ABSTRACT
This article has been retracted: please see Elsevier Policy on Article Withdrawal (https://www.elsevier.com/about/our-business/policies/article-withdrawal). This article has been retracted at the request of the Editor-in-Chief, Professor Hugh Hemmings, based on the recommendations of Justus-Liebig-University Giessen following an internal review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.
ABSTRACT
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.
Subject(s)
Machine Learning , Radiology , Humans , Precision MedicineABSTRACT
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.
