6 research outputs found
ΠΠ²Π΅Π΄Π΅Π½ΠΈΠ΅ Π·Π°ΠΏΡΠ΅ΡΠ° Π½Π° ΠΏΠΎΡΡΠ΅Π±Π»Π΅Π½ΠΈΠ΅ ΡΠ°Π±Π°ΠΊΠ° Π² ΠΌΠ΅Π΄ΠΈΡΠΈΠ½ΡΠΊΠΈΡ ΡΡΡΠ΅ΠΆΠ΄Π΅Π½ΠΈΡΡ : ΠΎΡΠ΅Π½ΠΊΠ° Π³ΠΎΡΠΎΠ²Π½ΠΎΡΡΠΈ
Summary. Within the framework of a programme on imposition of ban on tobacco consumption in 4 healthcare facilities at Moscow, healthcare workers were questioned in order to evaluate their adherence to this intervention. Totally, 715 healthcare workers were questioned. The questionnaire included different items regarding tobacco consumption, medical aid for smoking cessation, and a ban on tobacco consumption. Due to a high prevalence of smoking among healthcare workers, insufficient knowledge on harm of tobacco smoking and inadequate promptness for imposition of a ban on tobacco consumption, principal ways to increase the readiness for imposition of a ban on tobacco consumption have been outlined.Π Π΅Π·ΡΠΌΠ΅. Π ΡΠ°ΠΌΠΊΠ°Ρ
ΠΏΡΠΎΠ³ΡΠ°ΠΌΠΌΡ ΠΏΠΎ Π²Π²Π΅Π΄Π΅Π½ΠΈΡ Π·Π°ΠΏΡΠ΅ΡΠ° Π½Π° ΠΏΠΎΡΡΠ΅Π±Π»Π΅Π½ΠΈΠ΅ ΡΠ°Π±Π°ΠΊΠ° Π² ΠΌΠ΅Π΄ΠΈΡΠΈΠ½ΡΠΊΠΈΡ
ΡΡΡΠ΅ΠΆΠ΄Π΅Π½ΠΈΡΡ
ΡΡΠ΅Π΄ΠΈ ΠΏΠ΅ΡΡΠΎΠ½Π°Π»Π° 4 ΠΌΠ΅Π΄ΠΈΡΠΈΠ½ΡΠΊΠΈΡ
ΡΡΡΠ΅ΠΆΠ΄Π΅Π½ΠΈΠΉ ΠΠΎΡΠΊΠ²Ρ Π±ΡΠ» ΠΏΡΠΎΠ²Π΅Π΄Π΅Π½ ΠΎΠΏΡΠΎΡ Ρ ΡΠ΅Π»ΡΡ ΠΎΡΠ΅Π½ΠΊΠΈ Π³ΠΎΡΠΎΠ²Π½ΠΎΡΡΠΈ ΠΊ Π²Π²Π΅Π΄Π΅Π½ΠΈΡ Π·Π°ΠΏΡΠ΅ΡΠ°. ΠΡΠ΅Π³ΠΎ Π² ΠΎΠΏΡΠΎΡΠ΅ ΠΏΡΠΈΠ½ΡΠ»ΠΈ ΡΡΠ°ΡΡΠΈΠ΅ 715 ΠΌΠ΅Π΄ΠΈΡΠΈΠ½ΡΠΊΠΈΡ
ΡΠ°Π±ΠΎΡΠ½ΠΈΠΊΠΎΠ². ΠΡΠ»ΠΈ Π·Π°ΡΡΠΎΠ½ΡΡΡ ΡΠ°Π·Π»ΠΈΡΠ½ΡΠ΅ Π²ΠΎΠΏΡΠΎΡΡ, ΡΠ²ΡΠ·Π°Π½Π½ΡΠ΅ Ρ ΡΠΏΠΎΡΡΠ΅Π±Π»Π΅Π½ΠΈΠ΅ΠΌ ΡΠ°Π±Π°ΠΊΠ°, ΠΎΠΊΠ°Π·Π°Π½ΠΈΠ΅ΠΌ ΠΏΠΎΠΌΠΎΡΠΈ Π² ΠΎΡΠΊΠ°Π·Π΅ ΠΎΡ ΠΊΡΡΠ΅Π½ΠΈΡ ΠΈ Π²Π²Π΅Π΄Π΅Π½ΠΈΠ΅ΠΌ Π·Π°ΠΏΡΠ΅ΡΠ° Π½Π° ΡΠΏΠΎΡΡΠ΅Π±Π»Π΅Π½ΠΈΠ΅ ΡΠ°Π±Π°ΠΊΠ°. Π ΡΠ²ΡΠ·ΠΈ c Π²ΡΡΠ²Π»Π΅Π½Π½ΠΎΠΉ Π²ΡΡΠΎΠΊΠΎΠΉ ΡΠ°ΡΠΏΡΠΎΡΡΡΠ°Π½Π΅Π½Π½ΠΎΡΡΡΡ ΠΊΡΡΠ΅Π½ΠΈΡ ΡΡΠ΅Π΄ΠΈ ΠΌΠ΅Π΄ΠΈΡΠΈΠ½ΡΠΊΠΈΡ
ΡΠ°Π±ΠΎΡΠ½ΠΈΠΊΠΎΠ², Π½Π΅Π΄ΠΎΡΡΠ°ΡΠΎΡΠ½ΡΠΌ ΡΡΠΎΠ²Π½Π΅ΠΌ Π·Π½Π°Π½ΠΈΠΉ ΠΎ Π²ΡΠ΅Π΄Π΅ ΡΠ°Π±Π°ΠΊΠ° ΠΈ Π½Π΅Π΄ΠΎΡΡΠ°ΡΠΎΡΠ½ΠΎΠΉ Π³ΠΎΡΠΎΠ²Π½ΠΎΡΡΡΡ ΠΊ Π²Π²Π΅Π΄Π΅Π½ΠΈΡ Π·Π°ΠΏΡΠ΅ΡΠ° Π½Π° ΠΏΠΎΡΡΠ΅Π±Π»Π΅Π½ΠΈΠ΅ ΡΠ°Π±Π°ΠΊΠ° Π² ΠΌΠ΅Π΄ΠΈΡΠΈΠ½ΡΠΊΠΈΡ
ΡΡΡΠ΅ΠΆΠ΄Π΅Π½ΠΈΡΡ
