小柯机器人

英国伯明翰大学Jonathan J Deeks小组宣布他们研究了基于智能手机app的算法来评估成人皮肤癌风险的准确性。这一研究成果2020年2月10日发表在国际顶尖学术期刊《英国医学杂志》上。

为了评估基于智能手机应用程序（apps）的算法来评估可疑皮肤病变中皮肤癌风险的准确性和有效性，研究组在MEDLINE、Embase、CPCI、科学引文索引等大型数据库中检索从建库到2019年4月10日的相关文献，对智能手机app的诊断准确性进行了系统回顾和荟萃分析。

共有9项研究评估了6种可识别的智能手机app，其中6项根据组织学或随访来证实结果（725个病灶），3项通过专家建议证实结果（407个病灶）。研究规模小，方法学质量差，招募有偏倚，大量图像无价值，验证存在差异。病变选择和图像采集由临床医生而不是智能手机用户完成。有两个CE标记的应用程序可供下载。

一项研究评估了SkinScan app（15例参与者，5例黑色素瘤），检测黑色素瘤的敏感性为0%，特异性为100%。有两项研究（252例参与者，61例恶性或癌前病变）评估了SkinVision app，敏感性为80%，特异性为78%。根据专家建议验证，SkinVision app的准确性较差。

目前基于智能手机app的算法无法检测出所有的黑色素瘤或其他皮肤癌患者，实际测试性能比报告的要差。对基于算法的app授予CE认证的监管程序未能为公众提供足够的保护。

附：英文原文

Title: Algorithm based smartphone apps to assess risk of skin cancer in adults: systematic review of diagnostic accuracy studies

Author: Karoline Freeman, Jacqueline Dinnes, Naomi Chuchu, Yemisi Takwoingi, Sue E Bayliss, Rubeta N Matin, Abhilash Jain, Fiona M Walter, Hywel C Williams, Jonathan J Deeks

Issue&Volume: 2020/02/10

Abstract:

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.

DOI: 10.1136/bmj.m127

Source: https://www.bmj.com/content/368/bmj.m127

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