Персона: Проничев, Александр Николаевич
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Инженерно-физический институт биомедицины
Цель ИФИБ и стратегия развития – это подготовка высококвалифицированных кадров на базе передовых исследований и разработок новых перспективных методов и материалов в области инженерно-физической биомедицины. Занятие лидерских позиций в биомедицинских технологиях XXI века и внедрение их в образовательный процесс, что отвечает решению практикоориентированной задачи мирового уровня – диагностике и терапии на клеточном уровне социально-значимых заболеваний человека.
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Александр Николаевич
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- ПубликацияОткрытый доступСПОСОБ ДИАГНОСТИКИ ОНКОЛОГИЧЕСКОГО ЗАБОЛЕВАНИЯ КРОВИ(НИЯУ МИФИ, 2023) Никитаев, В. Г.; Проничев, А. Н.; Нагорнов, О. В.; Тупицын, Н. Н.; Сельчук, В. Ю.; Дмитриева, В. В.; Палладина, А. Д.; Поляков, Е. В.; Поляков, Евгений Валерьевич; Проничев, Александр Николаевич; Никитаев, Валентин Григорьевич; Нагорнов, Олег Викторович; Дмитриева, Валентина ВикторовнаИзобретение относится к области медицины и может быть использовано для диагностики минимальной остаточной болезни (МОБ) или минимальной резидуальной болезни (МРБ, Minimal residual diseases) - популяции опухолевых клеток, оставшейся в организме после достижения клинико-гематологической ремиссии (количество бластных клеток в миелограмме менее 5%) и острого лейкоза. Предлагается способ диагностики онкологического заболевания крови, заключающийся в проведении микроскопического анализа мазков периферической крови для определения формулы крови; проведении микроскопического анализа мазков костного мозга для получения изображений клеток костного мозга, распознавание клеток костного мозга путем сравнения их с образцовыми изображениями клеток и построение миелограммы; выполнении анализа костного мозга с применением цитохимических маркерных реакций на гранулоцитарный и моноцитарный ряды гемопоэза и иммунофенотипического исследования с помощью проточной лазерной цитофлюорометрии, в котором используется набор диагностических антител для определения направленности дифференцировки клеток и установления стадии созревания бластов и сопоставления полученных результатов микроскопического анализа костного мозга, формулы крови и миелограммы с результатами ранее выполняемых исследований, хранящихся в базе данных, для диагностики заболевания. Изобретение обеспечивает повышение точности выявления и диагностики онкологического заболевания крови. 3 ил.
- ПубликацияТолько метаданныеNevi in children (Part 1) epidermal nevi: Clinical picture, diagnosis, treatment(2020) Tamrazova, O. B.; Sergeev, V. Y.; Sergeev, Y. Y.; Nikitaev, V. G.; Pronichev, A. N.; Никитаев, Валентин Григорьевич; Проничев, Александр Николаевич© INRA and Springer-Verlag France 2015.Nevi are congenital formations that appear on the skin from birth or in early childhood, are very common in healthy children and, as a rule, are harmless. The article deals with epidermal nevi formed from epidermal cells. Particular attention is paid to the syndromes of epidermal nevi, which are characterized by a combination of skin rashes with systemic manifestations. Correct diagnosis of different subtypes of nevi, their differential diagnosis with other pigment formations (including melanomas) and non-melanoma skin cancer, as well as the recognition of non-uniform syndromes will help to determine the pediatrician correct tactics of management of patients, further counseling and assess the prognosis of the disease. Early diagnosis using dermatoscopy and modern techniques based on artificial intelligence is most significant in children before the development of progressive symptoms or neurological disorders. In the detection of epidermal nevus syndromes, consultations of related specialists (neurologists, traumatologists, cardiologists, etc.) are recommended.
