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Covid-19 Diagnosis by Artificial Intelligence Basing on Vibraimage Measurement of Head Movement

Abstract: The hypothesis of reflex micromovements dependence of person’s head in quasistationary state on the COVID-19 infection virus is discussed. Method for determining the dependence of vestibular-emotional reflex parameters on COVID-19, various diseases and pathologies is proposed. Micro-movements of a head for representatives of the control group (with a confirmed absence of COVID-19 disease) and a group of patients with a confirmed diagnosis of COVID-19 were studied using vibraimage technology. Parameters and criteria for the diagnosis of COVID-19 for training artificial intelligence (AI) on the control group and the patient group are proposed. 3-layer (one hidden layer) feedforward neural network (40+20+1 sigmoid neurons) was developed for AI training. AI was firstly trained on the primary sample of patients and a control group. Study of a random sample of people with trained AI was carried out and the possibility of detecting COVID-19 using the proposed method was proved a week before the onset of clinical symptoms of the disease. Number of COVID-19 diagnostic parameters was increased to 26 and AI was trained on a sample of 522 measurements, 261 patient measurement results and 261 measurement results in the control group. The achieved diagnostic accuracy was more than 99%, 4 errors per 522 measurements (2 false positive and 2 false negative), specificity 99,23% and sensitivity 99,23%. The issues of improving the accuracy and reliability of the proposed method for diagnosing COVID-19 are discussed. Further ways to improve the characteristics and applicability of the proposed method of diagnosis and self-diagnosis of COVID-19 are outlined. Keywords: vibraimage, health, artificial neural networks, ANN, artificial intelligence, AI, vestibular-emotional reflex, diagnosis of diseases, telemedicine, COVID-19.

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Covid-19 Diagnosis by Artificial Intelligence Basing on Vibraimage Measurement of Head Movement
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