Abstract
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.
Highlights
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
us to assess the probability of endometriosis with various localizations
Patients were divided into training and test samples
Summary
Исследование соответствует этическим стандартам, разработанным в соответствии с Хельсинкской декларацией Всемирной медицинской ассоциации «Этические принципы проведения научных медицинских исследований с участием человека» с поправками 2000 г. и «Правилами клинической практики в Российской Федерации», утвержденными Приказом Министерства здравоохранения Российской Федерации от 19.06.2003 г. Исследование соответствует этическим стандартам, разработанным в соответствии с Хельсинкской декларацией Всемирной медицинской ассоциации «Этические принципы проведения научных медицинских исследований с участием человека» с поправками 2000 г. И «Правилами клинической практики в Российской Федерации», утвержденными Приказом Министерства здравоохранения Российской Федерации от 19.06.2003 г. No 266, и было одобрено локальным этическим комитетом Красноярского государственного медицинского университета имени профессора В.Ф. Войно-Ясенецкого Министерства здравоохранения Российской Федерации (протокол No 71 от 28 сентября 2016 г.). В ходе исследования осуществлялось построение четырех математических моделей нейронной сети: для прогнозирования наличия эндометриоза в целом, а также для прогнозирования его различных локализаций – аденомиоза, эндометриоза яичников и перитонеального эндометриоза. Характеристика пациенток по наличию эндометриоза, а также его отдельных локализаций представлена в таблице 1. Характеристика пациенток по наличию эндометриоза, а также его отдельных локализаций Table 1. Characteristics of patients by the presence and individual localizations of endometriosis
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