Explanation of the mechanism by which CAD assistance improves diagnostic performance when reading CT images

Toru Matsumoto; Shinichi Wada; Shinji Yamamoto; Kohei Murao; Akira Furukawa; Masahiro Endo; Mitsuomi Matsumoto; Shusuke Sone

doi:10.1117/12.652928

17 March 2006 Explanation of the mechanism by which CAD assistance improves diagnostic performance when reading CT images

Toru Matsumoto, Shinichi Wada, Shinji Yamamoto, Kohei Murao, Akira Furukawa, Masahiro Endo, Mitsuomi Matsumoto, Shusuke Sone

Author Affiliations +

Proceedings Volume 6146, Medical Imaging 2006: Image Perception, Observer Performance, and Technology Assessment; 614619 (2006) https://doi.org/10.1117/12.652928
Event: Medical Imaging, 2006, San Diego, California, United States

Abstract

The purpose of our research is to make clear the mechanism that a reader (physician or radiological technologist) effectively identify abnormal findings in CT images of lung cancer screening by using with CAD system. A method guessing the 2X2 decision matrix between reader / CAD and reader / reader with CAD was investigated. We suppose the next scene to be it. At first, a reader judges whether abnormal findings per one patient per one CT image are present (1) or absent (0) without CAD results. The second, a reader judges whether abnormal findings are present (1) or absent (0) with CAD results. We expresses the correlation between diagnoses by a reader and CAD system for abnormal cases and for normal cases by following formula using phi correlation coefficient:φ=(cd-ab)/√(a+c)(b+d)(b+c)(a+d). a,b,c,d: 2X2 decision matrix parameters. If TPR1=(a+c)/n, TPR2=(b+c)/n and TPR3=(a+b+c)/n for abnormal cases, TPR3=TPR1+TPR2 - TPR1×TRR2 - φ√TPR1(1-TPR1)TPR2(1-TPR2). Therefore, a=n (TPR3 - TPR1), b=n (TPR3 - TPR2), c=n (TPR1 + TPR2 -TPR3), d=n (1.0 - TPR3). This theory was applied for the experimental data. The 41 students interpreted the same CT images [no training]. A second interpretation was performed after they had been instructed on how to interpret CT images [training], and third was assisted by a virtual CAD [training + CAD]. The mechanism that makes up for a good point of a reader and a CAD with CAD in interpreting CT images was theoretically and experimentally investigated. We concluded that a method guessing the decision matrix (2X2) between a reader and a CAD decided the "presence" or "absence" of abnormal findings explain the improvement mechanism of diagnostic performance with CAD system.

Citation Download Citation

Toru Matsumoto, Shinichi Wada, Shinji Yamamoto, Kohei Murao, Akira Furukawa, Masahiro Endo, Mitsuomi Matsumoto, and Shusuke Sone "Explanation of the mechanism by which CAD assistance improves diagnostic performance when reading CT images", Proc. SPIE 6146, Medical Imaging 2006: Image Perception, Observer Performance, and Technology Assessment, 614619 (17 March 2006); https://doi.org/10.1117/12.652928

ACCESS THE FULL ARTICLE

INSTITUTIONAL
Select your institution to access the SPIE Digital Library.

SELECT YOUR INSTITUTION

PERSONAL
Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.

PERSONAL SIGN IN

No SPIE Account? Create one

PURCHASE THIS CONTENT

SUBSCRIBE TO DIGITAL LIBRARY

50 downloads per 1-year subscription

Members: $195

Non-members: $335 ADD TO CART

25 downloads per 1 - year subscription

Members: $145

Non-members: $250 ADD TO CART

PURCHASE SINGLE ARTICLE

Includes PDF, HTML & Video, when available