Electrical bioimpedance in the era of artificial intelligence

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Today, the first thing that comes to mind when someone mentions bioimpedance might be body composition estimation, and when someone mentions AI, we might immediately think of chatbots.The progression of AI over the preceding decades has witnessed a transition from attempting to artificially replicate neural communication in the human brain during the 1950s to contemporary emphasis on ethical considerations associated with the responsible utilization of AI in the 2020s.The bandwagon of AI has influenced many fields of science.Given that electrical bioimpedance is often a multi-variable measurement for the prediction of a physiological state, the developments in AI-based solutions are certainly relevant to our field as well.An integration of electrical bioimpedance and AI could be important in the development of future health monitoring devices, driven by a shift from reactive treatment (after symptom onset) to preventive self-care.From simple neural networks to deep learning (DL), machine learning (ML) has already been used for over a decade in the development of prediction models based on bioimpedance data.More recently it has been used to improve several applications of electrical bioimpedance such as cuffless blood pressure [1, 2, 3], body composition analysis [4], non-invasive blood glucose measurement [5], classification of spectroscopic data (i.e., electrical impedance spectroscopy (EIS)) [6,7] and electrical impedance tomography (EIT) [8,9,10].Consider the non-invasive imaging technique EIT as an example, it reconstructs the spatial distribution of the passive electrical properties of the sensing area, relying on data processing and reconstruction algorithms.Recent substantial progress in leveraging DL techniques for AI-based medical imaging has prompted considerable

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