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 Ohno,Kota Aoyagi,Daisuke Takenaka,Takeshi Yoshikawa,Yasuko Fujisawa,Naoki Sugihara,Nayu Hamabuchi,Satomu Hanamatsu,Yuki Obama,Takahiro Ueda,Hidekazu Hattori,Kazuhiro Murayama,Hiroshi Toyama

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.

2021

Yoshiharu Ohno
Kota Aoyagi
Daisuke Takenaka
Takeshi Yoshikawa
Yasuko Fujisawa
Naoki Sugihara
Nayu Hamabuchi
Satomu Hanamatsu
Yuki Obama
Takahiro Ueda
Hidekazu Hattori
Kazuhiro Murayama
Hiroshi Toyama

BackgroundThe need for quantitative assessment of interstitial lung involvement on thin-section computed tomography (CT) has arisen in interstitial lung diseases including connective tissue disease...

Keywords:

Medicine
Interstitial lung disease
Therapeutic effect
Connective tissue disease
thin section ct
disease severity
Computed tomography
Radiology
texture
Lung

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