Assessing Diagnostic Confidence

Ng, Chaan S.; Palmer, C.

doi:10.1016/j.acra.2007.12.004

Cited by 9 publications

(1 citation statement)

References 20 publications

(27 reference statements)

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“…To our knowledge, a number of analytic methods have been proposed for this purpose [6], especially in order to take into account the impact of incorrectly confident diagnoses on patients management [2, 7]. In general, these methods assess the changes between pre- and posttest confidences of referring physicians by using proportions or scoring systems (e.g., 1 to 5 scale) rather than a continuum of information.…”

Section: Introductionmentioning

confidence: 99%

Differentiating Small (≤1 cm) Focal Liver Lesions as Metastases or Cysts by means of Computed Tomography: A Case-Study to Illustrate a Fuzzy Logic-Based Method to Assess the Impact of Diagnostic Confidence on Radiological Diagnosis

Girometti

Fabris

Sgarro

et al. 2014

Computational and Mathematical Methods in Medicine

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Purpose. To quantify the impact of diagnostic confidence on radiological diagnosis with a fuzzy logic-based method. Materials and Methods. Twenty-two oncologic patients with 20 cysts and 30 metastases ≤1 cm in size found at 64-row computed tomography were included. Two readers (R1/R2) expressed diagnoses as a subjective level of confidence P(d) in malignancy within the interval [0,1] rather than on a “crisp” basis (malignant/benign); confidence in benignancy was 1 − p(d). When cross-tabulating data according to the standard of reference, 2 × 2 table cells resulted from the aggregation between p(d)/1 − p(d) and final diagnosis. We then assessed (i) readers diagnostic performance on a fuzzy and crisp basis; (ii) the “divergence” δ(F, C) (%) as a measure of how confidence impacted on crisp diagnosis. Results. Diagnoses expressed with lower confidence increased fuzzy false positives compared to crisp ones (from 0 to 0.2 for R1; from 1 to 2.4 for R2). Crisp/fuzzy accuracy was 94.0%/93.6% (R1) and 94.0/91.6% (R2). δ(F, C) (%) was larger in the case of the less experienced reader (R2) (up to +7.95% for specificity). According to simulations, δ(F, C) (%) was negative/positive depending on the level of confidence in incorrect diagnoses. Conclusion. Fuzzy evaluation shows a measurable effect of uncertainty on radiological diagnoses.

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Section: Introductionmentioning

confidence: 99%