Use of artificial intelligence in triaging of chest radiographs to reduce radiologists' workload.
Sung Hyun YoonSunyoung ParkSowon JangJunghoon KimKyung Won LeeWoojoo LeeSeungjae LeeGabin YunKyung Hee LeePublished in: European radiology (2023)
• A 50% workload reduction simulation using deep learning-based detection algorithm maintained noninferior sensitivity while improving specificity. • The CT recommendation rate significantly decreased in the disease-negative patients, whereas it slightly increased in the disease-positive group without statistical significance. • In the exploratory analysis, the noninferiority of sensitivity was maintained until 70% of the workload was reduced; the difference in sensitivity was 0%.
Keyphrases
- artificial intelligence
- deep learning
- machine learning
- big data
- end stage renal disease
- ejection fraction
- newly diagnosed
- chronic kidney disease
- convolutional neural network
- prognostic factors
- computed tomography
- patient reported outcomes
- virtual reality
- structural basis
- sensitive detection
- loop mediated isothermal amplification
- pet ct