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Background: Respiratory sounds have been recognized as a possible indicator of behavior and health. Computer analysis of these sounds can indicate characteristic sound changes caused by COVID-19 and can be used for diagnostics of this illness., Objective: The aim of the study is to develop 2 fast, remote computer-assisted diagnostic methods for specific acoustic phenomena associated with COVID-19 based on analysis of respiratory sounds., Methods: Fast Fourier transform (FFT) was applied for computer analysis of respiratory sound recordings produced by hospital doctors near the mouths of 14 patients with COVID-19 (aged 18-80 years) and 17 healthy volunteers (aged 5-48 years). Recordings for 30 patients and 26 healthy persons (aged 11-67 years, 34, 60%, women), who agreed to be tested at home, were made by the individuals themselves using a mobile telephone; the records were passed for analysis using WhatsApp. For hospitalized patients, the illness was diagnosed using a set of medical methods; for outpatients, polymerase chain reaction (PCR) was used. The sampling rate of the recordings was from 44 to 96 kHz. Unlike usual computer-assisted diagnostic methods for illnesses based on respiratory sound analysis, we proposed to test the high-frequency part of the FFT spectrum (2000-6000 Hz)., Results: Comparing the FFT spectra of the respiratory sounds of patients and volunteers, we developed 2 computer-assisted methods of COVID-19 diagnostics and determined numerical healthy-ill criteria. These criteria were independent of gender and age of the tested person., Conclusions: The 2 proposed computer-assisted diagnostic methods, based on the analysis of the respiratory sound FFT spectra of patients and volunteers, allow one to automatically diagnose specific acoustic phenomena associated with COVID-19 with sufficiently high diagnostic values. These methods can be applied to develop noninvasive screening self-testing kits for COVID-19., (©Gregory Furman, Evgeny Furman, Artem Charushin, Ekaterina Eirikh, Sergey Malinin, Valery Sheludko, Vladimir Sokolovsky, David Shtivelman. Originally published in JMIR Formative Research (https://formative.jmir.org), 19.07.2022.)

The aim of the study is to develop a method for detection of pathological respiratory sound, caused by bronchial asthma, with the aid of machine learning techniques., Materials and Methods: To build and train neural networks, we used the records of respiratory sounds of bronchial asthma patients at different stages of the disease (n=951) aged from several months to 47 years old and healthy volunteers (n=167). The sounds were recorded with calm breathing at four points: at the oral cavity, above the trachea, on the chest (second intercostal space on the right side), and at a point on the back., Results: The method developed for computer-aided detection of respiratory sounds allows to diagnose sounds typical for bronchial asthma in 89.4% of cases with 89.3% sensitivity and 86.0% specificity regardless of sex and age of the patients, stage of the disease, and the point of sound recording., Competing Interests: The authors have no conflicts of interest to declare.

To develop a mathematical model and evaluate its prognostic significance for determining the severity of attack (SA) in patients with ulcerative colitis (UC) on the basis of minimally invasive laboratory inflammatory tests and albumin. The study involved 64 patients (33 men and 31 women) with UC in the stage of active inflammation. The method for diagnosing SA was performed by determining the concentration in the blood of tumor necrosis factor alpha (TNF-α), C-reactive protein (CRP), erythrocyte sedimentation rate (ESR), α2-globulin and albumin. Truelove and Witts criteria (1955) were used as a reference method for the evaluation of SA in patients with UC. In constructing a mathematical model, the severity of the attack of the UC emerged as a dependent variable. As independent variables or predictors - five laboratory parameters: TNF-α, CRP, ESR, α2-globulins and albumin, which had a high relationship with SA. At SA value in the range from 0 to 0.5, absence of attack or remission of the disease is determined, SA value in the range from 0.5 to 1.2 corresponds to mild severity of UC attack, SA value in the range from 1.2 to 2.2 corresponds to the moderate severity of the attack of the UC, at SA more than 2.2 one should speak of a severe attack of the UC. The model is statistically significant: the Fisher test F = 439.9; p < 0.0001, multiple R = 0.981; R2 = 0.961. The diagnostic sensitivity of this mathematical model was 98.4%, specificity - 96.7%. The index of the severity of the attack, calculated by the formula proposed by us, is able to differentiate the mild, moderate and severe SA, including the isolation of patients with a stage of remission. In this case, the method is non-traumatic, has a low cost and high sensitivity and specificity., Competing Interests: The authors declare no conflict of interest.