Work

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

Year: 2021

Type: article

Study design: Observational Study

Source: Acta Radiologica

DOI: 10.1177/02841851211044973

Authors: 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

Institutions: Fujita Health University, Kobe University, Canon Medical Systems Corporation (Japan), Hyogo Prefectural Cancer Center

Language: English

Topic: Radiomics and Machine Learning in Medical Imaging

Subfield: Radiology, Nuclear Medicine and Imaging

Field: Medicine

Domain: Health Sciences

SDG: Good health and well-being

Open Access status: closed

Funders: Canon Medical Systems Corporation, Smoking Research Foundation, Japan Society for the Promotion of Science

Awards: Development of a computer-aided device for ultra-low-dose CT screening for lung cancer and differential diagnosis of lung nodules using AI, Ultra-High-Resolution CT: Prediction of Therapeutic Induced Complication with Radiomics Appropach

OpenAlex ID: W3207754336

FWCI: 0.976

Cites: 37

Cited by: 16

Related to: 10