Perilesional sun damage as a diagnostic clue for pigmented actinic keratosis and Bowen's disease

被引:4
作者
Weber, P. [1 ]
Sinz, C. [1 ]
Rinner, C. [2 ]
Kittler, H. [1 ]
Tschandl, P. [1 ]
机构
[1] Med Univ Vienna, Dept Dermatol, Vienna, Austria
[2] Med Univ Vienna, Ctr Med Stat Informat & Intelligent Syst, Vienna, Austria
关键词
DERMATOSCOPY; DERMOSCOPY;
D O I
10.1111/jdv.17464
中图分类号
R75 [皮肤病学与性病学];
学科分类号
100206 ;
摘要
Background Chronic sun damage in the background is common in pigmented actinic keratoses and Bowen's disease (pAK/BD). While explainable artificial intelligence (AI) demonstrated increased background attention for pAK/BD, humans frequently miss this clue in dermatoscopic images because they tend to focus on the lesion. Aim To analyse whether perilesional sun damage is a robust diagnostic clue for pAK/BD and if teaching this clue to dermatoscopy users improves their diagnostic accuracy. Methods We assessed the interrater agreement and the frequency of perilesional sun damage in 220 dermatoscopic images and conducted a reader study with 124 dermatoscopy users. The readers were randomly assigned to one of two online tutorials; one tutorial pointed to perilesional sun damage as a clue to pAK/BD (group A) the other did not (group B). In both groups, we compared the frequencies of correct diagnoses before and after receiving the tutorial. Results The frequency of perilesional sun damage was higher in pAK/BD than in other types of pigmented skin lesions and interrater agreement was good (kappa = 0.675). The diagnostic accuracy for pAK/BD improved in both groups of readers (group A: +16.1%, 95%-CI: 9.5-22.7; group B: +13.1%; 95%-CI: 7.1-19.0; P for both <0.001), but the overall accuracy improved only in group A from (59.1% (95%-CI: 55.0-63.1) to 63.5% (95%-CI: 59.5-67.6); P = 0.002). Conclusion Perilesional sun damage is a good clue to differentiate pAK/BD from other pigmented skin lesions in dermatoscopic images, which could be useful for teledermatology. Knowledge of this clue improves the accuracy of dermatoscopy users, which demonstrates that insights from explainable AI can be used to train humans.
引用
收藏
页码:2022 / 2026
页数:5
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