Literature DB >> 22095995

Computer-aided diagnosis: how to move from the laboratory to the clinic.

Bram van Ginneken1, Cornelia M Schaefer-Prokop, Mathias Prokop.   

Abstract

Computer-aided diagnosis (CAD), encompassing computer-aided detection and quantification, is an established and rapidly growing field of research. In daily practice, however, most radiologists do not yet use CAD routinely. This article discusses how to move CAD from the laboratory to the clinic. The authors review the principles of CAD for lesion detection and for quantification and illustrate the state-of-the-art with various examples. The requirements that radiologists have for CAD are discussed: sufficient performance, no increase in reading time, seamless workflow integration, regulatory approval, and cost efficiency. Performance is still the major bottleneck for many CAD systems. Novel ways of using CAD, extending the traditional paradigm of displaying markers for a second look, may be the key to using the technology effectively. The most promising strategy to improve CAD is the creation of publicly available databases for training and validation. This can identify the most fruitful new research directions, and provide a platform to combine multiple approaches for a single task to create superior algorithms. © RSNA, 2011.

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Year:  2011        PMID: 22095995     DOI: 10.1148/radiol.11091710

Source DB:  PubMed          Journal:  Radiology        ISSN: 0033-8419            Impact factor:   11.105


  60 in total

1.  Impact of a computer-aided detection (CAD) system integrated into a picture archiving and communication system (PACS) on reader sensitivity and efficiency for the detection of lung nodules in thoracic CT exams.

Authors:  Luca Bogoni; Jane P Ko; Jeffrey Alpert; Vikram Anand; John Fantauzzi; Charles H Florin; Chi Wan Koo; Derek Mason; William Rom; Maria Shiau; Marcos Salganicoff; David P Naidich
Journal:  J Digit Imaging       Date:  2012-12       Impact factor: 4.056

2.  Feasibility Study of a Generalized Framework for Developing Computer-Aided Detection Systems-a New Paradigm.

Authors:  Mitsutaka Nemoto; Naoto Hayashi; Shouhei Hanaoka; Yukihiro Nomura; Soichiro Miki; Takeharu Yoshikawa
Journal:  J Digit Imaging       Date:  2017-10       Impact factor: 4.056

3.  Three-dimensional morphological and signal intensity features for detection of intervertebral disc degeneration from magnetic resonance images.

Authors:  A Neubert; J Fripp; C Engstrom; D Walker; M-A Weber; R Schwarz; S Crozier
Journal:  J Am Med Inform Assoc       Date:  2013-06-27       Impact factor: 4.497

Review 4.  Progress in Fully Automated Abdominal CT Interpretation.

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

5.  CAD system based on B-mode and color Doppler sonographic features may predict if a thyroid nodule is hot or cold.

Authors:  Ali Abbasian Ardakani; Ahmad Bitarafan-Rajabi; Afshin Mohammadi; Sepideh Hekmat; Aylin Tahmasebi; Mohammad Bagher Shiran; Ali Mohammadzadeh
Journal:  Eur Radiol       Date:  2019-01-09       Impact factor: 5.315

Review 6.  OIPAV: an Integrated Software System for Ophthalmic Image Processing, Analysis, and Visualization.

Authors:  Lichun Zhang; Dehui Xiang; Chao Jin; Fei Shi; Kai Yu; Xinjian Chen
Journal:  J Digit Imaging       Date:  2019-02       Impact factor: 4.056

7.  Toward clinically usable CAD for lung cancer screening with computed tomography.

Authors:  Matthew S Brown; Pechin Lo; Jonathan G Goldin; Eran Barnoy; Grace Hyun J Kim; Michael F McNitt-Gray; Denise R Aberle
Journal:  Eur Radiol       Date:  2014-07-24       Impact factor: 5.315

8.  [Future of mammography-based imaging].

Authors:  R Schulz-Wendtland; T Wittenberg; T Michel; A Hartmann; M W Beckmann; C Rauh; S M Jud; B Brehm; M Meier-Meitinger; G Anton; M Uder; P A Fasching
Journal:  Radiologe       Date:  2014-03       Impact factor: 0.635

9.  A fully automatic end-to-end method for content-based image retrieval of CT scans with similar liver lesion annotations.

Authors:  A B Spanier; N Caplan; J Sosna; B Acar; L Joskowicz
Journal:  Int J Comput Assist Radiol Surg       Date:  2017-11-16       Impact factor: 2.924

10.  Predicting visual semantic descriptive terms from radiological image data: preliminary results with liver lesions in CT.

Authors:  Adrien Depeursinge; Camille Kurtz; Christopher Beaulieu; Sandy Napel; Daniel Rubin
Journal:  IEEE Trans Med Imaging       Date:  2014-05-01       Impact factor: 10.048

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