11 research outputs found
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°
List of lists-annotated (LOLA): A database for annotation and comparison of published microarray gene lists
Microarray profiling of RNA expression is a powerful tool that generates large lists of transcripts that are potentially relevant to a disease or treatment. However, because the lists of changed transcripts are embedded in figures and tables, they are typically inaccessible for search engines. Due to differences in gene nomenclatures, the lists are difficult to compare between studies. LOLA (Lists of Lists Annotated) is an internet-based database for comparing gene lists from microarray studies or other genomic-scale methods. It serves as a common platform to compare and reannotate heterogeneous gene lists from different microarray platforms or different genomic methodologies such as serial analysis of gene expression (SAGE) or proteomics. LOLA (www.lola.gwu.edu) provides researchers with a means to store, annotate, and compare gene lists produced from different studies or different analyses of the same study. It is especially useful in identifying potentially high interest genes which are reported as significant across multiple studies and species. Its application to the fields of stem cell, cancer, and aging research is demonstrated by comparing published papers. Β© 2005 Elsevier B.V. All rights reserved
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
Inheritance of mutations in the V2 receptor gene in thirteen families with nephrogenic diabetes insipidus
Case-study of a user-driven prosthetic arm design: bionic hand versus customized body-powered technology in a highly demanding work environment
BACKGROUND: Prosthetic arm research predominantly focuses on "bionic" but not body-powered arms. However, any research orientation along user needs requires sufficiently precise workplace specifications and sufficiently hard testing. Forensic medicine is a demanding environment, also physically, also for non-disabled people, on several dimensions (e.g., distances, weights, size, temperature, time). METHODS: As unilateral below elbow amputee user, the first author is in a unique position to provide direct comparison of a "bionic" myoelectric iLimb Revolution (Touch Bionics) and a customized body-powered arm which contains a number of new developments initiated or developed by the user: (1) quick lock steel wrist unit; (2) cable mount modification; (3) cast shape modeled shoulder anchor; (4) suspension with a soft double layer liner (Ohio Willowwood) and tube gauze (Molnlycke) combination. The iLimb is mounted on an epoxy socket; a lanyard fixed liner (Ohio Willowwood) contains magnetic electrodes (Liberating Technologies). An on the job usage of five years was supplemented with dedicated and focused intensive two-week use tests at work for both systems. RESULTS: The side-by-side comparison showed that the customized body-powered arm provides reliable, comfortable, effective, powerful as well as subtle service with minimal maintenance; most notably, grip reliability, grip force regulation, grip performance, center of balance, component wear down, sweat/temperature independence and skin state are good whereas the iLimb system exhibited a number of relevant serious constraints. CONCLUSIONS: Research and development of functional prostheses may want to focus on body-powered technology as it already performs on manually demanding and heavy jobs whereas eliminating myoelectric technology's constraints seems out of reach. Relevant testing could be developed to help expediting this. This is relevant as Swiss disability insurance specifically supports prostheses that enable actual work integration. Myoelectric and cosmetic arm improvement may benefit from a less forgiving focus on perfecting anthropomorphic appearance