Combining independent decisions increases diagnostic accuracy of reading lumbosacral radiographs and magnetic resonance imaging

Ralf H J M Kurvers, Annemarie de Zoete, Shelby L Bachman, Paul R Algra, Raymond Ostelo

Research output: Contribution to JournalArticleAcademicpeer-review

Abstract

Diagnosing the causes of low back pain is a challenging task, prone to errors. A novel approach to increase diagnostic accuracy in medical decision making is collective intelligence, which refers to the ability of groups to outperform individual decision makers in solving problems. We investigated whether combining the independent ratings of chiropractors, chiropractic radiologists and medical radiologists can improve diagnostic accuracy when interpreting diagnostic images of the lumbosacral spine. Evaluations were obtained from two previously published studies: study 1 consisted of 13 raters independently rating 300 lumbosacral radiographs; study 2 consisted of 14 raters independently rating 100 lumbosacral magnetic resonance images. In both studies, raters evaluated the presence of "abnormalities", which are indicators of a serious health risk and warrant immediate further examination. We combined independent decisions of raters using a majority rule which takes as final diagnosis the decision of the majority of the group. We compared the performance of the majority rule to the performance of single raters. Our results show that with increasing group size (i.e., increasing the number of independent decisions) both sensitivity and specificity increased in both data-sets, with groups consistently outperforming single raters. These results were found for radiographs and MR image reading alike. Our findings suggest that combining independent ratings can improve the accuracy of lumbosacral diagnostic image reading.

Original languageEnglish
Article numbere0194128
Pages (from-to)1-17
Number of pages17
JournalPLoS ONE
Volume13
Issue number4
DOIs
Publication statusPublished - 3 Apr 2018

Keywords

  • Algorithms
  • Clinical Decision-Making/methods
  • Humans
  • Image Processing, Computer-Assisted
  • Lumbosacral Region/diagnostic imaging
  • Magnetic Resonance Imaging/methods
  • Radiography/methods
  • Reproducibility of Results
  • Sample Size
  • Sensitivity and Specificity
  • Workflow

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