Medanki S, Dommati N, Bodapati HH, Katru VNSK, Moses G, Komaraju A, Donepudi NS, Yalamanchili D, Sateesh J, Turimerla P. Artificial intelligence powered glucose monitoring and controlling system: Pumping module. World J Exp Med 2024; 14(1): 87916 [PMID: 38590308 DOI: 10.5493/wjem.v14.i1.87916]
Corresponding Author of This Article
Jasti Sateesh, PhD, Academic Research, Department of Electronics and Communication Engineering, Velagapudi Ramakrishna Siddhartha Engineering College, Kanuru, 340, Vijayawada 520007, Andhra Pradesh, India. sateeshjasti441@gmail.com
Research Domain of This Article
Engineering, Biomedical
Article-Type of This Article
Basic Study
Open-Access Policy of This Article
This article is an open-access article which was selected by an in-house editor and fully peer-reviewed by external reviewers. It is distributed in accordance with the Creative Commons Attribution Non Commercial (CC BY-NC 4.0) license, which permits others to distribute, remix, adapt, build upon this work non-commercially, and license their derivative works on different terms, provided the original work is properly cited and the use is non-commercial. See: http://creativecommons.org/licenses/by-nc/4.0/
World J Exp Med. Mar 20, 2024; 14(1): 87916 Published online Mar 20, 2024. doi: 10.5493/wjem.v14.i1.87916
Artificial intelligence powered glucose monitoring and controlling system: Pumping module
Sravani Medanki, Nikhil Dommati, Hema Harshitha Bodapati, Venkata Naga Sai Kowsik Katru, Gollapalli Moses, Abhishek Komaraju, Nanda Sai Donepudi, Dhanya Yalamanchili, Jasti Sateesh, Pratap Turimerla
Sravani Medanki, Nikhil Dommati, Hema Harshitha Bodapati, Venkata Naga Sai Kowsik Katru, Gollapalli Moses, Jasti Sateesh, Pratap Turimerla, Department of Electronics and Communication Engineering, Velagapudi Ramakrishna Siddhartha Engineering College, Vijayawada 520007, Andhra Pradesh, India
Nanda Sai Donepudi, Dhanya Yalamanchili, Department of General Medicine, Siddhartha Government Medical College, Vijayawada 520008, Andhra Pradesh, India
Author contributions: Sateesh J designed the research idea and supervised the project; Medanki S, Dommati N, Bodapati HH, Komaraju A, Katru VNSK, and Moses G performed the research; Donepudi NS and Yalamanchili D validation of idea and research supervision; Turimerla P design and supervision; All authors contributed equally.
Institutional review board statement: This work does not involve any animal or human samples and no experimentation was carried out on animals or humans. The authors feel this research does not necessitate institutional review board approval.
Conflict-of-interest statement: All the authors declare no conflicts of interest.
Data sharing statement: Data will be shared by the authors upon reasonable request.
Open-Access: This article is an open-access article that was selected by an in-house editor and fully peer-reviewed by external reviewers. It is distributed in accordance with the Creative Commons Attribution Noncommercial (CC BY-NC 4.0) license, which permits others to distribute, remix, adapt, build upon this work non-commercially, and license their derivative works on different terms, provided the original work is properly cited and the use is non-commercial. See: https://creativecommons.org/Licenses/by-nc/4.0/
Corresponding author: Jasti Sateesh, PhD, Academic Research, Department of Electronics and Communication Engineering, Velagapudi Ramakrishna Siddhartha Engineering College, Kanuru, 340, Vijayawada 520007, Andhra Pradesh, India. sateeshjasti441@gmail.com
Received: September 2, 2023 Peer-review started: September 2, 2023 First decision: November 1, 2023 Revised: December 18, 2023 Accepted: February 3, 2024 Article in press: February 3, 2024 Published online: March 20, 2024 Processing time: 199 Days and 10.4 Hours
Core Tip
Core Tip: This study describes an innovative diabetes management system that integrates real-time glucose monitoring with advanced artificial intelligence (AI) algorithms and closed-loop insulin delivery. This approach offers precise, personalized control of blood glucose levels, reducing the risk of complications. The simulations demonstrate the system's effectiveness in maintaining stable glucose levels, even in response to meals and exercise, thanks to the International Diabetes Federation controller and basal insulin dosage. Additionally, the study explores the use of ESP32 for monitoring input voltage levels to protect the system. Overall, this study provides a groundbreaking approach that combines AI, closed-loop control, and real-time monitoring to revolutionize diabetes care and improve patient outcomes.
