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Resiliency and Risk Assessment of Smart Vision-Based Skin Screening Applications with Dynamics Modeling

Author

Listed:
  • Misagh Faezipour

    (Department of Engineering Technology, Middle Tennessee State University, Murfreesboro, TN 37132, USA
    These authors contributed equally to this work.)

  • Miad Faezipour

    (School of Engineering Technology, Electrical and Computer Engineering Technology, Purdue University, West Lafayette, IN 47907, USA
    These authors contributed equally to this work.)

  • Saba Pourreza

    (Congdon School of Supply Chain, Business Analytics and Information Systems, University of North Carolina Wilmington, Wilmington, NC 28403, USA
    These authors contributed equally to this work.)

Abstract

The prevalence of skin diseases remains a concern, leading to a rising demand for the advancement of smart, portable, and non-invasive automated systems and applications. These sought-after technologies allow for the screening of skin lesions through captured images, offering improved and accessible healthcare solutions. Clinical methods include visual inspection by dermatologists; computer-aided vision-based image analysis at healthcare settings; and, lastly, biopsy tests, which are often costly and painful. Given the rise of artificial intelligence-based techniques for image segmentation, analysis, and classification, there remains a need to investigate the resiliency of personalized smartphone (hand-held) skin screening systems with respect to identified risks. This study represents a unique integration of distinct fields pertaining to smart vision-based skin lesion screening, resiliency, risk assessment, and system dynamics. The main focus is to explore the dynamics within the supply chain network of smart skin-lesion-screening systems. With the overarching aim of enhancing health, well-being, and sustainability, this research introduces a new framework designed to evaluate the resiliency of smart skin-lesion-screening applications. The proposed framework incorporates system dynamics modeling within a novel subset of a causal model. It considers the interactions and activities among key factors with unique mapping of capability and vulnerability attributes for effective risk assessment and management. The model has been rigorously tested under various case scenarios and settings. The simulation results offer insights into the model’s dynamics, demonstrating the fact that enhancing the skin screening device/app factors directly improves the resiliency level. Overall, this proposed framework marks an essential step toward comprehending and enhancing the overall resiliency of smart skin-lesion-screening systems.

Suggested Citation

  • Misagh Faezipour & Miad Faezipour & Saba Pourreza, 2023. "Resiliency and Risk Assessment of Smart Vision-Based Skin Screening Applications with Dynamics Modeling," Sustainability, MDPI, vol. 15(18), pages 1-19, September.
    • Handle: RePEc:gam:jsusta:v:15:y:2023:i:18:p:13832-:d:1241595
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    References listed on IDEAS

    as
    1. Misagh Faezipour & Miad Faezipour, 2020. "Sustainable Smartphone-Based Healthcare Systems: A Systems Engineering Approach to Assess the Efficacy of Respiratory Monitoring Apps," Sustainability, MDPI, vol. 12(12), pages 1-18, June.
    2. Andre Esteva & Brett Kuprel & Roberto A. Novoa & Justin Ko & Susan M. Swetter & Helen M. Blau & Sebastian Thrun, 2017. "Correction: Corrigendum: Dermatologist-level classification of skin cancer with deep neural networks," Nature, Nature, vol. 546(7660), pages 686-686, June.
    3. Lindbom, Hanna & Tehler, Henrik & Eriksson, Kerstin & Aven, Terje, 2015. "The capability concept – On how to define and describe capability in relation to risk, vulnerability and resilience," Reliability Engineering and System Safety, Elsevier, vol. 135(C), pages 45-54.
    4. Andre Esteva & Brett Kuprel & Roberto A. Novoa & Justin Ko & Susan M. Swetter & Helen M. Blau & Sebastian Thrun, 2017. "Dermatologist-level classification of skin cancer with deep neural networks," Nature, Nature, vol. 542(7639), pages 115-118, February.
    5. Hosseini, Seyedmohsen & Barker, Kash & Ramirez-Marquez, Jose E., 2016. "A review of definitions and measures of system resilience," Reliability Engineering and System Safety, Elsevier, vol. 145(C), pages 47-61.
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