- ПубликацияТолько метаданныеMolecular Oncology Diagnosis: A System for Processing Data from Biochips Based on Field Effect Nanotransistors(2020) Malsagova, K. A.; Pleshakova, T. O.; Romanova, T. S.; Valueva, A. A.; Nikitaev, V. G.; Pronichev, A. N.; Hamadi, K. I.; Druzhinina, E. A.; Никитаев, Валентин Григорьевич; Проничев, Александр Николаевич© 2020, Springer Science+Business Media, LLC, part of Springer Nature.This article discusses a system for the molecular diagnosis of diseases at the early stages based on biochips using field effect nanotransistors. Practical questions relating to data processing to avoid signal distortion are addressed, as well as problems of signal visualization.
- ПубликацияТолько метаданныеAFM-MS for Protein Analysis of Plasma Samples of Patients with Ovarian Cancer(2019) Kaysheva, A. L.; Pleshakova, T. O.; Malsagova, K. A.; Chingin, K.; Pronichev, A. N.; Nikitaev, V. G.; Ivanov, E. O.; Проничев, Александр Николаевич; Никитаев, Валентин ГригорьевичAn atomic force microscope (AFM) is a molecular detector that allows the recording of individual proteins and protein complexes on the surface of an atomically flat substrate, the AFM chip. Registration of target proteins is carried out after the fishing procedure - catching out of proteins from the volume of the analyzed solution to a surface of a small area (sensory zone of the chip) modified by affinity reagents against the target protein. The use of the procedure of biospecific enrichment makes it possible to effectively concentrate the molecules of the target proteins in an amount sufficient for the subsequent mass spectrometric analysis for early diagnosis of ovarian cancer in blood samples.
- ПубликацияТолько метаданныеAutomated Analysis of the Pigment Network in Dermatoscopic Images of Melanocytic Skin Tumors(2019) Tamrazova, O. B.; Sergeev, V. Y.; Sergeev, Y. Y.; Nikitaev, V. G.; Pronichev, A. N.; Kozyreva, A. V.; Polyakov, E. V.; Druzhinina, E. A.; Никитаев, Валентин Григорьевич; Проничев, Александр Николаевич; Поляков, Евгений Валерьевич© 2019, Springer Science+Business Media, LLC, part of Springer Nature.A method for recognition of the pigment network lines in dermatoscopic images of skin tumors is presented. The method provides calculation of characteristics of the pigment network lines and imaging of the obtained results. Experimental assessment of the effectiveness of the proposed method showed it to be promising for use in melanoma recognition systems.
- ПубликацияТолько метаданныеA Model for Detecting Structural Elements – Lines – in Digital Images in Oncodermatology(2021) Tamrazova, O. B.; Sergeev, V. Y.; Nikitaev, V. G.; Pronichev, A. N.; Otchenashenko, A. I.; Druzhinina, E. A.; Kozyreva, A. V.; Solomatin, M. A.; Kozlov, V. S.; Никитаев, Валентин Григорьевич; Проничев, Александр Николаевич; Отченашенко, Александр Иванович; Соломатин, Михаил Андреевич; Козлов, Владимир Сергеевич© 2021, Springer Science+Business Media, LLC, part of Springer Nature.The problem of early diagnosis of one of the most dangerous malignant neoplasms of the skin, melanoma, is considered. A model for detecting structural elements (lines) in digital images of skin neoplasms in oncodermatology has been developed. The model is based on adaptive binarization of the initial digital dermatoscopy image of skin les neoplasms ions and subsequent operations of dilation, erosion, skeletonization, and filtration of false line fragments. Test dermatoscopy images of skin neoplasms were visually divided into four groups to conduct the experiment. Optimal parameters of image processing of four groups for the model of detecting structural elements – lines – have been experimentally established. The experimentally determined accuracy of line detection was 95%. This research is the result of interdisciplinary cooperation of dermatologists of the Central Medical Academy of the Administrative Department of the President of the Russian Federation, the Medical Institute of the Russian Peoples’ Friendship University and experts in the field of information and measurement systems of the Engineering and Physical Institute of Biomedicine of the National Research Nuclear University “MEPhI”. The proposed model can be used in the development of computer systems to support medical decision-making in the diagnosis of skin melanoma – a dangerous malignant neoplasm.
