Is the generalizability of a developed artificial intelligence algorithm for COVID-19 on chest CT sufficient for clinical use? Results from the International Consortium for COVID-19 Imaging AI (ICOVAI).
Laurens TopffKevin B W Groot LipmanFrederic GuffensRianne WittenbergAnnemarieke Bartels-RuttenGerben van VeenendaalMirco HessKay LamerigtsJoris WakkieErik RanschaertStefano TrebeschiJacob J VisserRegina G H Beets-Tannull nullPublished in: European radiology (2023)
• The ICOVAI model for prediction of CO-RADS and quantification of disease extent on chest CT of COVID-19 patients was developed using a large sample of multicenter data. • There was substantial performance on internal testing; however, performance was significantly reduced on external validation, performed by independent researchers. The limited generalizability of the model restricts its potential for clinical use. • Results of AI models for COVID-19 imaging on internal tests may not generalize well to external data, demonstrating the importance of independent external validation.
Keyphrases
- artificial intelligence
- sars cov
- big data
- coronavirus disease
- machine learning
- deep learning
- high resolution
- computed tomography
- contrast enhanced
- electronic health record
- respiratory syndrome coronavirus
- image quality
- dual energy
- magnetic resonance
- magnetic resonance imaging
- cross sectional
- fluorescence imaging
- data analysis