» Articles » PMID: 22940835

Visual Expertise in Detecting and Diagnosing Skeletal Fractures

Overview
Journal Skeletal Radiol
Specialties Orthopedics
Radiology
Date 2012 Sep 4
PMID 22940835
Citations 32
Authors
Affiliations
Soon will be listed here.
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.

Citing Articles

Diagnostic Performance of an Artificial Intelligence Software for the Evaluation of Bone X-Ray Examinations Referred from the Emergency Department.

Diaz Moreno A, Cano Alonso R, Fernandez Alfonso A, Alvarez Vazquez A, Carrascoso Arranz J, Lopez Alcolea J Diagnostics (Basel). 2025; 15(4).

PMID: 40002642 PMC: 11854177. DOI: 10.3390/diagnostics15040491.


The price of pressure: nationwide survey on lifestyle disturbances, occupational burnout and compromised perceived-competency among radiology residents in China.

Liu Z, Yao Q, Wang P, Shen L, Li H, Zhang J Front Public Health. 2024; 12:1472397.

PMID: 39507651 PMC: 11538024. DOI: 10.3389/fpubh.2024.1472397.


Expert gaze as a usability indicator of medical AI decision support systems: a preliminary study.

Castner N, Arsiwala-Scheppach L, Mertens S, Krois J, Thaqi E, Kasneci E NPJ Digit Med. 2024; 7(1):199.

PMID: 39068241 PMC: 11283514. DOI: 10.1038/s41746-024-01192-8.


Discrepancies between Radiology Specialists and Residents in Fracture Detection from Musculoskeletal Radiographs.

Huhtanen J, Nyman M, Sequeiros R, Koskinen S, Pudas T, Kajander S Diagnostics (Basel). 2023; 13(20).

PMID: 37892028 PMC: 10605667. DOI: 10.3390/diagnostics13203207.


Eye tracking reveals expertise-related differences in the time-course of medical image inspection and diagnosis.

Brunye T, Drew T, Kerr K, Shucard H, Weaver D, Elmore J J Med Imaging (Bellingham). 2023; 7(5):051203.

PMID: 37476351 PMC: 10355124. DOI: 10.1117/1.JMI.7.5.051203.


References
1.
Manning D, Ethell S, Donovan T . Detection or decision errors? Missed lung cancer from the posteroanterior chest radiograph. Br J Radiol. 2004; 77(915):231-5. DOI: 10.1259/bjr/28883951. View

2.
Pinto A, Brunese L . Spectrum of diagnostic errors in radiology. World J Radiol. 2010; 2(10):377-83. PMC: 2999012. DOI: 10.4329/wjr.v2.i10.377. View

3.
Kundel H, Nodine C, Conant E, Weinstein S . Holistic component of image perception in mammogram interpretation: gaze-tracking study. Radiology. 2007; 242(2):396-402. DOI: 10.1148/radiol.2422051997. View

4.
KUNDEL H, NODINE C . A visual concept shapes image perception. Radiology. 1983; 146(2):363-8. DOI: 10.1148/radiology.146.2.6849084. View

5.
Myles-Worsley M, Johnston W, Simons M . The influence of expertise on X-ray image processing. J Exp Psychol Learn Mem Cogn. 1988; 14(3):553-7. DOI: 10.1037//0278-7393.14.3.553. View