Predicting Perceived Reporting Complexity of Abdominopelvic Computed Tomography With Deep Learning.
Phillip M ChengGilbert WhangEvan AllgoodTapas K TejuraPublished in: Journal of computer assisted tomography (2022)
There is moderate interrater agreement in radiologist-perceived reporting complexity for CT studies of the abdomen and pelvis. Automated prediction of reporting complexity in radiology studies may be a useful adjunct to radiology practice analytics.
Keyphrases
- deep learning
- computed tomography
- artificial intelligence
- adverse drug
- social support
- big data
- depressive symptoms
- machine learning
- dual energy
- positron emission tomography
- physical activity
- mental health
- contrast enhanced
- image quality
- healthcare
- magnetic resonance imaging
- case control
- primary care
- convolutional neural network
- high throughput
- high intensity
- emergency department
- quality improvement
- single cell