Π±ΡΠ»ΠΈ Π²ΡΠ΄Π΅Π»Π΅Π½Ρ ΠΎΡΠ½ΠΎΠ²Π½ΡΠ΅ Π½Π°ΠΏΡΠ°Π²Π»Π΅Π½ΠΈΡ Π΄Π»Ρ ΡΠ°Π±ΠΎΡΡ ΠΏΠΎ ΠΏΠΎΠ²ΡΡΠ΅Π½ΠΈΡ ΡΡΠΎΠ²Π½Ρ Π³ΠΎΡΠΎΠ²Π½ΠΎΡΡΠΈ ΠΊ Π²Π²Π΅Π΄Π΅Π½ΠΈΡ Π·Π°ΠΏΡΠ΅ΡΠ°
Π‘ΠΎΠ·Π΄Π°Π½ΠΈΠ΅ ΠΊΠΎΠ»Π»Π΅ΠΊΡΠΈΠΈ ΠΠ‘ΠΠ’-ΠΈΠ·ΠΎΠ±ΡΠ°ΠΆΠ΅Π½ΠΈΠΉ ΠΈ ΠΊΠ»ΠΈΠ½ΠΈΡΠ΅ΡΠΊΠΈΡ Π΄Π°Π½Π½ΡΡ ΠΏΡΠΈ ΠΎΡΡΡΡΡ Π½Π°ΡΡΡΠ΅Π½ΠΈΡΡ ΠΌΠΎΠ·Π³ΠΎΠ²ΠΎΠ³ΠΎ ΠΊΡΠΎΠ²ΠΎΠΎΠ±ΡΠ°ΡΠ΅Π½ΠΈΡ
Background The use of neuroimaging methods is an integral part of the process of assisting patients with acute cerebrovascular events (ACVE), and computed tomography (CT) is the Β«gold standardΒ» for examining this category of patients. The capabilities of the analysis of CT images may be significantly expanded with modern methods of machine learning including the application of the principles of radiomics. However, since the use of these methods requires large arrays of DICOM (Digital Imaging and Communications in Medicine)-images, their implementation into clinical practice is limited by the lack of representative sample sets. Inaddition, at present, collections (datasets) of CT images of stroke patients, that are suitable for machine learning, are practically not available in the public domain.Aim of study Regarding the aforesaid, the aim of this work was to create a DICOM images dataset of native CT and CT-angiography of patients with different types of stroke. Material and meth ods The collection was based on the medical cases of patients hospitalized in the Regional Vascular Center of the N.V. Sklifosovsky Research Institute for Emergency Medicine. We used a previously developed specialized platform to enter clinical data on the stroke cases, to attach CT DICOMimages to each case, to contour 3D areas of interest, and to tag (label) them. A dictionary was developed for tagging, where elements describe the type of lesion, location, and vascular territory.Results A dataset of clinical cases and images was formed in the course of the work. It included anonymous information about 220 patients, 130 of them with ischemic stroke, 40 with hemorrhagic stroke, and 50 patients