Machine learning for lung texture analysis on thin-section CT: Capability for assessments of disease severity and therapeutic effect for connective tissue disease patients in comparison with expert panel evaluations.
Yoshiharu OhnoKota AoyagiDaisuke TakenakaTakeshi YoshikawaYasuko FujisawaNaoki SugiharaNayu HamabuchiSatomu HanamatsuYuki ObamaTakahiro UedaHidekazu HattoriKazuhiro MurayamaHiroshi ToyamaPublished in: Acta radiologica (Stockholm, Sweden : 1987) (2021)
ML-based CT texture analysis has better potential than qualitatively assessed thin-section CT for disease severity assessment and treatment response evaluation for CTD-ILD.
Keyphrases
- contrast enhanced
- machine learning
- computed tomography
- image quality
- dual energy
- end stage renal disease
- magnetic resonance imaging
- newly diagnosed
- chronic kidney disease
- positron emission tomography
- magnetic resonance
- prognostic factors
- artificial intelligence
- clinical practice
- patient reported outcomes
- climate change
- deep learning
- systemic sclerosis