- ПубликацияТолько метаданныеModel of a Decision-Making System for the Diagnosis of Melanoma Using Artificial Intelligence(2021) Tamrazova, O. B.; Sergeev, V. Y.; Nikitaev, V. G.; Pronichev, A. N.; Druzhinina, E. A.; Medvedeva, O. A.; Solomatin, M. A.; Никитаев, Валентин Григорьевич; Проничев, Александр Николаевич; Соломатин, Михаил Андреевич© 2021, Springer Science+Business Media, LLC, part of Springer Nature.Interdisciplinary approaches to creating high-tech computer systems for the diagnosis of melanoma using artificial intelligence are presented. A model is proposed for the architecture of an interactive expert system. This includes a set of features for a contemporary medical algorithm (the Kittler algorithm) along with a knowledge base and a diagnosis evaluation score for the case under study.
- ПубликацияТолько метаданныеAlgorithm for the Analysis of Pigment Network Characteristics in Diagnosing Melanoma(2021) Tamrazova, O. B.; Sergeev, V. Y.; Nikitaev, V. G.; Pronichev, A. N.; Druzhinina, E. A.; Никитаев, Валентин Григорьевич; Проничев, Александр Николаевич© 2021, Pleiades Publishing, Ltd.Abstract: An algorithm for analyzing the characteristics of the pigment network of skin neoplasms is proposed. It is based on the assessment of the deviation coefficient of the average lengths of the pigment network segments in the local areas of the neoplasm from the average value of the lengths of the pigment network segments throughout the area of the neoplasm. The use of the algorithm makes it possible to distinguish the typical pigment network from an atypical one. An atypical pigment network is a core feature in identifying early melanoma. The algorithm can be used in automated systems to support medical decision-making in the diagnosis of skin neoplasms.
- ПубликацияТолько метаданныеDetection of Circles as Structural Elements in Dermatoscopic Images of Skin Neoplasms in the Diagnosis of Melanoma(2021) Tamrazova, O. B.; Sergeev, V. Y.; Nikitaev, V. G.; Pronichev, A. N.; Medvedeva, O. A.; Kozlov, V. S.; Solomatin, M. A.; Никитаев, Валентин Григорьевич; Проничев, Александр Николаевич; Козлов, Владимир Сергеевич; Соломатин, Михаил Андреевич© 2021, Springer Science+Business Media, LLC, part of Springer Nature.A method for recognizing “circles”, significant structural elements of skin neoplasms, has been proposed. An RDS-2 dermatoscope has been used for imaging. Special software has been developed to implement the proposed method for circle recognition. The results of experimental detection of circles are presented. The developed method can be used in diagnostic systems for detecting skin melanoma, a dangerous form of cancer.
- ПубликацияТолько метаданныеDiagnostic performance study on the melanoma automated diagnosis software powered by artificial intelligence technologies(2020) Sergeev, V. Yu.; Nikitaev, V. G.; Pronichev, A. N.; Никитаев, Валентин Григорьевич; Проничев, Александр Николаевич; Тамразова, О. Б.; Сергеев, Ю. Ю.INTRODUCTION: The research evaluates a series of publications on the machine recognition efficacy of cutaneous melanoma dermatoscopic images. Some authors report high sensitivity and specificity of automated diagnostics of skin tumors. Significant differences in the published data can be attributed to the use of different algorithms and groups of skin neoplasms to calculate the accuracy rate. MATERIALS AND METHODS: The diagnostic performance of two automated artificial intelligence systems is compared. RESULTS: The convolutional neural network algorithm improves the overall diagnostic accuracy by 7% compared to the algorithm without deep learning, while the overall accuracy rate was 78%. An initial set of 100 dermatoscopic images used in the study is published online for the assessment of the applicability of the obtained data when introducing existing artificial intelligence systems. CONCLUSION: The main limitations and possible ways to further improve the automated diagnosis of skin tumors based on digital dermatoscopy are outlined.