Efficiency of a computer-aided diagnosis (CAD) system with deep learning in detection of pulmonary nodules on 1-mm-thick images of computed tomography.

Journal: Japanese journal of radiology

Published Date: Jun 26, 2020

Abstract

PURPOSE: To evaluate the performance of a deep learning-based computer-aided diagnosis (CAD) system at detecting pulmonary nodules on CT by comparing radiologists' readings with and without CAD.

Authors

Takenori Kozuka

Department of Radiology, Kindai University Faculty of Medicine, 377-2 Ohno-Higashi, Osaka-Sayama, Osaka, 589-8511, Japan. takkozuka@gmail.com.
Yuko Matsukubo

Department of Radiology, Kindai University Faculty of Medicine, 377-2 Ohno-Higashi, Osaka-Sayama, Osaka, 589-8511, Japan.
Tomoya Kadoba

Department of Radiology, Kindai University Faculty of Medicine, 377-2 Ohno-Higashi, Osaka-Sayama, Osaka, 589-8511, Japan.
Teruyoshi Oda

Department of Radiology, Kindai University Faculty of Medicine, 377-2 Ohno-Higashi, Osaka-Sayama, Osaka, 589-8511, Japan.
Ayako Suzuki

Department of Radiology, Kindai University Faculty of Medicine, 377-2 Ohno-Higashi, Osaka-Sayama, Osaka, 589-8511, Japan.
Tomoko Hyodo

Department of Radiology, Kindai University Faculty of Medicine, 377-2 Ohno-Higashi, Osaka-Sayama, Osaka, 589-8511, Japan.
SungWoon Im

Department of Radiology, Kindai University Faculty of Medicine, 377-2 Ohno-Higashi, Osaka-Sayama, Osaka, 589-8511, Japan.
Hayato Kaida

Division of Positron Emission Tomography, Institute of Advanced Clinical Medicine, Kindai University, Osaka, Japan.
Yukinobu Yagyu

Department of Radiology, Kindai University Faculty of Medicine, 377-2 Ohno-Higashi, Osaka-Sayama, Osaka, 589-8511, Japan.
Masakatsu Tsurusaki

Department of Radiology, Kindai University Faculty of Medicine, 377-2 Ohno-Higashi, Osaka-Sayama, Osaka, 589-8511, Japan.
Mitsuru Matsuki

Department of Radiology, Kindai University Faculty of Medicine, 377-2 Ohno-Higashi, Osaka-Sayama, Osaka, 589-8511, Japan.
Kazunari Ishii

Division of Positron Emission Tomography, Institute of Advanced Clinical Medicine, Kindai University, Osaka, Japan.

Keywords

Adult Aged Aged, 80 and over Deep Learning Diagnosis, Computer-Assisted Female Humans Lung Lung Neoplasms Male Middle Aged Multiple Pulmonary Nodules Radiographic Image Interpretation, Computer-Assisted Reproducibility of Results Retrospective Studies Sensitivity and Specificity Solitary Pulmonary Nodule Tomography, X-Ray Computed Young Adult

External Resources

View on PubMed Access via DOI PubMed (32592003)

Efficiency of a computer-aided diagnosis (CAD) system with deep learning in detection of pulmonary nodules on 1-mm-thick images of computed tomography.

Abstract

Authors

Keywords

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