Identification of Chronic Wound Status under Tele-Wound Network through Smartphone

Identification of Chronic Wound Status under Tele-Wound Network through Smartphone

Chinmay Chakraborty (Birla Institute of Technology, India), Bharat Gupta (Birla Institute of Technology, India) and Soumya K. Ghosh (Indian Institute of Technology Kharagpur, India)
DOI: 10.4018/978-1-4666-8756-1.ch037
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This paper presents a tele-wound framework for monitoring chronic wound status based on color variation over a period of time. This will facilitate patients at remote locations to connect to medical experts through mobile devices. Further this will help medical professionals to monitor and manage the wounds in more timely, accurate and precise manner using the proposed framework. Tele-medical agent (TMA) collects the chronic wound data using smart phone and send it to the Tele-medical hub (TMH). In TMH, the wound image has been segmented using Fuzzy C-Means which gives highest segmented accuracy i.e. 92.60%, then the wound tissue is classified using proposed Bayesian classifier. The smart phone supported prototype system has been demonstrated with snapshots using very compatible and easy to integrate Hypertext preprocessor (PHP) and MySqL. The proposed system may facilitate better wound management and treatment by providing percentage of wound tissues.
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1. Introduction Of Chronic Wound

Management and monitoring of chronic wounds is a major challenge. A tele-wound care comprising transmission of chronic wound (CW) images and a clinical protocol to home bound patients resulted in reductions of emergency visits, hospitalization, hospital utilization and cost [Rees, et al. 2007]. More than $25 billion is spent annually on the treatment of CWs [Hopf, H. W. 2006]. In the United States, the percentage of the aged population (age 65 and more) is projected to increase from 12.4% in 2000 to 19.6% in 2030 [U.S. Census Bureau. 2013]. The cost-effectiveness analysis is used to measure and compare the relative costs and results associated with various interventions as comprehensively as possible [Weinstein, M. C. et al. 1996]. The CW size can be determined using various methods have been developed and validated including wound depth [Coulomb, B. et al. 1986], surface area [Thomas, A. 2002] [William, P.B et al. 1997] length and width [Herbin, M. et al. 1993] and volume [Thomas, G. 2004]. The authors [Stremitzer, S. et al. 2007] were to investigate the spread and variety in CW judgment. The different tissues like granulation, fibrin, necrosis, CW size, depth, exudate and edges were judged and the therapeutical consequences were determined. Several CW assessment tools have been developed like pressure sore status tool (PSST) [Julien, M. et al. 2008], the sessing scale [Ferrell, B. A. et al. 1995], sussman wound healing tool (SWHT) [Sussman, C. et al. 2007], pressure ulcer scale for healing (PUSH) [Plassmann, P. et al. 2013] and wound healing scale (WHS) [Julien, M. et al., 2008] to monitoring wound healing status.

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