without cerebrovascular disorders. Clinical data included information about type of stroke, presence of concomitant diseases and complications, length of hospital stay, methods of treatment, and outcome. The results of 370 studies of native CT and 102 studies of CT-angiography were entered for all patients. The areas of interest corresponding to direct and indirect signs of stroke were contoured and tagged by radiologists on each series of images.Conclusion The resulting collection of images will enable the use of various methods of data analysis and machine learning in solving the most important practical problems including diagnosis of the stroke type, assessment of lesion volume, and prediction of the degree of neurological deficit.ΠΠΊΡΡΠ°Π»ΡΠ½ΠΎΡΡΡ ΠΡΠΈΠΌΠ΅Π½Π΅Π½ΠΈΠ΅ ΠΌΠ΅ΡΠΎΠ΄ΠΎΠ² Π½Π΅ΠΉΡΠΎΠ²ΠΈΠ·ΡΠ°Π»ΠΈΠ·Π°ΡΠΈΠΈ ΡΠ²Π»ΡΠ΅ΡΡΡ Π½Π΅ΠΎΡΡΠ΅ΠΌΠ»Π΅ΠΌΠΎΠΉ ΡΠ°ΡΡΡΡ ΠΏΡΠΎΡΠ΅ΡΡΠ° ΠΎΠΊΠ°Π·Π°Π½ΠΈΡ ΠΏΠΎΠΌΠΎΡΠΈ Π±ΠΎΠ»ΡΠ½ΡΠΌ Ρ ΠΎΡΡΡΡΠΌΠΈ Π½Π°ΡΡΡΠ΅Π½ΠΈΡΠΌΠΈ ΠΌΠΎΠ·Π³ΠΎΠ²ΠΎΠ³ΠΎ ΠΊΡΠΎΠ²ΠΎΠΎΠ±ΡΠ°ΡΠ΅Π½ΠΈΡ (ΠΠΠΠ), ΠΏΡΠΈ ΡΡΠΎΠΌ Π·ΠΎΠ»ΠΎΡΡΠΌ ΡΡΠ°Π½Π΄Π°ΡΡΠΎΠΌ ΠΎΠ±ΡΠ»Π΅Π΄ΠΎΠ²Π°Π½ΠΈΡ Π΄Π°Π½Π½ΠΎΠΉ ΠΊΠ°ΡΠ΅Π³ΠΎΡΠΈΠΈ Π±ΠΎΠ»ΡΠ½ΡΡ
ΡΠ²Π»ΡΠ΅ΡΡΡ ΠΊΠΎΠΌΠΏΡΡΡΠ΅ΡΠ½Π°Ρ ΡΠΎΠΌΠΎΠ³ΡΠ°ΡΠΈΡ (ΠΠ’). ΠΠ½Π°ΡΠΈΡΠ΅Π»ΡΠ½ΠΎ ΡΠ°ΡΡΠΈΡΠΈΡΡ Π²ΠΎΠ·ΠΌΠΎΠΆΠ½ΠΎΡΡΠΈ Π°Π½Π°Π»ΠΈΠ·Π° ΠΠ’-ΠΈΠ·ΠΎΠ±ΡΠ°ΠΆΠ΅Π½ΠΈΠΉ Π²ΠΎΠ·ΠΌΠΎΠΆΠ½ΠΎ Ρ ΠΏΠΎΠΌΠΎΡΡΡ ΡΠΎΠ²ΡΠ΅ΠΌΠ΅Π½Π½ΡΡ
ΠΌΠ΅ΡΠΎΠ΄ΠΎΠ² ΠΌΠ°ΡΠΈΠ½Π½ΠΎΠ³ΠΎ ΠΎΠ±ΡΡΠ΅Π½ΠΈΡ, Π² ΡΠΎΠΌ ΡΠΈΡΠ»Π΅ Π½Π° ΠΎΡΠ½ΠΎΠ²Π΅ ΠΏΡΠΈΠΌΠ΅Π½Π΅Π½ΠΈΡ ΠΏΡΠΈΠ½ΡΠΈΠΏΠΎΠ² ΡΠ°Π΄ΠΈΠΎΠΌΠΈΠΊΠΈ. ΠΠ΄Π½Π°ΠΊΠΎ, ΡΠ°ΠΊ ΠΊΠ°ΠΊ ΠΈΡΠΏΠΎΠ»ΡΠ·ΠΎΠ²Π°Π½ΠΈΠ΅ ΡΡΠΈΡ
ΠΌΠ΅ΡΠΎΠ΄ΠΎΠ² ΡΡΠ΅Π±ΡΠ΅Ρ Π½Π°Π»ΠΈΡΠΈΡ Π±ΠΎΠ»ΡΡΠΈΡ
ΠΌΠ°ΡΡΠΈΠ²ΠΎΠ² DICOM (Digital Imaging and Communications in Medicine)-ΠΈΠ·ΠΎΠ±ΡΠ°ΠΆΠ΅Π½ΠΈΠΉ, ΠΈΡ
Π²Π½Π΅Π΄ΡΠ΅Π½ΠΈΠ΅ Π² ΠΊΠ»ΠΈΠ½ΠΈΡΠ΅ΡΠΊΡΡ ΠΏΡΠ°ΠΊΡΠΈΠΊΡ ΠΎΠ³ΡΠ°Π½ΠΈΡΠ΅Π½ΠΎ ΠΏΡΠΎΠ±Π»Π΅ΠΌΠΎΠΉ Π½Π°Π±ΠΎΡΠ° ΡΠ΅ΠΏΡΠ΅Π·Π΅Π½ΡΠ°ΡΠΈΠ²Π½ΡΡ
Π²ΡΠ±ΠΎΡΠΎΠΊ. ΠΡΠΎΠΌΠ΅ ΡΠΎΠ³ΠΎ, Π² Π½Π°ΡΡΠΎΡΡΠ΅Π΅ Π²ΡΠ΅ΠΌΡ Π² ΠΎΡΠΊΡΡΡΠΎΠΌ Π΄ΠΎΡΡΡΠΏΠ΅ ΠΏΡΠ°ΠΊΡΠΈΡΠ΅ΡΠΊΠΈ Π½Π΅ ΠΏΡΠ΅Π΄ΡΡΠ°Π²Π»Π΅Π½Ρ ΠΊΠΎΠ»Π»Π΅ΠΊΡΠΈΠΈ, ΡΠΎΠ΄Π΅ΡΠΆΠ°ΡΠΈΠ΅ ΠΠ’-ΠΈΠ·ΠΎΠ±ΡΠ°ΠΆΠ΅Π½ΠΈΡ Π±ΠΎΠ»ΡΠ½ΡΡ
