Capabilities of computer analysis of breath sounds in patients with COVID-19

Objective. To develop methods for a rapid distance computer diagnosis of COVID-19 based on the analysis of breath sounds. It is known that changes in breath sounds can be the indicators of respiratory organs diseases. Computer analysis of these sounds can indicate their typical changes caused by COVID-19, and can be used for a rapid preliminary diagnosis of this disease. Materials and methods. The method of fast Fourier transform (FFT) was used for computer analysis of breath sounds, recorded near the mouth of 14 COVID-19 patients (aged 1880 years) and 17 healthy volunteers (aged 548 years). The frequency of breath sound records ranged from 44 to 96 kHz. Unlike the conventional methods of computer analysis for diagnosis of diseases based on respiratory sound studying, we offer to test a high-frequency part of FFT (20006000 kHz). Results. While comparing the breath sound FFT in patients and healthy volunteers, we developed the methods for COVID-19 computer diagnosis and determined the numerical criteria in patients and healthy persons. These criteria do not depend on sex and age of the examined persons. Conclusions. The offered computer methods based on the analysis of breath sound FFT in patients and volunteers permit to diagnose COVID -19 with relatively high diagnostic parameters. These methods can be used in development of noninvasive means for preliminary self-express diagnosis of COVID-19.

System for computer diagnosis of hereditary diseases

В работе описаны принципы создания компьютерной консультативной системы поддержки принятия решений при диагностике наследственных заболеваний на долабораторном этапе. Прототип реализуется на примере клинических проявлений лизосомных болезней накопления. В основу системы положены литературные данные, которые сопровождаются оценками экспертов. Разрабатываемая интеллектуальная система формирует ограниченный дифференциально-диагностический ряд гипотез. Это обеспечивает помощь врачу-генетику в дифференциальной диагностике как при наличии у него предварительного диагноза, так и при отсутствии у него предположений о нозологии заболевания. The paper describes the principles of creating a computer consultative decision support system for the diagnosis of hereditary diseases at the pre-laboratory stage. The prototype is implemented on the example of the clinical manifestations of lysosomal storage diseases. The system is based on literary data, which are accompanied by expert assessments. The developed intellectual system forms a limited differential diagnostic row of hypotheses. It provides assistance to the geneticist in differential diagnosis both in the presence of a preliminary diagnosis and in the absence of assumptions about the nosology of the disease.

Medical data preprocessing for increased selectivity of diagnosis

In this review, we present a framework that will enable us to obtain increased accuracy of computer diagnosis in medical patient checkups. To some extent, a new proposition for medical data analysis has been built based on medical data preprocessing. The result of such preprocessing is transformation of medical data from descriptive, semantic form into parameterized math form. A proper model for digging of hidden medical data properties is presented as well. Exploration of hidden data properties achieved by means of preprocessing creates new possibilities for medical data interpretation. Diagnosis selectivity has been increased by means of parameterized illnesses patterns in medical databases.