Dermatology Unit, Department of Medical, Surgical and Neurosciences, University of Siena, Siena, Italy.
Department of Medical Biotechnologies, University of Siena, Siena, Italy.
J Eur Acad Dermatol Venereol. 2020 Mar;34(3):640-647. doi: 10.1111/jdv.15923. Epub 2019 Nov 6.
Although live and teledermoscopic examination has been successfully used to achieve non-invasive diagnosis of melanocytic skin lesions (MSLs), early melanoma (EM) and atypical nevi (AN) continue to be a challenge, and none of the various algorithms proposed have been sufficiently accurate. We designed a scoring classifier diagnostic method, the iDScore that combines clinical data of the patient with dermoscopic features of the MSL.
To test the accuracy of the iDScore in differentiating EM from AN in a teledermoscopy setting and to compare it with intuitive diagnosis, the ABCD rule and the seven-point checklist.
A dedicated teledermoscopy web platform was designed. This involved the following: (i) collecting a large integrated clinical-historical-dermoscopic data set of difficult MSLs from eight European dermatology centres; (ii) online testing, education and training in using the iDScore. A total of 904 images were combined with age, sex, lesion diameter and body site data and evaluated on the platform by 111 participants with four levels of skill in dermoscopy. Each testing session consisted of 30 blind cases to examine consecutively by the above four methods. 'Management decisions' and personal participant data were also recorded.
iDScore-aided diagnosis achieved satisfactory diagnostic accuracy for all lesions, irrespective of centre of affiliation, showing an average AUC of 0.776 in all participant testing sessions. All skill groups improved their accuracy by 10-16% with respect to intuitive diagnosis and the other methods, showing high concordance and avoiding wrong management decisions.
We demonstrated the validity of the iDScore method for managing suspicious MSLs in a large multicentric data set and a teledermoscopic setting. The platform designed for the iDScore project provides ready support for physicians of any dermoscopy skill level and is useful for education and training.
虽然活体检查和远程皮肤镜检查已成功用于实现对黑素细胞皮肤病变（MSL）的非侵入性诊断，但早期黑素瘤（EM）和非典型痣（AN）仍然是一个挑战，并且提出的各种算法都没有足够准确。我们设计了一种评分分类器诊断方法，即 iDScore，该方法将患者的临床数据与 MSL 的皮肤镜特征相结合。
在远程皮肤镜检查中测试 iDScore 区分 EM 和 AN 的准确性，并将其与直观诊断、ABCD 规则和七点检查表进行比较。
设计了一个专门的远程皮肤镜检查网络平台。这涉及以下内容：（i）从 8 个欧洲皮肤科中心收集了一个大型的综合临床历史皮肤镜数据 MSL 数据集；（ii）在线测试、教育和培训使用 iDScore。将 904 张图像与年龄、性别、病变直径和身体部位数据相结合，并由 111 名具有 4 级皮肤镜技能的参与者在平台上进行评估。每个测试会话由 30 个盲法病例组成，由上述四种方法依次进行检查。还记录了“管理决策”和个人参与者数据。
iDScore 辅助诊断对所有病变均具有令人满意的诊断准确性，无论所属中心如何，在所有参与者的测试会议中平均 AUC 为 0.776。所有技能组相对于直观诊断和其他方法，其准确性提高了 10-16%，显示出高度一致性并避免了错误的管理决策。
我们在大型多中心数据集和远程皮肤镜检查环境中证明了 iDScore 方法管理可疑 MSL 的有效性。为 iDScore 项目设计的平台为任何皮肤镜技能水平的医生提供了现成的支持，并且对教育和培训很有用。