c ΠΠΠΠ, ΠΊΠΎΡΠΎΡΡΠ΅ Π±ΡΠ»ΠΈ Π±Ρ ΠΏΡΠΈΠ³ΠΎΠ΄Π½Ρ Π΄Π»Ρ ΠΌΠ°ΡΠΈΠ½Π½ΠΎΠ³ΠΎ ΠΎΠ±ΡΡΠ΅Π½ΠΈΡ.Π¦Π΅Π»Ρ Π ΡΠ²ΡΠ·ΠΈ Ρ Π²ΡΡΠ΅ΡΠΊΠ°Π·Π°Π½Π½ΡΠΌ, ΡΠ΅Π»ΡΡ Π΄Π°Π½Π½ΠΎΠΉ ΡΠ°Π±ΠΎΡΡ ΡΠ²Π»ΡΠ»ΠΎΡΡ ΡΠΎΠ·Π΄Π°Π½ΠΈΠ΅ ΠΊΠΎΠ»Π»Π΅ΠΊΡΠΈΠΈ DICOM-ΠΈΠ·ΠΎΠ±ΡΠ°ΠΆΠ΅Π½ΠΈΠΉ Π½Π°ΡΠΈΠ²Π½ΠΎΠΉ ΠΠ’ ΠΈ ΠΠ’-Π°Π½Π³ΠΈΠΎΠ³ΡΠ°ΡΠΈΠΈ Ρ ΠΏΠ°ΡΠΈΠ΅Π½ΡΠΎΠ² Ρ ΡΠ°Π·Π»ΠΈΡΠ½ΡΠΌΠΈ ΡΠΈΠΏΠ°ΠΌΠΈ ΠΠΠΠ.ΠΠ°ΡΠ΅ΡΠΈΠ°Π» ΠΈ ΠΌΠ΅ΡΠΎΠ΄Ρ ΠΡΠ½ΠΎΠ²ΠΎΠΉ Π΄Π»Ρ ΡΠΎΠ·Π΄Π°Π½ΠΈΡ ΠΊΠΎΠ»Π»Π΅ΠΊΡΠΈΠΈ ΡΡΠ°Π»ΠΈ ΠΈΡΡΠΎΡΠΈΠΈ Π±ΠΎΠ»Π΅Π·Π½ΠΈ ΠΏΠ°ΡΠΈΠ΅Π½ΡΠΎΠ², Π³ΠΎΡΠΏΠΈΡΠ°Π»ΠΈΠ·ΠΈΡΠΎΠ²Π°Π½Π½ΡΡ
Π² ΡΠ΅Π³ΠΈΠΎΠ½Π°Π»ΡΠ½ΡΠΉ ΡΠΎΡΡΠ΄ΠΈΡΡΡΠΉ ΡΠ΅Π½ΡΡ ΠΠΠ Π‘Π ΠΈΠΌ. Π.Π. Π‘ΠΊΠ»ΠΈΡΠΎΡΠΎΠ²ΡΠΊΠΎΠ³ΠΎ. ΠΠ»Ρ ΡΠΎΡΠΌΠΈΡΠΎΠ²Π°Π½ΠΈΡ ΠΊΠΎΠ»Π»Π΅ΠΊΡΠΈΠΈ ΠΈΡΠΏΠΎΠ»ΡΠ·ΠΎΠ²Π°Π»Π°ΡΡ ΡΠ°Π·ΡΠ°Π±ΠΎΡΠ°Π½Π½Π°Ρ Π½Π°ΠΌΠΈ ΡΠ°Π½Π΅Π΅ ΡΠΏΠ΅ΡΠΈΠ°Π»ΠΈΠ·ΠΈΡΠΎΠ²Π°Π½Π½Π°Ρ ΠΏΠ»Π°ΡΡΠΎΡΠΌΠ°, ΠΏΠΎΠ·Π²ΠΎΠ»ΡΡΡΠ°Ρ Π²Π²ΠΎΠ΄ΠΈΡΡ ΠΊΠ»ΠΈΠ½ΠΈΡΠ΅ΡΠΊΠΈΠ΅ Π΄Π°Π½Π½ΡΠ΅ ΠΎ ΡΠ»ΡΡΠ°ΡΡ
ΠΠΠΠ, ΠΏΡΠΈΠΊΡΠ΅ΠΏΠ»ΡΡΡ ΠΊ ΠΊΠ°ΠΆΠ΄ΠΎΠΌΡ ΡΠ»ΡΡΠ°Ρ DICOM-ΠΈΠ·ΠΎΠ±ΡΠ°ΠΆΠ΅Π½ΠΈΡ ΠΏΡΠΎΠ²Π΅Π΄Π΅Π½Π½ΡΡ
ΠΈΡΡΠ»Π΅Π΄ΠΎΠ²Π°Π½ΠΈΠΉ, Π° ΡΠ°ΠΊΠΆΠ΅ ΠΎΠΊΠΎΠ½ΡΡΡΠΈΠ²Π°ΡΡ ΠΈ ΡΠ΅Π³ΠΈΡΠΎΠ²Π°ΡΡ (ΡΠ°Π·ΠΌΠ΅ΡΠ°ΡΡ) 3D-ΠΎΠ±Π»Π°ΡΡΠΈ ΠΈΠ½ΡΠ΅ΡΠ΅ΡΠ°. ΠΠ»Ρ ΡΠ΅Π³ΠΈΡΠΎΠ²Π°Π½ΠΈΡ Π±ΡΠ» ΡΠ°Π·ΡΠ°Π±ΠΎΡΠ°Π½ ΡΠ»ΠΎΠ²Π°ΡΡ, ΡΠ»Π΅ΠΌΠ΅Π½ΡΡ ΠΊΠΎΡΠΎΡΠΎΠ³ΠΎ ΠΎΠΏΠΈΡΡΠ²Π°ΡΡ ΡΠΈΠΏ ΠΏΠ°ΡΠΎΠ»ΠΎΠ³ΠΈΡΠ΅ΡΠΊΠΎΠ³ΠΎ ΠΎΠ±ΡΠ°Π·ΠΎΠ²Π°Π½ΠΈΡ, Π»ΠΎΠΊΠ°Π»ΠΈΠ·Π°ΡΠΈΡ ΠΈ Π±Π°ΡΡΠ΅ΠΉΠ½ ΠΊΡΠΎΠ²ΠΎΡΠ½Π°Π±ΠΆΠ΅Π½ΠΈΡ.Π Π΅Π·ΡΠ»ΡΡΠ°ΡΡ Π Ρ
ΠΎΠ΄Π΅ ΡΠ°Π±ΠΎΡΡ Π±ΡΠ»Π° ΡΡΠΎΡΠΌΠΈΡΠΎΠ²Π°Π½Π° ΠΊΠΎΠ»Π»Π΅ΠΊΡΠΈΡ ΠΊΠ»ΠΈΠ½ΠΈΡΠ΅ΡΠΊΠΈΡ
ΡΠ»ΡΡΠ°Π΅Π² ΠΈ ΠΈΠ·ΠΎΠ±ΡΠ°ΠΆΠ΅Π½ΠΈΠΉ, Π²ΠΊΠ»ΡΡΠ°ΡΡΠ°Ρ Π°Π½ΠΎΠ½ΠΈΠΌΠΈΠ·ΠΈΡΠΎΠ²Π°Π½Π½ΡΡ ΠΈΠ½ΡΠΎΡΠΌΠ°ΡΠΈΡ ΠΎ 220 ΠΏΠ°ΡΠΈΠ΅Π½ΡΠ°Ρ
, ΠΈΠ· Π½ΠΈΡ
130 - Ρ ΠΈΡΠ΅ΠΌΠΈΡΠ΅ΡΠΊΠΈΠΌ ΠΈΠ½ΡΡΠ»ΡΡΠΎΠΌ, 40 - Ρ Π³Π΅ΠΌΠΎΡΡΠ°Π³ΠΈΡΠ΅ΡΠΊΠΈΠΌ ΠΈΠ½ΡΡΠ»ΡΡΠΎΠΌ, Π° ΡΠ°ΠΊΠΆΠ΅ 50 ΡΠ΅Π»ΠΎΠ²Π΅ΠΊ Π±Π΅Π· ΡΠ΅ΡΠ΅Π±ΡΠΎΠ²Π°ΡΠΊΡΠ»ΡΡΠ½ΠΎΠΉ ΠΏΠ°ΡΠΎΠ»ΠΎΠ³ΠΈΠΈ. ΠΠ»ΠΈΠ½ΠΈΡΠ΅ΡΠΊΠΈΠ΅ Π΄Π°Π½Π½ΡΠ΅ Π²ΠΊΠ»ΡΡΠ°Π»ΠΈ ΡΠ²Π΅Π΄Π΅Π½ΠΈΡ ΠΎ ΡΠΈΠΏΠ΅ ΠΠΠΠ, Π½Π°Π»ΠΈΡΠΈΠΈ ΡΠΎΠΏΡΡΡΡΠ²ΡΡΡΠΈΡ
Π·Π°Π±ΠΎΠ»Π΅Π²Π°Π½ΠΈΠΉ ΠΈ ΠΎΡΠ»ΠΎΠΆΠ½Π΅Π½ΠΈΠΉ, Π΄Π»ΠΈΡΠ΅Π»ΡΠ½ΠΎΡΡΠΈ Π³ΠΎΡΠΏΠΈΡΠ°Π»ΠΈΠ·Π°ΡΠΈΠΈ, ΡΠΏΠΎΡΠΎΠ±Π΅ Π»Π΅ΡΠ΅Π½ΠΈΡ ΠΈ ΠΈΡΡ
