Artificial Intelligence-assisted quantification of COVID-19 pneumonia burden from computed tomography improves prediction of adverse outcomes over visual scoring systems.
Kajetan GrodeckiAditya KillekarJudit SimonAndrew LinSebastien CadetPriscilla McElhinneyCato ChanMichelle C WilliamsBarry D PressmanPeter JulienDebiao LiPeter ChenNicola GaibazziUdit ThakurElisabetta ManciniCecilia AgalbatoJiro MunechikaHidenari MatsumotoRoberto MeneGianfranco ParatiFranco CernigliaroNitesh NerlekarCamilla TorlascoGianluca PontonePal Maurovich-HorvatPiotr J SlomkaDamini DeyPublished in: The British journal of radiology (2023)
Quantitative pneumonia burden assessed using AI demonstrated higher performance for predicting clinical deterioration compared to current semi-quantitative scoring systems. Such an AI system has the potential to be applied for image-based triage of COVID-19 patients in clinical practice.
Keyphrases
- artificial intelligence
- deep learning
- sars cov
- machine learning
- computed tomography
- big data
- clinical practice
- coronavirus disease
- high resolution
- emergency department
- magnetic resonance imaging
- positron emission tomography
- risk factors
- respiratory failure
- community acquired pneumonia
- respiratory syndrome coronavirus
- contrast enhanced
- climate change