Department of Dermatology, Copenhagen University Hospital - Bispebjerg, Copenhagen, Denmark.
Department of Dermatology, Private Hospital Molholm, Vejle, Denmark.
Skin Res Technol. 2024 Oct;30(10):e70056. doi: 10.1111/srt.70056.
The development of artificial intelligence (AI) is rapidly expanding, showing promise in the dermatological field. Skin checks are a resource-heavy challenge that could potentially benefit from AI-tool assistance, particularly if provided in widely available AI solutions. A novel smartphone application(app)-based AI system, "SCAI," was developed and trained to recognize spots in paired images of skin, pursuing identification of new skin lesions. This pilot study aimed to investigate the feasibility of the SCAI-app to identify simulated skin changes in vivo.
The study was conducted in a controlled setting with healthy volunteers and standardized, simulated skin changes (test spots), consisting of customized 3-mm adhesive spots in three colors (black, brown, and red). Each volunteer had a total of eight test spots adhered to four areas on back and legs. The SCAI-app collected smartphone- and template-guided standardized images before and after test spot application, using its backend AI algorithms to identify changes between the paired images.
Twenty-four volunteers were included, amounting to a total of 192 test spots. Overall, the detection algorithms identified test spots with a sensitivity of 92.0% (CI: 88.1-95.9) and a specificity of 95.5% (CI: 95.0-96.0). The SCAI-app's positive predictive value was 38.0% (CI: 31.0-44.9), while the negative predictive value was 99.7% (CI: 99.0-100).
This pilot study showed that SCAI-app could detect simulated skin changes in a controlled in vivo setting. The app's feasibility in a clinical setting with real-life skin lesions remains to be investigated, where the challenge with false positives in particular needs to be addressed.
人工智能（AI）的发展迅速扩展，在皮肤科领域显示出前景。皮肤检查是一项资源密集型的挑战，可能受益于 AI 工具的辅助，特别是如果提供广泛可用的 AI 解决方案。开发并训练了一种新的基于智能手机应用程序（app）的 AI 系统“ SCAI”，以识别皮肤配对图像中的斑点，旨在识别新的皮肤病变。这项初步研究旨在调查 SCAI-app 识别体内模拟皮肤变化的可行性。
该研究在受控环境中进行，有健康志愿者和标准化的模拟皮肤变化（测试点）参与，测试点由三种颜色（黑色，棕色和红色）的定制 3mm 粘性斑点组成。每个志愿者的背部和腿部的四个区域总共贴有八个测试点。 SCAI-app 使用其后端 AI 算法在应用测试点前后采集智能手机和模板引导的标准化图像，以识别配对图像之间的变化。
共纳入 24 名志愿者，总计 192 个测试点。总体而言，检测算法识别测试点的敏感性为 92.0％（置信区间：88.1-95.9），特异性为 95.5％（置信区间：95.0-96.0）。 SCAI-app 的阳性预测值为 38.0％（置信区间：31.0-44.9），而阴性预测值为 99.7％（置信区间：99.0-100）。
这项初步研究表明，SCAI-app 可以在受控的体内环境中检测模拟皮肤变化。该应用程序在具有真实皮肤病变的临床环境中的可行性仍有待研究，特别是需要解决假阳性的挑战。
