Value of CT quantification in progressive fibrosing interstitial lung disease: a deep learning approach.
Seok Young KohJong Hyuk LeeHyungin ParkJin Mo GooPublished in: European radiology (2023)
• Radiologic findings on high-resolution CT are important in diagnosing progressive fibrosing interstitial lung disease. • Deep learning-based quantification results for fibrosis and total interstitial lung disease extents correlated with the decline in forced vital capacity and visual assessments of interstitial lung disease progression, and emerged as independent prognostic factors. • Deep learning-based interstitial lung disease CT quantification can play a key role in diagnosing and prognosticating progressive fibrosing interstitial lung disease.
Keyphrases
- interstitial lung disease
- deep learning
- systemic sclerosis
- rheumatoid arthritis
- idiopathic pulmonary fibrosis
- prognostic factors
- multiple sclerosis
- image quality
- computed tomography
- dual energy
- high resolution
- contrast enhanced
- artificial intelligence
- convolutional neural network
- machine learning
- positron emission tomography
- magnetic resonance imaging
- magnetic resonance
- pet ct
- high speed