Automated quantification of COVID-19 severity and progression using chest CT images.
Jiantao PuJoseph K LeaderAndriy BandosShi KeJing WangJunli ShiPang DuYoumin GuoSally E WenzelCarl R FuhrmanDavid O WilsonFrank C SciurbaChenwang JinPublished in: European radiology (2020)
• Both computer vision and deep learning technology were used to develop computer software to quantify the presence and progression of pneumonia associated with COVID-19 depicted on CT images. • The computer software was tested using both quantitative experiments and subjective assessment. • The computer software has the potential to assist in the detection of the pneumonic regions, monitor disease progression, and assess treatment efficacy related to COVID-19.
Keyphrases
- deep learning
- coronavirus disease
- sars cov
- convolutional neural network
- artificial intelligence
- machine learning
- computed tomography
- image quality
- dual energy
- respiratory syndrome coronavirus
- data analysis
- high resolution
- mass spectrometry
- combination therapy
- climate change
- drug induced
- optical coherence tomography
- acute respiratory distress syndrome
- human health
- extracorporeal membrane oxygenation