist monitoring. Traditional wound management methods often fall short in providing timely intervention, especially for patients in remote or underserved areas. This presentation explores an integrated approach combining advanced imaging technologies, machine learning, and telemedicine to revolutionize chronic wound care.

We propose a framework where high-resolution wound images are captured using mobile or portable imaging systems and analyzed through ML algorithms to classify wound types, predict healing progression, and identify potential complications. The system is designed to be embedded within a telemedicine platform, enabling clinicians to remotely monitor and manage patients while reducing hospital visits and improving care continuity.

Key components include real-time image processing, predictive analytics, patient data integration, and remote communication features. The presentation highlights case studies, data acquisition protocols, algorithm performance, and deployment strategies in clinical and rural settings. This multidisciplinary approach promises not only to improve diagnostic accuracy and treatment outcomes but also to reduce healthcare costs and enhance patient satisfaction. The solution aligns with current trends in digital health, making it a timely and scalable model for modern wound care.

Biography:

Dr. Chinmay Chakraborty, SMIEEE, MACM, is currently serving as Associate Professor and Head, Centre of Interdisciplinary Research (COIR) in Medical Technology at KIIT Deemed to be University, India. He received a Post-Doctoral Fellowship from the Federal University of Piauí, Brazil, and has held visiting research positions at the University of Malta (Europe) and Chongqing Technology & Business University (China). His research interests include the Internet of Medical Things (IoMT), Artificial Intelligence & Machine Learning (AI/ML), Health Informatics, Biomedical Systems, Telemedicine, and Medical Imaging.

Dr. Chakraborty has published over 250 research articles in peer-reviewed journals, conferences, and book chapters. He serves as Deputy Editor-in-Chief of IEEE TCEL, Senior Editor of IEEE JETCAS, and Associate Editor for IEEE TII, IEEE Sensors Journal, and other top-tier journals. He is also Lead Guest Editor for several IEEE and ACM special issues and Book Series Editor of Advances in Smart Healthcare Technologies (CRC Press, Taylor & Francis). He has received several prestigious recognitions, including Stanford University’s Top 2% Global Scientists in “AI & Image Processing” (2021–2025), the AICTE Distinguished Professional (2025), and the Marie Skłodowska-Curie Actions Fellowship (Horizon Europe). He also won the Startup Ideation Prize at the SERB-INAE ANRF Conclave 2025 and received the BIT Faculty Appreciation Award (BITFAA’24).

Dr. Chakraborty is actively involved in academic collaboration, editorial leadership, and interdisciplinary research, promoting innovation in smart and affordable healthcare systems.

Email: