MEDICAL DIAGNOSTICS BY MICROSTRUCTURAL ANALYSIS OF BIOLOGICAL LIQUID DRIED PATTERNS AS A PROBLEM OF BIOINFORMATICS

• Main Authors: Petr Vladimirovich Lebedev-Stepanov, Dr., Marina Eduardovna Buzoverya, Dr., Irina Shishpor, Konstantin Olegovich Vlasov, Yulia Pavlovna Potekhina, Prof.
• Format: Article
• Language: English
• Published: Cifra Publishing House 2018-02-01
• Series: Journal of Bioinformatics and Genomics
• Online Access: http://journal-biogen.org/article/view/78

Motivation: It is important to develop the high-precision computerized methods for medical rapid diagnostic which is generalizing the unique clinical experience obtained in the past decade as specialized solutions for diagnostic problems of control of specific diseases and, potentially, for a wide health monitoring of virtually healthy population, identify the reserves of human health and take the actions to prevent of these reserves depletion. In this work we present one of the new directions in bioinformatics, i.e. medical diagnostics by automated expert system on basis of morphology analysis of digital image of biological liquid dried pattern. Results: Proposed method is combination of bioinformatics and biochemistry approaches for obtaining diagnostic information from a morphological analysis of standardized dried patterns of biological liquid sessile drop. We have carried out own research in collaboration with medical diagnostic centers and formed the electronic database for recognition the following types of diseases: candidiasis; neoplasms; diabetes mellitus; diseases of the circulatory system; cerebrovascular disease; diseases of the digestive system; diseases of the genitourinary system; infectious diseases; factors relevant to the work; factors associated with environmental pollution; factors related to lifestyle. The laboratory setup for diagnostics of the human body in pathology states is developed. The diagnostic results are considered. Availability: Access to testing the software can be obtained on request to the contact email below.