Advertisement

Skeletal Radiology

, Volume 42, Issue 2, pp 165–172 | Cite as

Visual expertise in detecting and diagnosing skeletal fractures

  • Greg Wood
  • Karen M. Knapp
  • Benjamin Rock
  • Chris Cousens
  • Carl Roobottom
  • Mark R. Wilson
Scientific Article

Abstract

Objective

Failure to identify fractures is the most common error in accident and emergency departments. Therefore, the current research aimed to understand more about the processes underlying perceptual expertise when interpreting skeletal radiographs.

Materials and methods

Thirty participants, consisting of ten novices, ten intermediates, and ten experts were presented with ten clinical cases of normal and abnormal skeletal radiographs of varying difficulty (obvious or subtle) while wearing eye tracking equipment.

Results

Experts were significantly more accurate, more confident, and faster in their diagnoses than intermediates or novices and this performance advantage was more pronounced for the subtle cases. Experts were also faster to fixate the site of the fracture and spent more relative time fixating the fracture than intermediates or novices and this was again most pronounced for subtle cases. Finally, a multiple linear regression analysis found that time to fixate the fracture was inversely related to diagnostic accuracy and explained 34 % of the variance in this variable.

Conclusions

The results suggest that the performance advantage of expert radiologists is underpinned by superior pattern recognition skills, as evidenced by a quicker time to first fixate the pathology, and less time spent searching the image.

Keywords

Visual search Gaze behavior Interpretation Eye movements 

Notes

Acknowledgments

The authors declare that they have no conflicts of interest.

References

  1. 1.
    Krupinski EA. The importance of perception research in medical imaging. Radiat Med. 2000;18:329–34.PubMedGoogle Scholar
  2. 2.
    Kundel HL, Nodine CF. A visual concept shapes image perception. Radiology. 1983;146:363–8.PubMedGoogle Scholar
  3. 3.
    Richler J, Cheung O, Gauthier I. Holistic processing predicts face recognition. Psych Sci. 2011;22:464–71.CrossRefGoogle Scholar
  4. 4.
    Mello-Thoms C. The problem of image interpretation in mammography: Effects of lesion conspicuity on the visual search strategy of radiologists. Br J Radiol. 2006;79:S111–6.PubMedCrossRefGoogle Scholar
  5. 5.
    Kundel HL, Nodine CF, Conant EF, Weinstein SP. Holistic component of image perception in mammogram interpretation. Radiology. 2007;242:396–402.PubMedCrossRefGoogle Scholar
  6. 6.
    Kundel HL, Nodine CF, Krupinski EA, Mello-Thoms C. Using gaze-tracking data and mixture distribution analysis to support a holistic model for the detection of cancers on mammograms. Acad Radiol. 2008;15:881–6.PubMedCrossRefGoogle Scholar
  7. 7.
    Manning DJ, Ethell SC, Donovan T. Detection or decision errors? Missed lung cancer from the posteroanterior chest radiograph. Br J Radiol. 2004;77:231–5.PubMedCrossRefGoogle Scholar
  8. 8.
    Manning D, Barker-Mill SC, Donovan T, Crawford T. Time-dependent observer errors in pulmonary module detection. Br J Radiol. 2005;79:342–6.CrossRefGoogle Scholar
  9. 9.
    Manning D, Ethell S, Donovan T, Crawford T. How do radiologists do it? The influence of experience and training on searching for chest nodules. Radiography. 2006;12:134–42.CrossRefGoogle Scholar
  10. 10.
    Nodine CF, Mello-Thoms C, Kundel HL, Weinstein SP. Time course of perception and decision-making during mammographic interpretation. AJR. 2002;179:917–23.PubMedGoogle Scholar
  11. 11.
    Pinto A, Brunese L. Spectrum of diagnostic errors in radiology. World J Radiol. 2010;2:377–83.PubMedCrossRefGoogle Scholar
  12. 12.
    Leong JJH, Nicolaou M, Emery RJ, Darzi AW, Yang G-Z. Visual search behaviour in skeletal radiographs: a cross-speciality study. Clin Radiol. 2007;62:1069–77.PubMedCrossRefGoogle Scholar
  13. 13.
    Hu CH, Kundell HL, Nodine CF, Krupinski EA, Toto LC. Searching for bone fractures: a comparison with pulmonary module search. Acad Radiol. 1994;1:25–32.PubMedCrossRefGoogle Scholar
  14. 14.
    Kundel HL, Nodine CF, Krupinski EA. Searching for lung nodules. Visual dwell indicates locations of false positive and false-negative decisions. Invest Radiol. 1989;24:472–8.PubMedCrossRefGoogle Scholar
  15. 15.
    Myles-Worsley M, Johnston W, Simons MA. The influence of expertise on x-ray image processing. J Exp Psychol Learn Mem Cogn. 1988;14:553–37.PubMedCrossRefGoogle Scholar

Copyright information

© ISS 2012

Authors and Affiliations

  • Greg Wood
    • 1
  • Karen M. Knapp
    • 2
  • Benjamin Rock
    • 3
  • Chris Cousens
    • 3
  • Carl Roobottom
    • 3
  • Mark R. Wilson
    • 1
  1. 1.College of Life and Environmental SciencesUniversity of ExeterExeterUK
  2. 2.College of Engineering, Mathematics, and Physical SciencesUniversity of ExeterExeterUK
  3. 3.Peninsula Radiology AcademyPlymouthUK

Personalised recommendations