Computer-aided diagnosis : how to move from the laboratory to the clinic
© RSNA, 2011..
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.
| Medienart: |
E-Artikel |
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| Erscheinungsjahr: |
2011 |
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| Erschienen: |
2011 |
| Enthalten in: |
Zur Gesamtaufnahme - volume:261 |
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| Enthalten in: |
Radiology - 261(2011), 3 vom: 17. Dez., Seite 719-32 |
| Sprache: |
Englisch |
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| Beteiligte Personen: |
van Ginneken, Bram [VerfasserIn] |
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| Links: |
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| Anmerkungen: |
Date Completed 26.01.2012 Date Revised 31.03.2022 published: Print Citation Status MEDLINE |
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| doi: |
10.1148/radiol.11091710 |
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| funding: |
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| Förderinstitution / Projekttitel: |
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| 520 | |a 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 | ||
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