Weiya Shi

TL;DR: A deep learning (DL) based segmentation system is developed to automatically quantify infection regions of interest (ROIs) and their volumetric ratios w.r.t. the lung and possible applications, including but not limited to analysis of follow-up CT scans and infection distributions in the lobes and segments correlated with clinical findings were discussed.

TL;DR: A DL‐based segmentation system has been developed to automatically segment and quantify infection regions in CT scans of COVID‐19 patients andQuantitative evaluation indicated high accuracy in automatic infection delineation and severity prediction.

TL;DR: A model of deep learning (DL)-based quantitative computed tomography (CT) and initial clinical features in prediction of the severity of COVID-19 showed more efficiency in predicting the severity than quantitative CT parameters and PSI score do.

TL;DR: In this paper, a boron carbon nitride thin film was prepared on Si (100) substrates by means of plasma-assisted pulsed laser deposition at low temperatures ( 4 C) target was ablated by laser pulses in the environment of a nitrogen plasma, generated from electron cyclotron resonance (ECR) microwave discharge in pure nitrogen gas.

TL;DR: In this article, a deep learning model based on quantitative computed tomography (CT) and initial clinical features to predict the severity of coronavirus disease 2019 (COVID-19) by clinical presentation has not met the urgent clinical need so far.