A Survey on Impact of Internet of Medical Things Against Diabetic Foot Ulcer

被引：0
作者：
Vaishnavi R.A. [1 ]
Jegathesh P. [2 ]
Jayasheela M. [1 ,3 ]
Mahalakshmi K. [1 ,4 ]
机构：
[1] Kalaignar Karunanidhi Institute of Technology, Coimbatore
[2] Department of Information Technology, Karpagam College of Engineering, Coimbatore
[3] Department of Electronics and Communication Engineering, Kalaignar Karunanidhi Institute of Technology, Coimbatore
[4] Department of Computer Science and Engineering, Kalaignar Karunanidhi Institute of Technology, Coimbatore
来源：
EAI Endorsed Transactions on Pervasive Health and Technology | 2024年 / 10卷
关键词：
Classification; Detection; Diabetic Foot Ulcer; Internet of Medical Things; Smart analysis prediction; Smart Health;
D O I：
10.4108/eetpht.10.5170
中图分类号：
学科分类号：
摘要：
INTRODUCTION: In this study, we explore the intricate domain of Diabetic Foot Ulcers (DFU) through the development of a comprehensive framework that encompasses diverse operational scenarios. The focus lies on the identification and classification assessment of diabetic foot ulcers, the implementation of smart health management strategies, and the collection, analysis, and intelligent interpretation of data related to diabetic foot ulcers. The framework introduces an innovative approach to predicting diabetic foot ulcers and their key characteristics, offering a technical solution for forecasting. The exploration delves into various computational strategies designed for intelligent health analysis tailored to patients with diabetic foot ulcers. OBJECTIVES: The primary objective of this paper is to present a technical solution for forecasting diabetic foot ulcers, utilizing computational strategies for intelligent health analysis. METHODS: Techniques derived from social network analysis are employed to conduct this research, focusing on diverse computational strategies geared towards intelligent health analysis for patients with diabetic foot ulcers. The study highlights methodologies addressing the unique challenges posed by diabetic foot ulcers, with a central emphasis on the integration of Internet of Medical Things (IoMT) in prediction strategies. RESULTS: The main results of this paper include the proposal of IoMT-based computing strategies covering the entire spectrum of DFU analysis, such as localization, classification assessment, intelligent health management, and detection. The study also acknowledges the challenges faced by previous research, including low classification rates and elevated false alarm rates, and proposes automatic recognition approaches leveraging advanced machine learning techniques to enhance accuracy and efficacy. CONCLUSION: The proposed IoMT-based computing strategies present a significant advancement in addressing the challenges associated with predicting diabetic foot ulcers. The integration of advanced machine learning techniques demonstrates promise in improving accuracy and efficiency in diabetic foot ulcer localization, marking a positive stride towards overcoming existing limitations in previous research. © 2024 R. Athi Vaishnavi et al.
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  • [1] Akturk A, van Netten JJ, Vermeer M, Kruse RR, Schaper NC, van Gemert-Pijnen LJ, van Baal JG., Improved outcomes in patients with diabetic foot ulcers despite differences in baseline characteristics, Diabet Foot Ulcers, 26, 6, pp. 912-919, (2021)
  • [2] Alshamrani M., IoT and artificial intelligence implementations for remote healthcare monitoring systems: A survey, J King Saud Univ-Comput Inf Sci, 34, 8, pp. 4687-4701, (2021)
  • [3] Anjum N, Asif A, Kiran M, Jabeen F, Yang Z, Huang C, Mohamed EM., Intelligent covid-19 forecasting, diagnoses and monitoring systems: A survey, IEEE Communications Surveys & Tutorials, 14, 8, pp. 1-24, (2021)
  • [4] Baras MH, Bin-Hameed EA., Estimating the Efficiency of Phagocytic Neutrophil Cells and Studying Its Risk Factors Among Diabetic Foot Ulcers, J Phys: Conf Ser, 1900, 1, pp. 1-8, (2021)
  • [5] Bus SA, Wouter B, van Baal JG, Busch-Westbroek TE, Nollet F, van Netten JJ., Effectiveness of at-home skin temperature monitoring in reducing the incidence of foot ulcer recurrence in people with diabetes: a multicenter randomized controlled trial (DIATEMP), BMJ Open Diab Res Care, 9, 1, pp. 1-11, (2021)
  • [6] Cassidy B, Reeves ND, Pappachan JM, Gillespie D, O'Shea C, Rajbhandari S, Yap MH., The DFUC 2020 dataset: Analysis towards diabetic foot ulcer detection, Touch REV Endocrinol, 17, 1, pp. 5-11, (2021)
  • [7] Chiu A, Sharma D, Zhao F., Tissue Engineering-Based Strategies for Diabetic Foot Ulcer Management, Adv Wound Care, 12, 3, pp. 145-167, (2023)
  • [8] Gershater MA, Apelqvist J., Elderly individuals with diabetes and foot ulcer have a probability for healing despite extensive comorbidity and dependency, Expert Rev Pharmacoecon Outcomes Res, 21, 2, pp. 277-284, (2021)
  • [9] Gokalgandhi D, Kamdar L, Shah N, Mehendale N., A Review of Smart Technologies Embedded in Shoes, J Med Syst, 44, 9, pp. 1-9, (2020)
  • [10] Hassan H, Ren Z, Zhou C, Khan MA, Pan Y, Zhao J, Huang B., Supervised and weakly supervised deep learning models for COVID-19 CT diagnosis: A systematic review, Comput Methods Programs Biomed, 218, 1, pp. 1-18, (2022)
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