Exploring diabetes through the lens of AI and computer vision: Methods and future prospects.

Journal: Computers in biology and medicine
PMID: 39672014

Abstract

Early diagnosis and timely initiation of treatment plans for diabetes are crucial for ensuring individuals' well-being. Emerging technologies like artificial intelligence (AI) and computer vision are highly regarded for their ability to enhance the accessibility of large datasets for dynamic training and deliver efficient real-time intelligent technologies and predictable models. The application of AI and computer vision techniques to enhance the analysis of clinical data is referred to as eHealth solutions that employ advanced approaches to aid medical applications. This study examines several advancements and applications of machine learning, deep learning, and machine vision in global perception, with a focus on sustainability. This article discusses the significance of utilizing artificial intelligence and computer vision to detect diabetes, as it has the potential to significantly mitigate harm to human life. This paper provides several comments addressing challenges and recommendations for the use of this technology in the field of diabetes. This study explores the potential of employing Industry 4.0 technologies, including machine learning, deep learning, and computer vision robotics, as effective tools for effectively dealing with diabetes related aspects.

Authors

  • Ramesh Chundi
    School of Computer Applications, Dayananda Sagar University, Bangalore, India.
  • Sasikala G
    School of Computer Science and Applications, REVA University, Rukmini Knowledge Park, Bangalore 560064, India.
  • Praveen Kumar Basivi
    Pukyong National University Industry-University Cooperation Foundation, Pukyong National University, Busan 48513, Republic of Korea.
  • Anitha Tippana
    Department of Biotechnology, School of Applied Sciences, REVA University, Rukmini Knowledge Park, Bangalore 560064, India.
  • Vishwanath R Hulipalled
    School of Computing and Information Technology, REVA University, Rukmini Knowledge Park, Bangalore 560064, India.
  • Prabakaran N
    School of Computer Science and Engineering, Vellore Institute of Technology, Vellore, 632014, Tamilnadu, India.
  • Jay B Simha
    Abiba Systems, CTO, and RACE Labs, REVA University, Rukmini Knowledge Park, Bangalore 560064, India.
  • Chang Woo Kim
    Department of Nanotechnology Engineering, College of Engineering, Pukyong National University, Busan 48513, Republic of Korea.
  • Vijay Kakani
    Integrated System Engineering, Inha University, 100 Inha-ro, Nam-gu, 22212, Incheon, Republic of Korea. Electronic address: vjkakani@inha.ac.kr.
  • Visweswara Rao Pasupuleti
    Department of Biotechnology, School of Applied Sciences, REVA University, Rukmini Knowledge Park, Bangalore 560064, India; School of Biosciences, Taylor's University, Lakeside Campus, 47500, Subang Jaya, Selangor, Malaysia; Faculty of Earth Sciences, Universiti Malaysia Kelantan, Campus Jeli, Kelantan, 17600 Jeli, Malaysia. Electronic address: visweswararao@reva.edu.in.

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