Telemedicine
  • 2018 № 2 «Cross-reporting» as a way for sub-speciality teleradiology

    There is an original telemedicine­based approach for a sub­speciality reporting of a radiology examinations at primary level facilities. This approach allows to increase quality of «neuroradiology» reporting at children. The level of discrepancies decreased from 42,0% to 20,0%.

    Authors: Morozov S. P. [7] Vladzymyrskyy A. V. [7] Ledikhova N. V. [4] Kuzmina E. S. [2]

    Tags: distant peer-review1 out-patient hospital1 primary care2 radiology4 telemedicine17 teleradiology4

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  • 2018 № 4 Expert Telemedicine Consultations in Radiology Service of Moscow

    In 2017 Moscow Research and Practical Center of Medical Radiology conducted 2678 expert telemedicine consultations (i.e., TMC) following the requests from the radiology departments in Moscow municipal hospitals. A system-integrated base for distance consultations is called Unified Radiological Information Service (i.e., URIS). Expert TMCs were performed based on their modality, i.e. 52% (1386 cases) for MR imaging, 47% (1257 cases) for CT, 1% (35 cases) for X-ray imaging. The vast majority (i.e., 96.7% cases) of all applications were received from municipal outpatient clinics, providing healthcare assistance to the adult population. 3.2% of cases of expert TMC were conducted on an urgent basis. For the first time we defined the average demand for expert TMC results of radiology studies to be equal to 4,8 teleconsultations for 1,000 CTs, 17,42 – for 1 CT scanner, 8,9 – for 1,000 MRI, 26,98 – for 1 MRI scanner

    Authors: Morozov S. P. [7] Vladzymyrskyy A. V. [7] Ledikhova N. V. [4] Kuzmina E. S. [2]

    Tags: outpatient hospital1 primary care2 radiology4 telemedicine17 teleradiology4 uris1

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  • Diagnostic systems
  • 2017 № 3 From PACS to teleradiology

    This article is about the current situation of the distribution and exchange of medical images. Today PACS is familiar to medical facilities and it is widely used system in radiology departments, but the “teleradiology” term still has difficulties with interpretation. When digital medical images are printing on a film and then transfer to other specialists, this way of exchange becomes more and more expensive and inefficient. That’s why the problem of acquisition, dis- tribution and getting access to the results of the diagnostic studies still actual. Another problem is the lack of qualified radiologists in the regions of Russia, due to the uneven population density; competent specialists are concentrated in regional centers. Therefore performing of urgent remote consultation is an actual problem too. In this article have been defined perspectives of advancement of the teleradiological systems, that began to appear due to PACS implementa- tion in radiology departments. The purpose of this scientific research is to develop the methods of teleradiology systems organization for consultations using PACS and telemedicine networks created in the regions. Factors influencing the quality of teleradiology consultations were discussed in article. Prerequisites for the development and implementation of such socially important systems, as a regional teleradiology system were defined

    Authors: Koshkarov A. A. [8] Dubrovin A. V. [2]

    Tags: dicom2 medical imaging1 pacs2 radiology4 remote consultation1 ris2 telemedicine17 teleradiology4

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  • Artificial intelligence in health care
  • 2020 № 4 Moscow experiment on computer vision in radiology: involvement and participation of radiologists

    B a c k g r o u n d . In 2019, the Moscow Government decided to conduct a large-scale scientific research – an the Experiment on the use of innovative computer vision technologies for medical image analysis and subsequent applicability in the healthcare system of Moscow (www.mosmed.ai).
    O b j e c t i v e – analyze engagement, attitudes and feedback from doctors-radiologists in frame of the Experiment.
    M a t e r i a l s a n d m e t h o d s . The Experiment is a prospective research approved by the Independent Ethics Committee and registered with Clinicaltrails.gov (ID NCT04489992). Patients signed informed voluntary consent. On the date 01.10.2020, ten services are involved in the Experiment, they providing automated analysis of chest computed tomography and x-ray, mammography. The study includes quantitative indicators of the Experiment from 06/18/2020 to 10/01/2020. Methods of social survey, descriptive statistics, assessment of diagnostic accuracy metrics were used.
    R e s u l t s a n d d i s c u s s i o n . During the first four months of the active phase of the Experiment, ten computer vision services were integrate into Unified Radiology Service of Moscow. More then 497 thousand studies have been successfully analyzed. Analyzes is carried out for 884 diagnostic devices in 293 medical organizations, 272 of them are actively involved. The involvement of medical organizations is 82%. The median time for automatic analysis of 1 study is 8 minutes. Overall, 63% of studies were analyzed in less than 15 minutes. At the beginning of the Experiment, 538 doctors had access to the system; in four months this number increased to 899. The involvement of doctors was 24%, which is slightly higher than the global indicators. According to the results of a sociological survey, the attitude to AI technologies of Moscow radiologists can be characterize as expectant, moderately optimistic. Radiologists
    have determined that the results of computer vision services are fully consistent with the real situation in 64% of cases. In 36% cases some inconsistencies were recorded; of this number, significant discrepancies took place in 6%, insignificant – in 23%.
    C o n c l u s i o n . Results of the Experiment’s first four months can be consider as successful. A high level of involvement of radiologists is define. Special measures will be implement to increase the involvement of radiologists, as well as a comprehensive comparative assessment of the work of services at the further stages of the Experiment.

    Authors: Morozov S. P. [7] Vladzymyrskyy A. V. [7] Ledikhova N. V. [4] Andrejchenko A. E. [1] Arzamasov K. M. [1] Balanjuk E. A. [1] Gombolevskij V. A. [1] Ermolaev S. O. [1] Zhivodenko V. S. [1] Idrisov I. M. [1] Kirpichev Ju. S. [1] Logunova T. A. [1] Nuzhdina V. A. [1] Omeljanskaja O. V. [1] Rakovchen V. G. [1] Slepushkina A. V. [1]

    Tags: artificial intelligence10 chest1 computed tomography2 computer vision1 malignant tumor1 mammography2 radiography1 radiology4

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