Disease Staging and Prognosis in Smokers Using Deep Learning in Chest Computed Tomography.
Germán GonzálezSamuel Y AshGonzalo Vegas Sánchez-FerreroJorge Onieva OnievaFarbod N RahaghiJames C RossAlejandro DíazRaúl San José EstéparGeorge R Washkonull nullPublished in: American journal of respiratory and critical care medicine (2019)
A deep-learning approach that uses only computed tomography imaging data can identify those smokers who have COPD and predict who are most likely to have ARD events and those with the highest mortality. At a population level CNN analysis may be a powerful tool for risk assessment.
Keyphrases
- deep learning
- computed tomography
- convolutional neural network
- risk assessment
- positron emission tomography
- smoking cessation
- artificial intelligence
- magnetic resonance imaging
- chronic obstructive pulmonary disease
- dual energy
- machine learning
- high resolution
- lymph node
- contrast enhanced
- cardiovascular events
- big data
- electronic health record
- human health
- risk factors
- image quality
- lung function
- heavy metals
- pet ct
- cardiovascular disease
- type diabetes
- cystic fibrosis
- mass spectrometry
- magnetic resonance
- air pollution