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Evaluation of computer‐aided detection and diagnosis systems a)
Author(s) -
Petrick Nicholas,
Sahiner Berkman,
Armato Samuel G.,
Bert Alberto,
Correale Loredana,
Delsanto Silvia,
Freedman Matthew T.,
Fryd David,
Gur David,
Hadjiiski Lubomir,
Huo Zhimin,
Jiang Yulei,
Morra Lia,
Paquerault Sophie,
Raykar Vikas,
Samuelson Frank,
Summers Ronald M.,
Tourassi Georgia,
Yoshida Hiroyuki,
Zheng Bin,
Zhou Chuan,
Chan HeangPing
Publication year - 2013
Publication title -
medical physics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.473
H-Index - 180
eISSN - 2473-4209
pISSN - 0094-2405
DOI - 10.1118/1.4816310
Subject(s) - cad , standardization , computer science , medical diagnosis , medical physics , computer aided diagnosis , clinical decision support system , risk analysis (engineering) , data science , decision support system , medicine , artificial intelligence , engineering drawing , pathology , engineering , operating system
Computer‐aided detection and diagnosis (CAD) systems are increasingly being used as an aid by clinicians for detection and interpretation of diseases. Computer‐aided detection systems mark regions of an image that may reveal specific abnormalities and are used to alert clinicians to these regions during image interpretation. Computer‐aided diagnosis systems provide an assessment of a disease using image‐based information alone or in combination with other relevant diagnostic data and are used by clinicians as a decision support in developing their diagnoses. While CAD systems are commercially available, standardized approaches for evaluating and reporting their performance have not yet been fully formalized in the literature or in a standardization effort. This deficiency has led to difficulty in the comparison of CAD devices and in understanding how the reported performance might translate into clinical practice. To address these important issues, the American Association of Physicists in Medicine (AAPM) formed the Computer Aided Detection in Diagnostic Imaging Subcommittee (CADSC), in part, to develop recommendations on approaches for assessing CAD system performance. The purpose of this paper is to convey the opinions of the AAPM CADSC members and to stimulate the development of consensus approaches and “best practices” for evaluating CAD systems. Both the assessment of a standalone CAD system and the evaluation of the impact of CAD on end‐users are discussed. It is hoped that awareness of these important evaluation elements and the CADSC recommendations will lead to further development of structured guidelines for CAD performance assessment. Proper assessment of CAD system performance is expected to increase the understanding of a CAD system's effectiveness and limitations, which is expected to stimulate further research and development efforts on CAD technologies, reduce problems due to improper use, and eventually improve the utility and efficacy of CAD in clinical practice.