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Toward the adoption of artificial intelligence-enabled wearable sensors interconnected with intelligent medical objects, this contactless multi-intelligent wearable technology provides a solution for healthcare to monitor hard-to-heal wounds and create optimal efficiencies for clinical professionals by minimizing the risk of disease infection. This article addressed a flexible artificial intelligence-guiding (FLEX-AI) wearable sensor that can be operated with a deep artificial neural network (deep ANN) algorithm for chronic wound monitoring via short-range communication toward a seamless, MXENE-attached, radio frequency-tuned, and wound dressing-integrated (SMART-WD) bandage. Based on a supervised training set of on-contact pH-responsive voltage output, the confusion matrix for healing-stage recognition from this deep ANN machine learning revealed an accuracy of 94.6% for the contactless measurement. The core analytical design of these smart bandages integrated wound dressing of poly(vinyl acrylic) gel@PANI/Cu2O NPs for instigating pH-responsive current during the wound healing process. Effectively, a chip-free bandage tag was fabricated with a capacitive Mxene/PTFE electret and adhesive acrylic inductance to match the resonance frequency generated by the intelligent wearable antenna. Under zero-current electrochemical potential, the wound dressing attained a slope of −76 mV/pH. With the higher activation voltage applied toward the wound dressing electrodes, cuprous ions intercalated more into the hybrid PVA gel/PANI shell, resulting in an exponential increase of the two-terminal current response. The healing phase diagram was classified into regimes of fast-curing, slow-curing, and no-curing for skin disease treatment with corticosteroids. Ultimately, the near-field sensing technology offers adequate information for guiding treatment decisions as well as drug effectiveness for wound care.
Kalasin et al. (Mon,) studied this question.
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