Measures of Diagnostic Accuracy: Basic Definitions

By A. Šimundić. EJIFCC, 2009

1,526 citations recorded by the source corpus.

Diagnostic accuracy relates to the ability of a test to discriminate between the target condition and health. This discriminative potential can be quantified by the measures of diagnostic accuracy such as sensitivity and specificity, predictive values, likelihood ratios, the area under the ROC curve, Youden's index and diagnostic odds ratio. Different measures of diagnostic accuracy relate to the different aspects of diagnostic procedure: while some measures are used to assess the discriminative property of the test, others are used to assess its predictive ability. Measures of diagnostic accuracy are not fixed indicators of a test performance, some are very sensitive to the disease prevalence, while others to the spectrum and definition of the disease. Furthermore, measures of diagnostic accuracy are extremely sensitive to the design of the study. Studies not meeting strict methodological standards usually over- or under-estimate the indicators of test performance as well as they limit the applicability of the results of the study. STARD initiative was a very important step toward the improvement the quality of reporting of studies of diagnostic accuracy. STARD statement should be included into the Instructions to authors by scientific journals and authors should be encouraged to use the checklist whenever reporting their studies on diagnostic accuracy. Such efforts could make a substantial difference in the quality of reporting of studies of diagnostic accuracy and serve to provide the best possible evidence to the best for the patient care. This brief review outlines some basic definitions and characteristics of the measures of diagnostic accuracy.

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Citations in the corpus

Systematic review of research on artificial intelligence applications in higher education – where are the educators? (2019, background): “…of positive instances correctly classified as positive, the number of negative instances incorrectly classified as positive, and the number of positive instances incorrectly classified as negative (Umer et al., 2017; for a brief overview of measures of diagnostic accuracy, see Šimundić, 2009).”
Evaluation metrics and statistical tests for machine learning (2024, background): “− 1 15 , gives an equal weight to the accuracies within the positive and the negative instances, regardless of their numbers.”
The development and psychometric properties evaluation of the Indonesian version of the Wechsler Adult Intelligence Scale - Fourth Edition (WAIS-IV) (2018, background): “Sensitivity is the probability of getting a positive test result from an individual with a clinical condition while specificity is the probability of obtaining a negative test result from an individual without a clinical condition (Šimundić, 2008).”