Utility of non-rule-based visual matching as a strategy to allow novices to achieve skin lesion diagnosis

Abstract

Non-analytical reasoning is thought to play a key role in dermatology diagnosis. Considering its potential importance, surprisingly little work has been done to research whether similar identification processes can be supported in non-experts. We describe here a prototype diagnostic support software, which we have used to examine the ability of medical students (at the beginning and end of a dermatology attachment) and lay volunteers, to diagnose 12 images of common skin lesions. Overall, the non-experts using the software had a diagnostic accuracy of 98% (923/936) compared with 33% for the control group (215/648) (Wilcoxon p < 0.0001). We have demonstrated, within the constraints of a simplified clinical model, that novices' diagnostic scores are significantly increased by the use of a structured image database coupled with matching of index and referent images. The novices achieve this high degree of accuracy without any use of explicit definitions of likeness or rule-based strategies.

Fig. 1

Screenshots from the software showing how a correct diagnostic match could be made for index/test image 11 (a seborrhoeic keratosis). The boxes highlight the user’s selections at each of the three levels. A video of the software in action is available to view on YouTube (14).

Fig. 2

Plot of all 60 students’ scores by group and test date, and the 20 lay novices’ scores. The maximum score of 12 is achieved by correctly identifying all the test images. Day 1 control group (n = 28, median score 1), Day 10 control group (n = 26, median score 6), Day 1 software group (n = 30, median score 12), Day 10 software group (n = 28, median score 12). Lay group score (n = 20, median score 12).