Covid-19 Diagnosis by Artificial Intelligence Based on Vibraimage Measurement of Behavioral Parameters

Abstract

The hypothesis of behavioral parameters dependence measured from person's head movements in quasi-stationary state on COVID-19 disease is discussed. Method for determining the dependence of vestibular-emotional reflex parameters on COVID-19, various diseases and pathologies are 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 536 measurements, 268 patient measurement results and 268 measurement results in the control group. The achieved diagnostic accuracy was more than 99%, 4 errors per 536 measurements (2 false positive and 2 false negative), specificity 99.25% and sensitivity 99.25%. 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.

COVID-19 Diagnosis by 5-second Facial Video Processing Using Vibraimage and Artificial Intelligence

“…Preliminary study materials and methods. Data 547,039 measurements of 40 behavioral parameters instantaneous values 11 obtained by vibraimage programs MED, HT and PRO 15 with vibraimage settings (N=100; f=5Hz) included: a) patients with confirmed diagnosis of COVID-19. The total number of results in the COVID-19 patient database was 211,064 measurements of 40 behavioral parameters instantaneous values (BPIV).…”

“…The most frequently used vibraimage settings for emotional state measurement are N=100; f=5Hz, which determines the period of information about the psychophysiological state integration T=20s 11 . These standard settings (N=100; f=5Hz) were used in the first versions of the COVID-19 diagnostic vibraimage programs 15,16 , since head micromovements capturing was done by existing programs 15 . The standard approach on psychophysiological parameters measurement using vibraimage technology focused on the averaged values of behavioral parameters, because this increased the accuracy of emotional states measurements 17 .…”

“…Averaged values eliminated the influence of chronobiological processes 18,19,20 and increased the stability of behavioral parameters measured as physical quantities based on developed equations 11 . Using vibraimage technology and trained artificial neural networks (ANN) was possible to achieve almost 100% discrimination accuracy in patients and controls database 15,16,21 . However, COVID-19 diagnosis results calculated by averaged vibraimage parameters processing for a random sample showed the accuracy of COVID-19 diagnostics about 80%, which is comparable to the RT-PCR accuracy of COVID-19 diagnostics 22 , but not enough for the mass application of new COVID-19 non-contact diagnostics technology due to the mistrust of specialists and low accuracy.…”