Intelligent imaging: Applications of machine learning and deep learning in radiology.
Geoffrey Michael CurrieEric RohrenPublished in: Veterinary radiology & ultrasound : the official journal of the American College of Veterinary Radiology and the International Veterinary Radiology Association (2022)
Artificial intelligence (AI) in radiology is transforming medical image analysis. While applications in triaging for priority reporting and radiomic feature analysis have been widely reported, perhaps the most important applications lie in noise reduction, image optimization following dose reduction strategies, image reconstruction direct from projection data and generation of pseudo-CT for attenuation correction. There are common beneficial applications, and potential risks, between human radiology and veterinary radiology. Artificial intelligence may see recrafting of some responsibilities but offers AI augmentation of human driven systems. The redundancy afforded by human augmentation of AI and AI autonomy are not on the horizon, but rather are already here.
Keyphrases
- artificial intelligence
- deep learning
- machine learning
- big data
- endothelial cells
- convolutional neural network
- induced pluripotent stem cells
- pluripotent stem cells
- healthcare
- magnetic resonance imaging
- air pollution
- image quality
- magnetic resonance
- risk assessment
- electronic health record
- climate change
- dual energy