Literature DB >> 23822459

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

Zhimin Huo1, 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.   

Abstract

Computer-aided detection/diagnosis (CAD) is increasingly used for decision support by clinicians for detection and interpretation of diseases. However, there are no quality assurance (QA) requirements for CAD in clinical use at present. QA of CAD is important so that end users can be made aware of changes in CAD performance both due to intentional or unintentional causes. In addition, end-user training is critical to prevent improper use of CAD, which could potentially result in lower overall clinical performance. Research on QA of CAD and user training are limited to date. The purpose of this paper is to bring attention to these issues, inform the readers of the opinions of the members of the American Association of Physicists in Medicine (AAPM) CAD subcommittee, and thus stimulate further discussion in the CAD community on these topics. The recommendations in this paper are intended to be work items for AAPM task groups that will be formed to address QA and user training issues on CAD in the future. The work items may serve as a framework for the discussion and eventual design of detailed QA and training procedures for physicists and users of CAD. Some of the recommendations are considered by the subcommittee to be reasonably easy and practical and can be implemented immediately by the end users; others are considered to be "best practice" approaches, which may require significant effort, additional tools, and proper training to implement. The eventual standardization of the requirements of QA procedures for CAD will have to be determined through consensus from members of the CAD community, and user training may require support of professional societies. It is expected that high-quality CAD and proper use of CAD could allow these systems to achieve their true potential, thus benefiting both the patients and the clinicians, and may bring about more widespread clinical use of CAD for many other diseases and applications. It is hoped that the awareness of the need for appropriate CAD QA and user training will stimulate new ideas and approaches for implementing such procedures efficiently and effectively as well as funding opportunities to fulfill such critical efforts.

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Year:  2013        PMID: 23822459      PMCID: PMC5438240          DOI: 10.1118/1.4807642

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


  13 in total

1.  Computer-aided detection as evidence in the courtroom: potential implications of an appellate court's ruling.

Authors:  R James Brenner; Michael J Ulissey; Ronald M Wilt
Journal:  AJR Am J Roentgenol       Date:  2006-01       Impact factor: 3.959

2.  Improved cancer detection using computer-aided detection with diagnostic and screening mammography: prospective study of 104 cancers.

Authors:  Judy C Dean; Christina C Ilvento
Journal:  AJR Am J Roentgenol       Date:  2006-07       Impact factor: 3.959

3.  Computer-aided detection for CT colonography: incremental benefit of observer training.

Authors:  S A Taylor; D Burling; M Roddie; L Honeyfield; J McQuillan; P Bassett; S Halligan
Journal:  Br J Radiol       Date:  2008-01-07       Impact factor: 3.039

Review 4.  Anniversary paper: History and status of CAD and quantitative image analysis: the role of Medical Physics and AAPM.

Authors:  Maryellen L Giger; Heang-Ping Chan; John Boone
Journal:  Med Phys       Date:  2008-12       Impact factor: 4.071

5.  Evaluation of computer-aided detection and diagnosis systems.

Authors:  Nicholas Petrick; 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
Journal:  Med Phys       Date:  2013-08       Impact factor: 4.071

6.  WE-E-217A-03: Methodologies for Evaluation of Effects of CAD on Users.

Authors:  N Petrick
Journal:  Med Phys       Date:  2012-06       Impact factor: 4.071

7.  WE-E-217A-02: Methodologies for Evaluation of Standalone CAD System Performance.

Authors:  B Sahiner
Journal:  Med Phys       Date:  2012-06       Impact factor: 4.071

8.  Computer-Aided Diagnosis in Mammography Using Content-based Image Retrieval Approaches: Current Status and Future Perspectives.

Authors:  Bin Zheng
Journal:  Algorithms       Date:  2009-06-01

9.  Temporal and multiinstitutional quality assessment of CT colonography.

Authors:  Robert L Van Uitert; Ronald M Summers; Jacob M White; Keshav K Deshpande; J Richard Choi; Perry J Pickhardt
Journal:  AJR Am J Roentgenol       Date:  2008-11       Impact factor: 3.959

10.  The caBIG annotation and image Markup project.

Authors:  David S Channin; Pattanasak Mongkolwat; Vladimir Kleper; Kastubh Sepukar; Daniel L Rubin
Journal:  J Digit Imaging       Date:  2009-03-18       Impact factor: 4.056

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  7 in total

1.  Evaluation of computer-aided detection and diagnosis systems.

Authors:  Nicholas Petrick; 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
Journal:  Med Phys       Date:  2013-08       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

Review 3.  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

Review 4.  Computer-aided diagnosis in the era of deep learning.

Authors:  Heang-Ping Chan; Lubomir M Hadjiiski; Ravi K Samala
Journal:  Med Phys       Date:  2020-06       Impact factor: 4.071

Review 5.  Deep Learning in Medical Image Analysis.

Authors:  Heang-Ping Chan; Ravi K Samala; Lubomir M Hadjiiski; Chuan Zhou
Journal:  Adv Exp Med Biol       Date:  2020       Impact factor: 2.622

6.  Risks of feature leakage and sample size dependencies in deep feature extraction for breast mass classification.

Authors:  Ravi K Samala; Heang-Ping Chan; Lubomir Hadjiiski; Mark A Helvie
Journal:  Med Phys       Date:  2021-04-12       Impact factor: 4.506

7.  Deep Learning in CT Images: Automated Pulmonary Nodule Detection for Subsequent Management Using Convolutional Neural Network.

Authors:  Yi-Ming Xu; Teng Zhang; Hai Xu; Liang Qi; Wei Zhang; Yu-Dong Zhang; Da-Shan Gao; Mei Yuan; Tong-Fu Yu
Journal:  Cancer Manag Res       Date:  2020-04-29       Impact factor: 3.989

  7 in total

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