CORADS-AI is a freely accessible deep learning algorithm that automatically assigns CO-RADS and CT severity scores to non-contrast CT scans of patients suspected of COVID-19 with high diagnostic performance.

Classification is an important task at which both biological and artificial neural networks excel 1,2 . In machine learning, nonlinear projection into a high-dimensional feature space can make data linearly separable 3,4 , simplifying the classification of complex features. Such nonlinear projections are computationally expensive in conventional computers. A promising approach is to exploit physical materials systems that perform this nonlinear projection intrinsically, because of their high computational density 5 , inherent parallelism and energy efficiency 6,7 . However, existing approaches either rely on the systems' time dynamics, which requires sequential data processing and therefore hinders parallel computation 5,6,8 , or employ large materials systems that are difficult to scale up 7 . Here we use a parallel, nanoscale approach inspired by filters in the brain 1 and artificial neural networks 2 to perform nonlinear classification and feature extraction. We exploit the nonlinearity of hopping conduction [9][10][11] through an electrically tunable network of boron dopant atoms in silicon, reconfiguring the network through artificial evolution to realize different computational functions. We first solve the canonical two-input binary classification problem, realizing all Boolean logic gates 12 up to room temperature, demonstrating nonlinear classification with the nanomaterial system. We then evolve our dopant network to realize feature filters 2 that can perform four-input binary classification on the Modified National Institute of Standards and Technology handwritten digit database. Implementation of our material-based filters substantially improves the classification accuracy over that of a linear classifier directly applied to the original data 13 . Our results establish a paradigm of silicon-based electronics for smallfootprint and energy-efficient computation 14 .Doping is a crucial process in semiconductor electronics, where impurity atoms are introduced to modulate the charge carrier concentration. Conventional semiconductor devices operate in the band regime of charge transport, where the delocalization of the charge carriers gives rise to high mobility and a linear response to an applied electric field. At sufficiently low doping concentration and temperature 9,15 , however, delocalization is lost, and carriers move sequentially from dopant atom to dopant atom. This is referred to as the hopping regime 10,11,16 , which exhibits higher resistivity and nonlinearity. Nonlinearity is often undesired, but it is a valuable asset for unconventional computing, that is, for systems that do not follow the Turing model of computation [6][7][8][17][18][19] . Rather than excluding nonlinearity, we can exploit it 12 and manipulate our physical system with artificial evolution to solve computational problems 17 . This evolution in materio has been used, for example, for frequency distinguishing by liquid crystals 18 and robot control with carbon nanotubes 19 . We recently showed that a disordered network of gold...

We present an atomic-scale mechanism based on variable-range hopping of interacting charges enabling reconfigurable logic and nonlinear classification tasks in dopant network processing units in silicon. Kinetic Monte Carlo simulations of the hopping process show temperature-dependent current-voltage characteristics, artificial evolution of basic Boolean logic gates, and fitness-dependent gate abundances in striking agreement with experiment. The simulations provide unique insights in the local electrostatic potential and current flow in the dopant network, showing subtle changes induced by control voltages that set the conditions for the logic operation. These insights will be crucial in the systematic further development of this burgeoning technology for unconventional computing. The establishment of the principles underlying the logic functionality of these devices encourages the exploration and utilization of the same principles in other materials and device geometries.

Cine-MRI for adhesion detection is a promising novel modality that can help the large group of patients developing pain after abdominal surgery. Few studies into its diagnostic accuracy are available, and none address observer variability. This retrospective study explores the inter- and intra-observer variability, diagnostic accuracy, and the effect of experience. A total of 15 observers with a variety of experience reviewed 61 sagittal cine-MRI slices, placing box annotations with a confidence score at locations suspect for adhesions. Five observers reviewed the slices again one year later. Inter- and intra-observer variability are quantified using Fleiss’ (inter) and Cohen’s (intra) κ and percentage agreement. Diagnostic accuracy is quantified with receiver operating characteristic (ROC) analysis based on a consensus standard. Inter-observer Fleiss’ κ values range from 0.04 to 0.34, showing poor to fair agreement. High general and cine-MRI experience led to significantly (p < 0.001) better agreement among observers. The intra-observer results show Cohen’s κ values between 0.37 and 0.53 for all observers, except one with a low κ of −0.11. Group AUC scores lie between 0.66 and 0.72, with individual observers reaching 0.78. This study confirms that cine-MRI can diagnose adhesions, with respect to a radiologist consensus panel and shows that experience improves reading cine-MRI. Observers without specific experience adapt to this modality quickly after a short online tutorial. Observer agreement is fair at best and area under the receiver operating characteristic curve (AUC) scores leave room for improvement. Consistently interpreting this novel modality needs further research, for instance, by developing reporting guidelines or artificial intelligence-based methods.