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K M A, R R, Krishnamoorthy R, Gogula S, S B, Muthu S, Chellamuthu G, Subramaniam K. Internet of Things enabled open source assisted real-time blood glucose monitoring framework. Sci Rep 2024; 14:6151. [PMID: 38486038 PMCID: PMC10940634 DOI: 10.1038/s41598-024-56677-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/15/2023] [Accepted: 03/09/2024] [Indexed: 03/18/2024] Open
Abstract
Regular monitoring of blood glucose levels is essential for the management of diabetes and the development of appropriate treatment protocols. The conventional blood glucose (BG) testing have an intrusive technique to prick the finger and it can be uncomfortable when it is a regular practice. Intrusive procedures, such as fingerstick testing has negatively influencing patient adherence. Diabetic patients now have an exceptional improvement in their quality of life with the development of cutting-edge sensors and healthcare technologies. intensive care unit (ICU) and pregnant women also have facing challenges including hyperglycemia and hypoglycemia. The worldwide diabetic rate has incited to develop a wearable and accurate non-invasive blood glucose monitoring system. This research developed an Internet of Things (IoT) - enabled wearable blood glucose monitoring (iGM) system to transform diabetes care and enhance the quality of life. The TTGOT-ESP32 IoT platform with a red and near-infrared (R-NIR) spectral range for blood glucose measurement has integrated into this wearable device. The primary objective of this gadget is to provide optimal comfort for the patients while delivering a smooth monitoring experience. The iGM gadget is 98.82 % accuracy when used after 10 hours of fasting and 98.04 % accuracy after 2 hours of breakfast. The primary objective points of the research were continuous monitoring, decreased risk of infection, and improved quality of life. This research contributes to the evolving field of IoT-based healthcare solutions by streaming real-time glucose values on AWS IoT Core to empower individuals with diabetes to manage their conditions effectively. The iGM Framework has a promising future with the potential to transform diabetes management and healthcare delivery.
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Affiliation(s)
- Abubeker K M
- Department of Electronics and Communication Engineering, Amal Jyothi College of Engineering (Autonomous), Koovappally, Kerala, India.
| | - Ramani R
- Department of Computer Science and Engineering, P.S.R Engineering College, Sivakasi, Tamilnadu, India
| | - Raja Krishnamoorthy
- Department of Electronics and Communication Engineering, Kalasalingam Academy of Research and Education, Krishnankoil, Tamilnadu, India
| | - Sreenivasulu Gogula
- Department of Computer Science and Engineering, (Data Science), Vardhaman College of Engineering, Shamshabad, Hyderabad, India
| | - Baskar S
- Faculty of Engineering, Department of Electronics and Communication Engineering, Karpagam Academy of Higher Education, Coimbatore, Tamilnadu, India
| | - Sathish Muthu
- Department of Orthopaedics, Government Medical College & Hospital, Karur, Tamilnadu, India
| | - Girinivasan Chellamuthu
- Department of Orthopaedics, Saveetha Medical College and Hospital, Saveetha Institute of Medical and Technical Sciences (SIMATS), Saveetha University, Chennai, Tamilnadu, India
| | - Kamalraj Subramaniam
- Department of Biomedical Engineering, Karpagam Academy of Higher Education, Coimbatore, Tamilnadu, India
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K M A, S B. B2-Net: An Artificial Intelligence Powered Machine Learning Framework for the Classification of Pneumonia in Chest X-ray Images. Mach Learn : Sci Technol 2023. [DOI: 10.1088/2632-2153/acc30f] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/11/2023] Open
Abstract
Abstract
A chest X-ray radiograph is still the global standard for diagnosing pneumonia and helps distinguish between bacterial and viral pneumonia. Despite several studies, radiologists and physicians still have trouble correctly diagnosing and classifying pneumonia without false negatives. Modern mathematical modeling and artificial intelligence could help to reduce false-negative rates and improve diagnostic accuracy. This research aims to create a novel and efficient multiclass machine learning framework for analyzing and classifying chest X-ray images on a graphics processing unit. Researchers initially applied a geometric augmentation using a positional transformation function to the original dataset to enhance the sample size and aid future transfer learning. Models with the best accuracy, AUROC, F1 score, precision, recall, and specificity are chosen from a pool of nine state-of-the-art neural network models. The best-performing models are then retrained using an ensemble technique using depth-wise convolutions, demonstrating significant improvements over the baseline models employed in this research. With a remarkable 97.69% accuracy, 100% recall, and 0.9977 AUROC scores, the proposed B2-Net (Bek-Bas Network) model can differentiate between normal, bacterial, and viral pneumonia in chest X-ray images. A superior model is retrained using the chosen DenseNet-160, ResNet-121, and VGGNet-16 ensemble models. The diagnostic accuracy of the X-ray classification unit is enhanced by the newly designed multiclass network, the B2-Net model. The developed GPU-based framework has been examined and tested to the highest clinical standards. After extensive clinical testing, the final B2-Net model is implemented on an NVIDIA Jetson Nano graphics processing unit computer. Healthcare facilities have confirmed the B2-Net is the most effective framework for identifying bacterial and viral pneumonia in chest X-rays.
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