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BackgroundRespiratory sounds have been recognized as a possible indicator of behavior and health. Computer analysis of these sounds can indicate of characteristic sound changes caused by COVID-19 and can be used for diagnosis of this illness.PurposeThe communication aim is development of fast remote computer-assistance diagnosis of COVID-19, based on analysis of respiratory sounds.Materials and MethodsFast Fourier transform (FFT) was applied for analyses of respiratory sounds recorded near the mouth of 9 COVID-19 patients and 4 healthy volunteers. Sampling rate was 48 kHz.ResultsComparing of FFT spectrums of the respiratory sounds of the patients and volunteers we proposed numerical healthy-ill criterions.ConclusionsThe proposed computer method, based on analysis of the FFT spectrums of respiratory sounds of the patients and volunteers, allows one to automatically diagnose COVID-19 with sufficiently high diagnostic values. This method can be applied at development of noninvasive self-testing kits for COVID-19.