Clinical impact of a deep learning system for automated detection of missed pulmonary nodules on routine body computed tomography including the chest region.
Kueian ChenYing-Chieh LaiBalamuralidhar VanniarajanPieh-Hsu WangShao-Chung WangYu-Chun LinShu-Hang NgPelu TranGigin LinPublished in: European radiology (2022)
• DLS-assisted automated detection as a second reader is feasible in a large consecutive cohort. • Performance of combined radiologists and DLS was better than DLS or radiologists alone. • Pulmonary nodules were missed more frequently in abdomino-pelvis CT than the thoracic CT.
Keyphrases
- deep learning
- computed tomography
- artificial intelligence
- dual energy
- image quality
- contrast enhanced
- positron emission tomography
- machine learning
- pulmonary hypertension
- loop mediated isothermal amplification
- high throughput
- magnetic resonance imaging
- convolutional neural network
- label free
- real time pcr
- spinal cord
- clinical practice
- magnetic resonance
- spinal cord injury