OBJECTIVE To examine the validity and findings of studies that examine the accuracy of algorithm based smartphone applications ("apps") to assess risk of skin cancer in suspicious skin lesions. DESIGN Systematic review of diagnostic accuracy studies. DATA SOURCES Cochrane Central Register of Controlled Trials, MEDLINE, Embase, CINAHL, CPCI, Zetoc, Science Citation Index, and online trial registers (from database inception to 10 April 2019). ELIGIBILITY CRITERIA FOR SELECTING STUDIES Studies of any design that evaluated algorithm based smartphone apps to assess images of skin lesions suspicious for skin cancer. Reference standards included histological diagnosis or follow-up, and expert recommendation for further investigation or intervention. Two authors independently extracted data and assessed validity using QUADAS-2 (Quality Assessment of Diagnostic Accuracy Studies 2 tool). Estimates of sensitivity and specificity were reported for each app. RESULTS Nine studies that evaluated six different identifiable smartphone apps were included. Six verified results by using histology or follow-up (n=725 lesions), and three verified results by using expert recommendations (n=407 lesions). Studies were small and of poor methodological quality, with selective recruitment, high rates of unevaluable images, and differential verification. Lesion selection and image acquisition were performed by clinicians rather than smartphone users. Two CE (Conformit Europenne) marked apps are available for download. SkinScan was evaluated in a single study (n=15, five melanomas) with 0% sensitivity and 100% specificity for the detection of melanoma. SkinVision was evaluated in two studies (n=252, 61 malignant or premalignant lesions) and achieved a sensitivity of 80% (95% confidence interval 63% to 92%) and a specificity of 78% (67% to 87%) for the detection of malignant or premalignant lesions. Accuracy of the SkinVision app verified against expert recommendations was poor (three studies). CONCLUSIONS Current algorithm based smartphone apps cannot be relied on to detect all cases of melanoma or other skin cancers. Test performance is likely to be poorer than reported here when used in clinically relevant populations and by the intended users of the apps. The current regulatory process for awarding the CE marking for algorithm based apps does not provide adequate protection to the public. SYSTEMATIC REVIEW REGISTRATION PROSPERO CRD42016033595.

中文翻译：

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基于算法的智能手机应用程序评估成人皮肤癌的风险：诊断准确性研究的系统综述。

目的检验基于算法的智能手机应用程序（“ apps”）评估可疑皮肤病变中皮肤癌风险的准确性的研究的有效性和研究结果。设计诊断准确性研究的系统评价。数据源Cochrane对照试验中央注册，MEDLINE，Embase，CINAHL，CPCI，Zetoc，《科学引文索引》和在线试验注册（从数据库成立到2019年4月10日）。选择研究的资格标准任何设计的研究都会评估基于算法的智能手机应用程序，以评估可疑皮肤癌的皮肤病变图像。参考标准包括组织学诊断或随访，以及进一步研究或干预的专家建议。两位作者独立提取数据，并使用QUADAS-2（诊断准确性研究2的质量评估工具）评估了有效性。报告了每个应用的敏感性和特异性估计。结果纳入了九项研究，这些研究评估了六个不同的可识别智能手机应用程序。通过组织学或随访证实了六个结果（n = 725个病灶），通过专家推荐证实了三个结果（n = 407个病灶）。研究规模小，方法学质量差，选择性招募，无法评估的图像率高和差异验证。病变选择和图像采集是由临床医生而不是智能手机用户执行的。可以下载两个带有CE（欧洲标准）标记的应用程序。一项研究对SkinScan进行了评估（n = 15，5个黑色素瘤），检测黑色素瘤的灵敏度为0％，特异性为100％。在两项研究（n = 252、61个恶性或恶变前病变）中对SkinVision进行了评估，其对皮肤的敏感性为80％（95％置信区间63％至92％），特异性为78％（67％至87％）。检测恶性或恶变前病变。根据专家建议验证的SkinVision应用程序的准确性很差（三项研究）。结论不能依靠当前基于算法的智能手机应用程序来检测所有黑色素瘤或其他皮肤癌病例。当在临床相关人群中以及由应用程序的预定用户使用时，测试性能可能会比此处报告的要差。当前针对基于算法的应用授予CE标记的监管程序并未为公众提供足够的保护。