ΠΎΠ΄Π΅. ΠΡΠ΅Π³ΠΎ Π΄Π»Ρ ΠΏΠ°ΡΠΈΠ΅Π½ΡΠΎΠ² Π±ΡΠ»ΠΈ Π²Π²Π΅Π΄Π΅Π½Ρ ΡΠ΅Π·ΡΠ»ΡΡΠ°ΡΡ 370 ΠΈΡΡΠ»Π΅Π΄ΠΎΠ²Π°Π½ΠΈΠΉ Π½Π°ΡΠΈΠ²Π½ΠΎΠΉ ΠΠ’ ΠΈ 102 ΠΈΡΡΠ»Π΅Π΄ΠΎΠ²Π°Π½ΠΈΡ ΠΠ’-Π°Π½Π³ΠΈΠΎΠ³ΡΠ°ΡΠΈΠΈ. ΠΠ° ΠΊΠ°ΠΆΠ΄ΠΎΠΉ ΡΠ΅ΡΠΈΠΈ ΠΈΠ·ΠΎΠ±ΡΠ°ΠΆΠ΅Π½ΠΈΠΉ Π²ΡΠ°ΡΠΎΠΌ-ΡΠΊΡΠΏΠ΅ΡΡΠΎΠΌ Π±ΡΠ»ΠΈ ΠΎΠΊΠΎΠ½ΡΡΡΠ΅Π½Ρ ΠΈ ΠΏΡΠΎΡΠ΅Π³ΠΈΡΠΎΠ²Π°Π½Ρ ΠΎΠ±Π»Π°ΡΡΠΈ ΠΈΠ½ΡΠ΅ΡΠ΅ΡΠ°, ΡΠΎΠΎΡΠ²Π΅ΡΡΡΠ²ΡΡΡΠΈΠ΅ ΠΏΡΡΠΌΡΠΌ ΠΈ ΠΊΠΎΡΠ²Π΅Π½Π½ΡΠΌ ΠΏΡΠΈΠ·Π½Π°ΠΊΠ°ΠΌ ΠΠΠΠ.ΠΡΠ²ΠΎΠ΄ Π‘ΡΠΎΡΠΌΠΈΡΠΎΠ²Π°Π½Π½Π°Ρ ΠΊΠΎΠ»Π»Π΅ΠΊΡΠΈΡ ΠΈΠ·ΠΎΠ±ΡΠ°ΠΆΠ΅Π½ΠΈΠΉ ΠΏΠΎΠ·Π²ΠΎΠ»ΠΈΡ Π² ΠΏΠΎΡΠ»Π΅Π΄ΡΡΡΠ΅ΠΌ ΠΏΡΠΈΠΌΠ΅Π½ΠΈΡΡ ΡΠ°Π·Π»ΠΈΡΠ½ΡΠ΅ ΠΌΠ΅ΡΠΎΠ΄Ρ Π°Π½Π°Π»ΠΈΠ·Π° Π΄Π°Π½Π½ΡΡ
ΠΈ ΠΌΠ°ΡΠΈΠ½Π½ΠΎΠ³ΠΎ ΠΎΠ±ΡΡΠ΅Π½ΠΈΡ Π² ΡΠ΅ΡΠ΅Π½ΠΈΠΈ Π²Π°ΠΆΠ½Π΅ΠΉΡΠΈΡ
ΠΏΡΠ°ΠΊΡΠΈΡΠ΅ΡΠΊΠΈΡ
Π·Π°Π΄Π°Ρ, Π² ΡΠΎΠΌ ΡΠΈΡΠ»Π΅ Π΄ΠΈΠ°Π³Π½ΠΎΡΡΠΈΠΊΠΈ ΡΠΈΠΏΠ° ΠΠΠΠ, ΠΎΡΠ΅Π½ΠΊΠΈ ΠΎΠ±ΡΠ΅ΠΌΠ° ΠΏΠΎΡΠ°ΠΆΠ΅Π½ΠΈΡ, ΠΏΡΠΎΠ³Π½ΠΎΠ·Π° ΡΡΠ΅ΠΏΠ΅Π½ΠΈ Π½Π΅Π²ΡΠΎΠ»ΠΎΠ³ΠΈΡΠ΅ΡΠΊΠΎΠ³ΠΎ Π΄Π΅ΡΠΈΡΠΈΡΠ°
Creation of a Dataset of MSCT-Images and Clinical Data for Acute Cerebrovascular Events
Background The use of neuroimaging methods is an integral part of the process of assisting patients with acute cerebrovascular events (ACVE), and computed tomography (CT) is the Β«gold standardΒ» for examining this category of patients. The capabilities of the analysis of CT images may be significantly expanded with modern methods of machine learning including the application of the principles of radiomics. However, since the use of these methods requires large arrays of DICOM (Digital Imaging and Communications in Medicine)-images, their implementation into clinical practice is limited by the lack of representative sample sets. Inaddition, at present, collections (datasets) of CT images of stroke patients, that are suitable for machine learning, are practically not available in the public domain.Aim of study Regarding the aforesaid, the aim of this work was to create a DICOM images dataset of native CT and CT-angiography of patients with different types of stroke. Material and meth ods The collection