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Number №3, 2023

China as a supplier of medical equipment in the Russian Federation. Options for cooperation and features of working with Chinese suppliers

Number №2, 2023

Experience in teaching telemedicine in the system of higher professional education The attitude of medical workers to telemedicine technologies

Number №4, 2022

Physician burnout: the hidden healthcare crisis. Results of an online survey of doctors

Number №3, 2022

Interaction of clinical and diagnostic medicine. Results of an online survey of doctors

Number №2, 2022

Mobile applications for mental health self-management: a review of customers’ opinions Ultrasound robots: ready-to-use solutions and perspective directions

Number №1, 2022

Digital transformation of the pathological service as a way to improve the quality of medical care

Number №4, 2021

Clinical guidelines of the Ministry of Health of the Russian Federation: are doctors ready to follow them? Results of an online survey of doctors.

Number №3, 2021

Виртуальная реальность (VR) в клинической медицине: международный и российский опыт

Number №2, 2021

Дистанционные консультации пациентов: что изменилось за 20 лет?

Number №1, 2021

Experience of participation in the blood pressure telemonitoring pilot project of the Ministry of Healthcare

Number №4, 2020

Автоматизация процесса выявления у беременных заболевания COVID-19

Number №3, 2020

Remote cognitive behavioral therapy for stress disorder associated with the COVID-19 pandemic

Number №2, 2020

Distance education at a medical school during the COVID-19 pandemic: the first experience through the eyes of students

Number №1-2, 2018

Ответственность при использовании телемедицины: врач или юрист
Скрининг меланомы: искусственный интеллект, mHealth и теледерматология

Number №3, 2018 год

II Всероссийский форум по телемедицине, цифровизации здравоохранения и медицинскому маркетингу «ТЕЛЕМЕДФОРУМ 2019»
Эффективность телемедицинских консультаций «пациент-врач»
Телереабилитация: рандомизированное исследование исходов

Number №1-2, 2019

Роль искусственного интеллекта в медицине
Информационная система поддержки принятия врачебных решений

Number №1, 2020

Technologies for continuous monitoring of blood pressure: prospects for practical application
Telemedicine technologies in the Chinese army

Number №2, 2017

Primary telemedicine consultation "patient-doctor": first systematization of methodology

Number №1, 2017

1. A systematic review of using Internet messengers in telemedicine
2. Telemedicine and social networks in the fight against drug addiction

Number №1, 2016

1. The Experience of the Telehealth Network of Minas Gerais, Brazil
2. The Remote Monitoring of Patients with Congestive Heart Failure:The Organizational
Impact..

Number №1, 2015

Teleassessment for diagnosis and treatment in urology
Efficiency of telemedicine at the northern regions Russian Federation A.L. Tsaregorodtsev

Abstract in Russian /

Article in Russian

Number №2, 2020 - page 22-29

Deep machine learning (artificial intelligence) in ultrasound diagnostics DOI: 10.29188/2542-2413-2020-6-2-22-29

For citation: Lebedev G.S., Maslyukov A.P., Shaderkin I.A., Shaderkina A.I. Deep machine learning (artificial intelligence) in ultrasound diagnostics. Journal of Telemedicine and E-Health 2020;(2):22-29

Lebedev G.S., Shaderkin I.A., Shaderkina A.I., Maslyukov A.P.

Lebedev G.S. – Dr. Sc., professor, Head of the Department of Information and Internet Technologies at Sechenov University; Head of the Department of Innovative Development and Scientific Design of the Central Research Institute of Organization and Informatization of Health Care of the Ministry of Health of the Russian Federation
Maslyukov A.P. - 5th year student of the Medical School «Medicine of the Future» of the First Moscow State Medical University them Sechenov (Sechenov University)
Shaderkin I.A. – PhD, Head of the Laboratory of Electronic Health, Institute of Digital Medicine, Sechenov University, ORCID 0000-0001-8669-2674
Shaderkina A.I. – 1st year student Institute of Clinical Medicine of the First Moscow State Medical University them I.M. Sechenov (Sechenov University), ORCID 0000-0003-0639-3274

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Aim of the review is to analyze methods of machine learning (artificial intelligence – AI) in ultrasound investigation and to search solutions connected with the methodology.

Materials and methods. Authors conducted careful analysis of literature using e-library, Pubmed, Schoolar Google, IEEE and analyzed materials published on this theme from more than 106 journals and conferences (Med Imaging, Cell Biochem Biophys, International Journal of Advanced Computer Science and Applications, Journal of Medical Systems etc), and from this materials were selected only 36 which described application of machine learning for ultrasound.

Results. We showed opportunities of application of machine learning methods in detection of different organs diseases such as breast tumor, thyroid nodules, focal lesions of liver, cardiovascular system diseases. In all these cases САО showed quite successful results, on a par and sometimes better than skilled clinicians’ ones.

Conclusions. Application of САО in ultrasonic investigation has several complications such as bad quality of images, artifacts, few images for analyzing. For solving these problems preliminary image processing with artifacts remove, data augmentation, developing a data base is used. Nevertheless, currently there are huge differences in amount and modality of typesetting used in different researches, therefore it is hard to fairly estimate their capacity. For solving of this problem developing of standardized database is required. It should become the aim of further researches.

Conflict of interest. The author declare no conflict of interest