Title
Differences in examination characteristics of pigmented skin lesions: results of an eye tracking study.
Abstract
To use computer-based eye tracking technology to record and evaluate examination characteristics of the diagnosis of pigmented skin lesions.16 study participants with varying levels of diagnostic expertise (little, intermediate, superior) were recorded while diagnosing a series of 28 digital images of pigmented skin lesions, obtained by non-invasive digital dermatoscopy, on a computer screen. Eye tracking hardware recorded the gaze track and fixations of the physicians while they examined the lesion images. Analysis of variance was used to test for differences in examination characteristics between physicians grouped according to expertise.There were no significant differences between physicians with little and intermediate levels of expertise in terms of average time until diagnosis (6.61 vs. 6.19s), gaze track length (6.65 vs. 6.15 kilopixels), number of fixations (23.1 vs. 19.1), and time in fixations (4.91 vs. 4.17s). The experts were significantly different with 3.17s time until diagnosis, 4.53 kilopixels gaze track length, 9.9 fixations, and 1.74s in fixations, respectively. Differentiation between benign and malignant lesions had no effect on examination measurements.The results show that experience level has a significant impact on the way in which lesion images are examined. This finding can be used to construct decision support systems that employ important diagnostic features identified by experts, and to optimize teaching for less experienced physicians.
Year
DOI
Venue
2012
10.1016/j.artmed.2011.11.004
Artificial Intelligence In Medicine
Keywords
Field
DocType
examination characteristic,eye tracking study,diagnostic expertise,track length,examination measurement,eye tracking hardware,average time,computer-based eye tracking technology,digital image,lesion image,pigmented skin lesion
Computer vision,Fixation (psychology),Lesion,Pigmented skin,Gaze,Computer science,Dermatoscopy,Eye tracking,Optometry,Artificial intelligence,Machine learning
Journal
Volume
Issue
ISSN
54
3
1873-2860
Citations 
PageRank 
References 
1
0.37
0
Authors
3
Name
Order
Citations
PageRank
Stephan Dreiseitl133834.80
Maja Pivec29910.55
Michael Binder310.37