was based on the medical cases of patients hospitalized in the Regional Vascular Center of the N.V. Sklifosovsky Research Institute for Emergency Medicine. We used a previously developed specialized platform to enter clinical data on the stroke cases, to attach CT DICOMimages to each case, to contour 3D areas of interest, and to tag (label) them. A dictionary was developed for tagging, where elements describe the type of lesion, location, and vascular territory.Results A dataset of clinical cases and images was formed in the course of the work. It included anonymous information about 220 patients, 130 of them with ischemic stroke, 40 with hemorrhagic stroke, and 50 patients without cerebrovascular disorders. Clinical data included information about type of stroke, presence of concomitant diseases and complications, length of hospital stay, methods of treatment, and outcome. The results of 370 studies of native CT and 102 studies of CT-angiography were entered for all patients. The areas of interest corresponding to direct and indirect signs of stroke were contoured and tagged by radiologists on each series of images.Conclusion The resulting collection of images will enable the use of various methods of data analysis and machine learning in solving the most important practical problems including diagnosis of the stroke type, assessment of lesion volume, and prediction of the degree of neurological deficit
ΠΠΠΠΠ’Π ΠΠΠΠ«Π Π‘ΠΠΠΠ ΠΠ’Π«: ΠΠ¦ΠΠΠΠ ΠΠΠΠΠΠΠ‘ΠΠΠ‘Π’Π Π Π ΠΠ‘ΠΠΠ ΠΠΠ― ΠΠΠΠ ΠΠΠ¬Π―
A new product which is an electronic cigarette acting as nicotine delivery has been marketed at early 2000s. An electronic cigarette generates nicotine aerosol from a solution comprised of several basic substances, nicotine and flavours. The electronic cigarettes were widely promoted by a manufacturer like a safe product substituting tobacco smoking during quitting period. As a result, their consumption has been increasing progressively worldwide. Investigators