The DOI matches its Crossref record; 32 of the 32 references it cites were found in a registry or an index.
Recommendations for research design and reporting in computer-assisted diagnosis to facilitate meta-analysis.
Open accessAbstract
Computer-assisted diagnosis (CAD) describes a diverse, heterogeneous range of applications rather than a single entity. The aims and functions of CAD systems vary considerably and comparing studies and systems is challenging due to methodological and design differences. In addition, poor study quality and reporting can reduce the value of some publications. Meta-analyses of CAD are therefore difficult and may not provide reliable conclusions. Aiming to determine the major sources of heterogeneity and thereby what CAD researchers could change to allow this sort of assessment, this study reviews a sample of 147 papers concerning CAD used with imaging for cancer diagnosis. It discusses sources of variability, including the goal of the CAD system, learning methodology, study population, design, outcome measures, inclusion of radiologists, and study quality. Based upon this evidence, recommendations are made to help researchers optimize the quality and comparability of their trial design and reporting.
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By ensuring each reader reviewed each case only once in either the assisted or unassisted modality, the design eliminated any memory effect associated with a crossover study.
“By ensuring each reader reviewed each case only once in either the assisted or unassisted modality, this design eliminated any memory effect associated with a crossover study (where memorable cases may inflate the diagnostic performance when reviewed a second time by the same readers).28,29”
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Jain, A., Way, D., Gupta, V., Gao, Y., de Oliveira Marinho, G., Hartford, J., Sayres, R., Kanada, K., Eng, C., Nagpal, K., et al. (2021) · JAMA Network Open · doi:10.1001/jamanetworkopen.2021.7249 · Cited by 164
Metadata
- Authors
- Eadie, L. H., Taylor, P., & Gibson, A. P.
- Affiliations
- University College London (GB)
- Corresponding author
- Eadie, Leila H.
- Venue
- Journal of Biomedical Informatics
- Publisher
- Elsevier BV
- Published
- 2012-04
- Volume
- 45
- Issue
- 2
- Pages
- 390-397
- ISSN
- 1532-0464 (ISSN-L)
- OpenAlex
- W2102226011
- Cited by
- 15
- References
- 32
- Citations by year
- 2021: 2 · 2020: 1 · 2018: 2 · 2017: 1 · 2016: 6 · 2015: 3
- FWCI
- 0.00
- Type
- journal article
- Language
- en
- Open access
- hybrid
- doi.org
- Licence
- other-oa
- www.elsevier.com/tdm/userlicense/1.0/ (tdm)
- www.elsevier.com/legal/tdmrep-license (tdm)
- www.elsevier.com/open-access/userlicense/1.0/ (vor, 508 d)
- doi.org/10.15223/policy-017 (stm-asf)
- doi.org/10.15223/policy-037 (stm-asf)
- doi.org/10.15223/policy-012 (stm-asf)
- doi.org/10.15223/policy-029 (stm-asf)
- doi.org/10.15223/policy-004 (stm-asf)
- Locations
- Journal of Biomedical Informatics (publishedVersion, other-oa)
- PubMed (publishedVersion)
- UCL Discovery (University College London) (submittedVersion)
- Funders
- Engineering and Physical Sciences Research Council
- Topics
- Social Sciences › Decision Sciences › Statistics, Probability and Uncertainty › Meta-analysis and systematic reviews
- Physical Sciences › Computer Science › Artificial Intelligence › AI in cancer detection
- Health Sciences › Medicine › Radiology, Nuclear Medicine and Imaging › Radiomics and Machine Learning in Medical Imaging
- Keywords
- computer-aided diagnosis, research design, reporting quality, meta-analysis, cancer diagnosis, diagnostic imaging, heterogeneity, study quality, outcome measures, learning methods, trial comparability
- MeSH terms
- Diagnosis, Computer-Assisted, Humans, Research Design, Meta-Analysis as Topic, Outcome Assessment, Health Care
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