The article presents current literature on proteomic profiling and the role of proteomic technologies in the diagnosis of various obstetric and gynecological diseases. Proteomic analysis is a promising research method, as it allows for a comprehensive study of protein expression and its regulation in the biological systems, which opens up new opportunities for a more in-depth and detailed study of the etiology and pathogenesis, as well as timely diagnosis and treatment of obstetric and gynecological pathology.

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Эндометриоз-патологический процесс, характеризующийся образованием эктопических очагов стромальной и железистой ткани эндометрия [25, 29, 33, 34, 45, 47, 53]. Генитальный эндометриоз является одним из наиболее распространенных пролиферативных заболеваний, частота которого у женщин репродуктивного возраста колеблется от 7 до 59%, что связано с разными методами диагностики и верификации [1, 11, 21, 38]. Несмотря на большое количество теорий, посвященных изучению причин развития данного заболевания, механизмы его патогенеза до настоящего времени остаются до конца не изученными [3]. В последнее время активно изучается роль ангиогенеза в патогенезе эндометриоза. В работах отечественных и зарубежных авторов показана связь между степенью васкуляризации, особенностями перитонеального микроокружения, уровнем цитокинов и факторов роста в перитонеальной жидкости у больных эндометриозом и пролиферативной активностью клеток эндометриоидных гетеротопий [5, 19, 23-25]. Функционирование органов и тканей в физиологических и патологических условиях зависит от регу

Introduction. Endometriosis is a difficult-to-diagnose pathology due to the diversity of clinical manifestations and the lack of high-precision markers necessary for rapid noninvasive diagnosis and timely administration of pathogenetically justified treatment.The aim of this work was to develop a computer system that allows us to assess the probability of endometriosis with various localizations in women, based on artificial neural networks.Material and Methods. The neural network mathematical models were constructed and tested based on data from 110 patients with morphologically pre-confirmed endometriosis. Patients were divided into training and test samples. The models were built based on anamnestic data and results of proteomic and enzyme immunoassays in blood plasma samples.Results and Discussion. In the course of the study, four mathematical models of neural networks were constructed to predict the presence or absence of endometriosis in a woman and its localization if present. Based on these mathematical models, a computer system “Differential diagnosis of endometriosis” was developed. This system allowed to assess the probability and localization of endometriosis in a patient based on parameters obtained as a result of neural network training.Conclusion. The developed computer diagnostic system allowed predicting the presence of endometriosis and its localization with a probability over 80%, depending on the predicted localization, based on data about the patient and the results of her examination. This system may be used for differential diagnosis of endometriosis from other diseases of the female reproductive system, as well as for differential diagnosis of various endometriosis localizations.

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