of the aerosol content reported that it mainly contains ultrafine particles which easily penetrate into alveoli and blood vessels. The aerosol also contains nitrosamines, toxic substances and heavy metals. Strong evidence has been made about the aerosol cytotoxicity that could lead to serious injury and diseases. Nicotine dependence was demonstrated to develop as a result of an electronic cigarette smoking. While smoking an electronic cigarette, the indoor air concentration of toxic substances could reach hazardous levels. Electronic cigarettes do not have any advantage and cannot be considered as a mean to quit tobacco smoking. Moreover, electronic cigarette consumers were shown to quit smoking significantly harder. A large body of scientific confirmation of hazardous effect of electronic cigarettes on a human either during active or passive smoking have been obtained. It is no doubt that further investigations are needed especially with regards to rapid change in the market of electronic cigarettes. To minimize adverse effect of electronic cigarettes both on an individual and on population at whole certain measures should be undertaken intended to limitation of demand and supply of electronic cigarettes in the country similar to those for typical tobacco products.Π Π½Π°ΡΠ°Π»Π΅ 2000-Ρ
Π³ΠΎΠ΄ΠΎΠ² Π½Π° ΡΡΠ½ΠΊΠ΅ ΠΏΠΎΡΠ²ΠΈΠ»ΡΡ Π½ΠΎΠ²ΡΠΉ ΠΏΡΠΎΠ΄ΡΠΊΡ β ΡΠ»Π΅ΠΊΡΡΠΎΠ½Π½ΡΠ΅ ΡΠΈΠ³Π°ΡΠ΅ΡΡ (ΠΠ‘), ΠΏΡΠ΅Π΄Π½Π°Π·Π½Π°ΡΠ΅Π½Π½ΡΠ΅ Π΄Π»Ρ Π΄ΠΎΡΡΠ°Π²ΠΊΠΈ Π½ΠΈΠΊΠΎΡΠΈΠ½Π° Π² ΠΎΡΠ³Π°Π½ΠΈΠ·ΠΌ ΡΠ΅Π»ΠΎΠ²Π΅ΠΊΠ°. ΠΠ»Ρ ΡΡΠΎΠ³ΠΎ Π³Π΅Π½Π΅ΡΠΈΡΡΠ΅ΡΡΡ Π½ΠΈΠΊΠΎΡΠΈΠ½ΡΠΎΠ΄Π΅ΡΠΆΠ°ΡΠΈΠΉ Π°ΡΡΠΎΠ·ΠΎΠ»Ρ ΠΈΠ· ΡΠ°ΡΡΠ²ΠΎΡΠ°, ΡΠΎΡΡΠΎΡΡΠ΅Π³ΠΎ ΠΈΠ· Π½Π΅ΡΠΊΠΎΠ»ΡΠΊΠΈΡ
Π±Π°Π·ΠΎΠ²ΡΡ
Π²Π΅ΡΠ΅ΡΡΠ², Π½ΠΈΠΊΠΎΡΠΈΠ½Π° ΠΈ Π°ΡΠΎΠΌΠ°ΡΠΈΠ·Π°ΡΠΎΡΠΎΠ². ΠΡΠΎΠΈΠ·Π²ΠΎΠ΄ΠΈΡΠ΅Π»ΠΈ Π½Π°ΡΠ°Π»ΠΈ ΡΠΈΡΠΎΠΊΠΎ ΡΠ΅ΠΊΠ»Π°ΠΌΠΈΡΠΎΠ²Π°ΡΡ ΠΠ‘ ΠΊΠ°ΠΊ Π±Π΅Π·ΠΎΠΏΠ°ΡΠ½ΡΠΉ ΠΏΡΠΎΠ΄ΡΠΊΡ ΠΈ ΠΏΡΠΎΠ΄Π²ΠΈΠ³Π°ΡΡ ΠΈΡ
