Your browser doesn't support javascript.
loading
Mostrar: 20 | 50 | 100
Resultados 1 - 20 de 248
Filtrar
7.
Br J Anaesth ; 125(3): 412-413, 2020 09.
Artigo em Inglês | MEDLINE | ID: mdl-32861403

RESUMO

This article has been retracted: please see Elsevier Policy on Article Withdrawal (https://www.elsevier.com/about/our-business/policies/article-withdrawal). This article has been retracted at the request of the Editor-in-Chief, Professor Hugh Hemmings, based on the recommendations of Justus-Liebig-University Giessen following an internal review of research conducted by Joachim Boldt at the University. This is further described in 'Further Retractions of Articles by Joachim Boldt', https://doi.org/10.1016/j.bja.2020.02.024.

8.
Radiologe ; 60(5): 375, 2020 05.
Artigo em Alemão | MEDLINE | ID: mdl-32342152
9.
Radiologe ; 60(1): 1-5, 2020 01.
Artigo em Alemão | MEDLINE | ID: mdl-31942670
11.
Radiologe ; 59(6): 501-502, 2019 Jun.
Artigo em Alemão | MEDLINE | ID: mdl-31197400
12.
Radiologe ; 59(5): 406-407, 2019 May.
Artigo em Alemão | MEDLINE | ID: mdl-31065748
13.
Radiologe ; 59(2): 93-94, 2019 Feb.
Artigo em Alemão | MEDLINE | ID: mdl-30710212
15.
Radiologe ; 58(Suppl 1): 1-6, 2018 Nov.
Artigo em Inglês | MEDLINE | ID: mdl-29922965

RESUMO

Machine learning is rapidly gaining importance in radiology. It allows for the exploitation of patterns in imaging data and in patient records for a more accurate and precise quantification, diagnosis, and prognosis. Here, we outline the basics of machine learning relevant for radiology, and review the current state of the art, the limitations, and the challenges faced as these techniques become an important building block of precision medicine. Furthermore, we discuss the roles machine learning can play in clinical routine and research and predict how it might change the field of radiology.


Assuntos
Aprendizado de Máquina , Radiologia , Humanos , Medicina de Precisão
17.
J Mater Chem B ; 6(39): 6245-6261, 2018 Oct 21.
Artigo em Inglês | MEDLINE | ID: mdl-32254615

RESUMO

Cell mechanical measurements are gaining increasing interest in biological and biomedical studies. However, there are no standardized calibration particles available that permit the cross-comparison of different measurement techniques operating at different stresses and time-scales. Here we present the rational design, production, and comprehensive characterization of poly-acrylamide (PAAm) microgel beads mimicking size and overall mechanics of biological cells. We produced mono-disperse beads at rates of 20-60 kHz by means of a microfluidic droplet generator, where the pre-gel composition was adjusted to tune the beads' elasticity in the range of cell and tissue relevant mechanical properties. We verified bead homogeneity by optical diffraction tomography and Brillouin microscopy. Consistent elastic behavior of microgel beads at different shear rates was confirmed by AFM-enabled nanoindentation and real-time deformability cytometry (RT-DC). The remaining inherent variability in elastic modulus was rationalized using polymer theory and effectively reduced by sorting based on forward-scattering using conventional flow cytometry. Our results show that PAAm microgel beads can be standardized as mechanical probes, to serve not only for validation and calibration of cell mechanical measurements, but also as cell-scale stress sensors.

19.
Radiologe ; 57(10): 801-803, 2017 Oct.
Artigo em Alemão | MEDLINE | ID: mdl-29149361
SELEÇÃO DE REFERÊNCIAS
DETALHE DA PESQUISA
...