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Open Access Technical advance

Computer-aided assessment of diagnostic images for epidemiological research

Alison G Abraham1*, Donald D Duncan2, Stephen J Gange1 and Sheila West3

Author Affiliations

1 Epidemiology Department, Johns Hopkins Bloomberg School of Public Health, Baltimore, Maryland, USA

2 OGI School of Science and Engineering, Oregon Health and Science University, Beaverton, Oregon, USA

3 Wilmer Eye Institute, Johns Hopkins Hospital, Baltimore, Maryland, USA

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BMC Medical Research Methodology 2009, 9:74  doi:10.1186/1471-2288-9-74

Published: 11 November 2009

Abstract

Background

Diagnostic images are often assessed for clinical outcomes using subjective methods, which are limited by the skill of the reviewer. Computer-aided diagnosis (CAD) algorithms that assist reviewers in their decisions concerning outcomes have been developed to increase sensitivity and specificity in the clinical setting. However, these systems have not been well utilized in research settings to improve the measurement of clinical endpoints. Reductions in bias through their use could have important implications for etiologic research.

Methods

Using the example of cortical cataract detection, we developed an algorithm for assisting a reviewer in evaluating digital images for the presence and severity of lesions. Available image processing and statistical methods that were easily implementable were used as the basis for the CAD algorithm. The performance of the system was compared to the subjective assessment of five reviewers using 60 simulated images. Cortical cataract severity scores from 0 to 16 were assigned to the images by the reviewers and the CAD system, with each image assessed twice to obtain a measure of variability. Image characteristics that affected reviewer bias were also assessed by systematically varying the appearance of the simulated images.

Results

The algorithm yielded severity scores with smaller bias on images where cataract severity was mild to moderate (approximately ≤ 6/16ths). On high severity images, the bias of the CAD system exceeded that of the reviewers. The variability of the CAD system was zero on repeated images but ranged from 0.48 to 1.22 for the reviewers. The direction and magnitude of the bias exhibited by the reviewers was a function of the number of cataract opacities, the shape and the contrast of the lesions in the simulated images.

Conclusion

CAD systems are feasible to implement with available software and can be valuable when medical images contain exposure or outcome information for etiologic research. Our results indicate that such systems have the potential to decrease bias and discriminate very small changes in disease severity. Simulated images are a tool that can be used to assess performance of a CAD system when a gold standard is not available.