Literature DB >> 23927365

Evaluation of computer-aided detection and diagnosis systems.

Nicholas Petrick1, Berkman Sahiner, Samuel G Armato, Alberto Bert, Loredana Correale, Silvia Delsanto, Matthew T Freedman, David Fryd, David Gur, Lubomir Hadjiiski, Zhimin Huo, Yulei Jiang, Lia Morra, Sophie Paquerault, Vikas Raykar, Frank Samuelson, Ronald M Summers, Georgia Tourassi, Hiroyuki Yoshida, Bin Zheng, Chuan Zhou, Heang-Ping Chan.   

Abstract

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.

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Year:  2013        PMID: 23927365      PMCID: PMC4108682          DOI: 10.1118/1.4816310

Source DB:  PubMed          Journal:  Med Phys        ISSN: 0094-2405            Impact factor:   4.071


  116 in total

1.  Maximum likelihood fitting of FROC curves under an initial-detection-and-candidate-analysis model.

Authors:  Darrin C Edwards; Matthew A Kupinski; Charles E Metz; Robert M Nishikawa
Journal:  Med Phys       Date:  2002-12       Impact factor: 4.071

2.  On the comparison of FROC curves in mammography CAD systems.

Authors:  Hans Bornefalk; Anna Bornefalk Hermansson
Journal:  Med Phys       Date:  2005-02       Impact factor: 4.071

3.  Three-class ROC analysis--a decision theoretic approach under the ideal observer framework.

Authors:  Xin He; Charles E Metz; Benjamin M W Tsui; Jonathan M Links; Eric C Frey
Journal:  IEEE Trans Med Imaging       Date:  2006-05       Impact factor: 10.048

Review 4.  Analysis of location specific observer performance data: validated extensions of the jackknife free-response (JAFROC) method.

Authors:  Dev P Chakraborty
Journal:  Acad Radiol       Date:  2006-10       Impact factor: 3.173

5.  One-shot estimate of MRMC variance: AUC.

Authors:  Brandon D Gallas
Journal:  Acad Radiol       Date:  2006-03       Impact factor: 3.173

Review 6.  Computer-aided diagnosis in medical imaging: historical review, current status and future potential.

Authors:  Kunio Doi
Journal:  Comput Med Imaging Graph       Date:  2007-03-08       Impact factor: 4.790

7.  Area under the free-response ROC curve (FROC) and a related summary index.

Authors:  Andriy I Bandos; Howard E Rockette; Tao Song; David Gur
Journal:  Biometrics       Date:  2008-05-13       Impact factor: 2.571

8.  Satisfaction of search in diagnostic radiology.

Authors:  K S Berbaum; E A Franken; D D Dorfman; S A Rooholamini; M H Kathol; T J Barloon; F M Behlke; Y Sato; C H Lu; G Y el-Khoury
Journal:  Invest Radiol       Date:  1990-02       Impact factor: 6.016

9.  Computer-aided detection with screening mammography in a university hospital setting.

Authors:  Robyn L Birdwell; Parul Bandodkar; Debra M Ikeda
Journal:  Radiology       Date:  2005-08       Impact factor: 11.105

10.  Screening mammography with computer-aided detection: prospective study of 12,860 patients in a community breast center.

Authors:  T W Freer; M J Ulissey
Journal:  Radiology       Date:  2001-09       Impact factor: 11.105

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  34 in total

1.  Quality assurance and training procedures for computer-aided detection and diagnosis systems in clinical use.

Authors:  Zhimin Huo; Ronald M Summers; Sophie Paquerault; Joseph Lo; Jeffrey Hoffmeister; Samuel G Armato; Matthew T Freedman; Jesse Lin; Shih-Chung Ben Lo; Nicholas Petrick; Berkman Sahiner; David Fryd; Hiroyuki Yoshida; Heang-Ping Chan
Journal:  Med Phys       Date:  2013-07       Impact factor: 4.071

Review 2.  Progress in Fully Automated Abdominal CT Interpretation.

Authors:  Ronald M Summers
Journal:  AJR Am J Roentgenol       Date:  2016-04-21       Impact factor: 3.959

3.  Variabilities in Reference Standard by Radiologists and Performance Assessment in Detection of Pulmonary Embolism in CT Pulmonary Angiography.

Authors:  Chuan Zhou; Heang-Ping Chan; Aamer Chughtai; Smita Patel; Jean Kuriakose; Lubomir M Hadjiiski; Jun Wei; Ella A Kazerooni
Journal:  J Digit Imaging       Date:  2019-12       Impact factor: 4.056

4.  Open access image repositories: high-quality data to enable machine learning research.

Authors:  F Prior; J Almeida; P Kathiravelu; T Kurc; K Smith; T J Fitzgerald; J Saltz
Journal:  Clin Radiol       Date:  2019-04-28       Impact factor: 2.350

5.  Can Occult Invasive Disease in Ductal Carcinoma In Situ Be Predicted Using Computer-extracted Mammographic Features?

Authors:  Bibo Shi; Lars J Grimm; Maciej A Mazurowski; Jay A Baker; Jeffrey R Marks; Lorraine M King; Carlo C Maley; E Shelley Hwang; Joseph Y Lo
Journal:  Acad Radiol       Date:  2017-05-11       Impact factor: 3.173

Review 6.  Radiologists and Clinical Trials: Part 2: Practical Statistical Methods for Understanding and Monitoring Independent Reader Performance.

Authors:  David L Raunig; Annette M Schmid; Colin G Miller; Richard C Walovitch; Michael O'Connor; Klaus Noever; Ivalina Hristova; Michael O'Neal; Guenther Brueggenwerth; Robert R Ford
Journal:  Ther Innov Regul Sci       Date:  2021-07-09       Impact factor: 1.778

7.  Diagnostic Accuracy of CT for Prediction of Bladder Cancer Treatment Response with and without Computerized Decision Support.

Authors:  Kenny H Cha; Lubomir M Hadjiiski; Richard H Cohan; Heang-Ping Chan; Elaine M Caoili; Matthew S Davenport; Ravi K Samala; Alon Z Weizer; Ajjai Alva; Galina Kirova-Nedyalkova; Kimberly Shampain; Nathaniel Meyer; Daniel Barkmeier; Sean Woolen; Prasad R Shankar; Isaac R Francis; Phillip Palmbos
Journal:  Acad Radiol       Date:  2018-11-10       Impact factor: 3.173

Review 8.  CAD and AI for breast cancer-recent development and challenges.

Authors:  Heang-Ping Chan; Ravi K Samala; Lubomir M Hadjiiski
Journal:  Br J Radiol       Date:  2019-12-16       Impact factor: 3.039

9.  Automated pectoral muscle identification on MLO-view mammograms: Comparison of deep neural network to conventional computer vision.

Authors:  Xiangyuan Ma; Jun Wei; Chuan Zhou; Mark A Helvie; Heang-Ping Chan; Lubomir M Hadjiiski; Yao Lu
Journal:  Med Phys       Date:  2019-03-12       Impact factor: 4.071

10.  Prediction of Occult Invasive Disease in Ductal Carcinoma in Situ Using Deep Learning Features.

Authors:  Bibo Shi; Lars J Grimm; Maciej A Mazurowski; Jay A Baker; Jeffrey R Marks; Lorraine M King; Carlo C Maley; E Shelley Hwang; Joseph Y Lo
Journal:  J Am Coll Radiol       Date:  2018-02-02       Impact factor: 5.532

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