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Use of artificial intelligence technologies in laboratory medicine, their effectiveness and application scenarios: a systematic review
Vasilev Y., Nanova O., Vladzymyrskyy A., Goldberg A., Blokhin I., Reshetnikov R.
Prospects of machine learning applications in affective disorders
Mosolova E., Alfimov A., Kostyukova E., Mosolov S.
Modern capabilities of artificial intelligence technologies in cardiovascular imaging
Islamgulov A., Bogdanova A., Sufiiarov D., Chernyavskaya A., Bairakaeva E., Maksimova A., Nemychnikov N., Bikieva D., Shakhmaeva A., Burdina L., Bolekhan A., Akimov E., Shurakova Z.
Assessment of ovarian follicular reserve according to ultrasound data based on machine learning methods
Laputin F., Sidorov I., Moshkin A.
Development of a prognostic model for diagnosis of prostate cancer based on radiomics of biparametric magnetic resonance imaging apparent diffusion coefficient maps and stacking of machine learning algorithms
Kuznetsov A.
Learning radiologists’ annotation styles with multi-annotator labeling for improved neural network performance
Nikitin E.
Radiomics in application to diseases of the musculoskeletal system: a review
Pleshkov M., Zamyshevskaya M., Kuchinskii E., Jin X., Zhang J., Zavadovskaya V., Zorkaltsev M., Kim T., Pogonchenkova D., Udodov V., Tolmachev I.
Artificial intelligence in ultrasound of thyroid nodules, prognosis of I-131 uptake
Manaev A., Trukhin A., Zakharova S., Sheremeta M., Troshina E.
Machine-learning technology for predicting intraocular lens power: Diagnostic data generalization
Arzamastsev A., Fabrikantov O., Zenkova N., Belikov S.
Digital diagnostics: A computer application for lymph node metastases in cervical cancer
Kuznetsov A.
Machine learning techniques for breast cancer diagnosis
Dyomin K., Germashev I.
Machine-learning and artificial neural network technologies in the classification of postkeratotomic corneal deformity
Tsyrenzhapova E., Rozanova O., Iureva T., Ivanov A., Rozanov I.
The concept of responsible artificial intelligence as the future of artificial intelligence in medicine
Germanov N.
Diagnostic accuracy of artificial intelligence for the screening of prostate cancer in biparametric magnetic resonance imaging: a systematic review
Kryuchkova O., Schepkina E., Rubtsova N., Alekseev B., Kuznetsov A., Epifanova S., Zarya E., Talyshinskii A.
Classification of optical coherence tomography images using deep machine-learning methods
Arzamastsev A., Fabrikantov O., Kulagina E., Zenkova N.
Evolution of research and development in the field of artificial intelligence technologies for healthcare in the Russian Federation: results of 2021
Gusev A., Vladzymyrskyy A., Sharova D., Arzamasov K., Khramov A.
Use of artificial intelligence in the diagnosis of arterial calcification
Trusov Y., Chupakhina V., Nurkaeva A., Yakovenko N., Ablenina I., Latypova R., Pitke A., Yazovskih A., Ivanov A., Bogatyreva D., Popova U., Yuzlekbaev A.
MosMedData: data set of 1110 chest CT scans performed during the COVID-19 epidemic
Morozov S., Andreychenko A., Blokhin I., Gelezhe P., Gonchar A., Nikolaev A., Pavlov N., Chernina V., Gombolevskiy V.
Using neural networks for non-invasive determination of glycated hemoglobin levels, illustrated by the application of an innovative portable glucometer in clinical practice
Poliker E., Koshechkin K., Timokhin A., Klyukina E., Belyakova E., Brovko A., Lalayan A., Ermolaeva A.
Dosiomics in the analysis of medical images and prospects for its use in clinical practice
Solodkiy V., Nudnov N., Ivannikov M., Shakhvalieva E., Sotnikov V., Smyslov A.
Predicting atrial fibrillation in comorbid patients with arterial hypertension and chronic obstructive pulmonary disease using laboratory research methods: a machine learning approach
Kazantseva E., Ivannikov A., Tarzimanova A., Podzolkov V.
Development of a portable spectrophotometer using artificial neural networks for non-invasive determination of glycated hemoglobin in blood by Raman spectroscopy
Poliker E., Zemskikh B., Koshechkin K.
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