BACKGROUND: Dermoscopy has been shown to enhance the diagnosis of melanoma. However, use of dermoscopy requires training and expertise to be effective. OBJECTIVES: To determine whether an Internet-based course is a suitable tool in teaching dermoscopy, and to evaluate the diagnostic value of pattern analysis and diagnostic algorithms in colleagues not yet familiar with this technique. METHODS: Sixteen colleagues who were not experts in dermoscopy were asked to evaluate the dermoscopic images of 20 pigmented skin lesions using different diagnostic methods (i.e. pattern analysis, ABCD rule, seven-point checklist and Menzies' method), before and after an Internet-based training course on dermoscopy. Mean +/- SEM sensitivity, specificity and diagnostic accuracy, and kappa (kappa) intraobserver agreement were evaluated for each diagnostic method before and after training for the 16 participants. Differences between mean values were assessed by means of two-tailed Wilcoxon rank-sum tests. RESULTS: There was a considerable improvement in the dermoscopic melanoma diagnosis after the Web-based training vs. before. Improvements in sensitivity and diagnostic accuracy were significant for the ABCD rule and Menzies' method. Improvements in sensitivity were also significant for pattern analysis, whereas the sensitivity values were high for the seven-point checklist in evaluations both before and after training. No significant difference was found for specificity before and after training for any method. There was a significant improvement in the kappa intraobserver agreement after training for pattern analysis and the ABCD rule. For the seven-point checklist and Menzies' method there was already good agreement before training, with no significant improvement after training. CONCLUSIONS: We demonstrated that Web-based training is an effective tool for teaching dermoscopy.
BACKGROUND: Dermoscopy has been shown to enhance the diagnosis of melanoma. However, use of dermoscopy requires training and expertise to be effective. OBJECTIVES: To determine whether an Internet-based course is a suitable tool in teaching dermoscopy, and to evaluate the diagnostic value of pattern analysis and diagnostic algorithms in colleagues not yet familiar with this technique. METHODS: Sixteen colleagues who were not experts in dermoscopy were asked to evaluate the dermoscopic images of 20 pigmented skin lesions using different diagnostic methods (i.e. pattern analysis, ABCD rule, seven-point checklist and Menzies' method), before and after an Internet-based training course on dermoscopy. Mean +/- SEM sensitivity, specificity and diagnostic accuracy, and kappa (kappa) intraobserver agreement were evaluated for each diagnostic method before and after training for the 16 participants. Differences between mean values were assessed by means of two-tailed Wilcoxon rank-sum tests. RESULTS: There was a considerable improvement in the dermoscopic melanoma diagnosis after the Web-based training vs. before. Improvements in sensitivity and diagnostic accuracy were significant for the ABCD rule and Menzies' method. Improvements in sensitivity were also significant for pattern analysis, whereas the sensitivity values were high for the seven-point checklist in evaluations both before and after training. No significant difference was found for specificity before and after training for any method. There was a significant improvement in the kappa intraobserver agreement after training for pattern analysis and the ABCD rule. For the seven-point checklist and Menzies' method there was already good agreement before training, with no significant improvement after training. CONCLUSIONS: We demonstrated that Web-based training is an effective tool for teaching dermoscopy.
Authors: Waqas R Shaikh; Alan Geller; Gwen Alexander; Maryam M Asgari; Gunther J Chanange; Stephen Dusza; Melody J Eide; Suzanne W Fletcher; Jacqueline M Goulart; Allan C Halpern; Shoshana Landow; Ashfaq A Marghoob; Elizabeth A Quigley; Martin A Weinstock Journal: J Cancer Educ Date: 2012-12 Impact factor: 2.037
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Authors: Robert L Williams; Laurie McPherson; Alberta Kong; Betty Skipper; Nancy Weller Journal: J Am Board Fam Med Date: 2009 Jul-Aug Impact factor: 2.657
Authors: Shirin Bajaj; Michael A Marchetti; Cristian Navarrete-Dechent; Stephen W Dusza; Kivanc Kose; Ashfaq A Marghoob Journal: JAMA Dermatol Date: 2016-06-01 Impact factor: 10.282
Authors: Stephen Maloney; Romi Haas; Jennifer L Keating; Elizabeth Molloy; Brian Jolly; Jane Sims; Prue Morgan; Terry Haines Journal: J Med Internet Res Date: 2011-12-22 Impact factor: 5.428
Authors: Jacqueline Dinnes; Jonathan J Deeks; Naomi Chuchu; Rubeta N Matin; Kai Yuen Wong; Roger Benjamin Aldridge; Alana Durack; Abha Gulati; Sue Ann Chan; Louise Johnston; Susan E Bayliss; Jo Leonardi-Bee; Yemisi Takwoingi; Clare Davenport; Colette O'Sullivan; Hamid Tehrani; Hywel C Williams Journal: Cochrane Database Syst Rev Date: 2018-12-04