ΠΊΠ°ΠΊ Π·Π°ΠΌΠ΅ΡΡΠΈΡΠ΅Π»ΡΠ½ΠΎΠ΅ ΡΡΠ΅Π΄ΡΡΠ²ΠΎ Π΄Π»Ρ ΡΡΡΠ΅ΠΊΡΠΈΠ²Π½ΠΎΠ³ΠΎ ΠΎΡΠΊΠ°Π·Π° ΠΎΡ ΡΠ°Π±Π°ΠΊΠ°. Π ΡΠ΅Π·ΡΠ»ΡΡΠ°ΡΠ΅ ΡΠ°ΡΠΏΡΠΎΡΡΡΠ°Π½Π΅Π½Π½ΠΎΡΡΡ ΠΠ‘ Π² ΠΌΠΈΡΠ΅ Π½Π΅ΡΠΊΠ»ΠΎΠ½Π½ΠΎ ΡΠ°ΡΡΠ΅Ρ. Π Π°ΡΡΠΎΠ·ΠΎΠ»Π΅ ΡΠΎΠ΄Π΅ΡΠΆΠ°ΡΡΡ Π² ΠΎΡΠ½ΠΎΠ²Π½ΠΎΠΌ ΡΠ»ΡΡΡΠ°ΠΌΠ΅Π»ΠΊΠΈΠ΅ ΡΠ°ΡΡΠΈΡΡ, ΡΠ²ΠΎΠ±ΠΎΠ΄Π½ΠΎ ΠΏΡΠΎΠ½ΠΈΠΊΠ°ΡΡΠΈΠ΅ Π² Π°Π»ΡΠ²Π΅ΠΎΠ»Ρ ΠΈ ΠΊΡΠΎΠ²Π΅Π½ΠΎΡΠ½ΡΡ ΡΠΈΡΡΠ΅ΠΌΡ ΡΠ΅Π»ΠΎΠ²Π΅ΠΊΠ°, Π½ΠΈΡΡΠΎΠ·Π°ΠΌΠΈΠ½Ρ, ΡΡΠ΄ ΡΠΎΠΊΡΠΈΡΠ΅ΡΠΊΠΈΡ
Π²Π΅ΡΠ΅ΡΡΠ², ΡΡΠΆΠ΅Π»ΡΠ΅ ΠΌΠ΅ΡΠ°Π»Π»Ρ, ΡΡΠΎ ΠΏΠΎΠ΄ΡΠ²Π΅ΡΠΆΠ΄Π°Π΅ΡΡΡ ΡΠ΅Π·ΡΠ»ΡΡΠ°ΡΠ°ΠΌΠΈ ΠΈΡΡΠ»Π΅Π΄ΠΎΠ²Π°Π½ΠΈΡ Π΅Π³ΠΎ ΡΠΎΡΡΠ°Π²Π°, ΠΏΠΎΠ»ΡΡΠ΅Π½Ρ ΡΠ°ΠΊΠΆΠ΅ ΡΡΡΠΎΠ³ΠΈΠ΅ Π΄ΠΎΠΊΠ°Π·Π°ΡΠ΅Π»ΡΡΡΠ²Π° ΡΠΈΡΠΎΡΠΎΠΊΡΠΈΡΠ½ΠΎΡΡΠΈ Π°ΡΡΠΎΠ·ΠΎΠ»Ρ, ΡΡΠΎ ΠΌΠΎΠΆΠ΅Ρ ΠΏΡΠΈΠ²ΠΎΠ΄ΠΈΡΡ ΠΊ ΡΠ°Π·Π²ΠΈΡΠΈΡ ΡΠ΅ΡΡΠ΅Π·Π½ΡΡ
ΠΏΠΎΠ²ΡΠ΅ΠΆΠ΄Π΅Π½ΠΈΠΉ ΠΈ Π±ΠΎΠ»Π΅Π·Π½Π΅ΠΉ ΡΠ΅Π»ΠΎΠ²Π΅ΠΊΠ°. ΠΠΎΠ΄ΡΠ²Π΅ΡΠΆΠ΄Π΅Π½ΠΎ, ΡΡΠΎ ΠΏΡΠΈ ΠΊΡΡΠ΅Π½ΠΈΠΈ ΠΠ‘ ΠΌΠΎΠΆΠ΅Ρ ΡΠ°Π·Π²ΠΈΠ²Π°ΡΡΡΡ Π½ΠΈΠΊΠΎΡΠΈΠ½ΠΎΠ²Π°Ρ Π·Π°Π²ΠΈΡΠΈΠΌΠΎΡΡΡ. Π£ΡΡΠ°Π½ΠΎΠ²Π»Π΅Π½ΠΎ, ΡΡΠΎ ΠΏΡΠΈ ΠΊΡΡΠ΅Π½ΠΈΠΈ ΠΠ‘ Π² ΠΏΠΎΠΌΠ΅ΡΠ΅Π½ΠΈΠΈ ΠΊΠΎΠ½ΡΠ΅Π½ΡΡΠ°ΡΠΈΡ ΡΠΎΠΊΡΠΈΡΠ΅ΡΠΊΠΈΡ
Π²Π΅ΡΠ΅ΡΡΠ² Π΄ΠΎΡΡΠΈΠ³Π°Π΅Ρ ΠΎΠΏΠ°ΡΠ½ΠΎΠ³ΠΎ Π΄Π»Ρ Π·Π΄ΠΎΡΠΎΠ²ΡΡ ΡΠ΅Π»ΠΎΠ²Π΅ΠΊΠ° ΡΡΠΎΠ²Π½Ρ. ΠΠ‘ Π½Π΅ ΠΎΠ±Π»Π°Π΄Π°ΡΡ Π½ΠΈΠΊΠ°ΠΊΠΈΠΌ ΠΏΡΠ΅ΠΈΠΌΡΡΠ΅ΡΡΠ²ΠΎΠΌ ΠΈ Π½Π΅ ΡΠ²Π»ΡΡΡΡΡ ΡΡΡΠ΅ΠΊΡΠΈΠ²Π½ΡΠΌ ΡΡΠ΅Π΄ΡΡΠ²ΠΎΠΌ Π΄Π»Ρ ΠΎΡΠΊΠ°Π·Π° ΠΎΡ ΡΠ°Π±Π°ΠΊΠΎΠΊΡΡΠ΅Π½ΠΈΡ, Π±ΠΎΠ»Π΅Π΅ ΡΠΎΠ³ΠΎ, Π΄ΠΎΠΊΠ°Π·Π°Π½ΠΎ, ΡΡΠΎ ΠΏΠΎΡΡΠ΅Π±ΠΈΡΠ΅Π»ΡΠΌ ΠΠ‘ Π·Π½Π°ΡΠΈΡΠ΅Π»ΡΠ½ΠΎ ΡΡΡΠ΄Π½Π΅Π΅ Π±ΡΠΎΡΠΈΡΡ ΠΊΡΡΠΈΡΡ. ΠΠΌΠ΅ΡΡΡΡ Π΄ΠΎΡΡΠ°ΡΠΎΡΠ½ΠΎ Π²Π΅ΡΠΊΠΈΠ΅ Π½Π°ΡΡΠ½ΡΠ΅ Π΄ΠΎΠΊΠ°Π·Π°ΡΠ΅Π»ΡΡΡΠ²Π° Π²ΡΠ΅Π΄Π½ΠΎΠ³ΠΎ Π²ΠΎΠ·Π΄Π΅ΠΉΡΡΠ²ΠΈΡ ΠΠ‘ Π½Π° ΠΎΡΠ³Π°Π½ΠΈΠ·ΠΌ ΡΠ΅Π»ΠΎΠ²Π΅ΠΊΠ° ΠΊΠ°ΠΊ ΠΏΡΠΈ Π°ΠΊΡΠΈΠ²Π½ΠΎΠΌ, ΡΠ°ΠΊ ΠΈ ΠΏΡΠΈ ΠΏΠ°ΡΡΠΈΠ²Π½ΠΎΠΌ ΠΊΡΡΠ΅Π½ΠΈΠΈ. ΠΠ΅Π·ΡΡΠ»ΠΎΠ²Π½ΠΎ, ΠΈΡΡΠ»Π΅Π΄ΠΎΠ²Π°Π½ΠΈΡ Π΄ΠΎΠ»ΠΆΠ½Ρ Π±ΡΡΡ ΠΏΡΠΎΠ΄ΠΎΠ»ΠΆΠ΅Π½Ρ, Ρ. ΠΊ. ΡΡΠ½ΠΎΠΊ ΠΠ‘ Π±ΡΡΡΡΠΎ ΠΈΠ·ΠΌΠ΅Π½ΡΠ΅ΡΡΡ. ΠΠ»Ρ ΠΌΠΈΠ½ΠΈΠΌΠΈΠ·Π°ΡΠΈΠΈ Π½Π΅Π³Π°ΡΠΈΠ²Π½ΠΎΠ³ΠΎ ΠΈΠ½Π΄ΠΈΠ²ΠΈΠ΄ΡΠ°Π»ΡΠ½ΠΎΠ³ΠΎ ΠΈ ΠΏΠΎΠΏΡΠ»ΡΡΠΈΠΎΠ½Π½ΠΎΠ³ΠΎ Π²Π»ΠΈΡΠ½ΠΈΡ ΠΠ‘ ΡΠ»Π΅Π΄ΡΠ΅Ρ ΠΏΡΠΈΠ½ΡΡΡ ΡΡΠ΄ ΠΌΠ΅Ρ, Π½Π°ΠΏΡΠ°Π²Π»Π΅Π½Π½ΡΡ
Π½Π° ΡΠΎΠΊΡΠ°ΡΠ΅Π½ΠΈΠ΅ ΠΈΡ
ΡΠΏΡΠΎΡΠ° ΠΈ ΠΏΡΠ΅Π΄Π»ΠΎΠΆΠ΅Π½ΠΈΡ, ΠΊΠ°ΠΊ ΡΡΠΎ Π±ΡΠ»ΠΎ ΠΏΡΠΈΠ½ΡΡΠΎ Π² ΠΎΡΠ½ΠΎΡΠ΅Π½ΠΈΠΈ ΡΡΠ°Π΄ΠΈΡΠΈΠΎΠ½Π½ΡΡ
ΡΠ°Π±Π°ΡΠ½ΡΡ
ΠΈΠ·Π΄Π΅Π»ΠΈΠΉ