Artificial Intelligence Smartphone Application for Detection of Simulated Skin Changes: An In Vivo Pilot Study.

IF 2 4区 医学 Q3 DERMATOLOGY
Gabriela Lladó Grove, Gorm Reedtz, Brian Vangsgaard, Hassan Eskandarani, Merete Haedersdal, Flemming Andersen, Peter Bjerring
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Abstract

Background: 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.

Materials and methods: 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.

Results: 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).

Conclusion: 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.

用于检测模拟皮肤变化的人工智能智能手机应用程序:体内试验研究。
背景:人工智能(AI)的发展日新月异,在皮肤科领域大有可为。皮肤检查是一项耗费大量资源的挑战,人工智能工具的辅助可能会使其受益,尤其是在广泛提供人工智能解决方案的情况下。我们开发了一种基于智能手机应用程序(App)的新型人工智能系统 "SCAI",并对其进行了训练,以识别配对皮肤图像中的斑点,进而识别新的皮肤病变。这项试验性研究旨在调查 SCAI 应用程序识别体内模拟皮肤变化的可行性:研究在受控环境下进行,由健康志愿者和标准化的模拟皮肤变化(测试点)组成,测试点由定制的三种颜色(黑色、棕色和红色)的 3 毫米胶点组成。每位志愿者背部和腿部的四个部位共粘有八个测试点。SCAI 应用程序通过智能手机和模板收集测试点粘贴前后的标准化图像,并利用其后台人工智能算法识别配对图像之间的变化:结果:共有 24 名志愿者参与了检测,检测点共计 192 个。总体而言,检测算法识别测试点的灵敏度为 92.0%(CI:88.1-95.9),特异性为 95.5%(CI:95.0-96.0)。SCAI-app的阳性预测值为38.0%(CI:31.0-44.9),阴性预测值为99.7%(CI:99.0-100):这项试点研究表明,SCAI 应用程序可在受控的活体环境中检测模拟皮肤变化。该应用程序在真实皮肤病变的临床环境中的可行性仍有待研究,尤其需要解决假阳性的难题。
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来源期刊
Skin Research and Technology
Skin Research and Technology 医学-皮肤病学
CiteScore
3.30
自引率
9.10%
发文量
95
审稿时长
6-12 weeks
期刊介绍: Skin Research and Technology is a clinically-oriented journal on biophysical methods and imaging techniques and how they are used in dermatology, cosmetology and plastic surgery for noninvasive quantification of skin structure and functions. Papers are invited on the development and validation of methods and their application in the characterization of diseased, abnormal and normal skin. Topics include blood flow, colorimetry, thermography, evaporimetry, epidermal humidity, desquamation, profilometry, skin mechanics, epiluminiscence microscopy, high-frequency ultrasonography, confocal microscopy, digital imaging, image analysis and computerized evaluation and magnetic resonance. Noninvasive biochemical methods (such as lipids, keratin and tissue water) and the instrumental evaluation of cytological and histological samples are also covered. The journal has a wide scope and aims to link scientists, clinical researchers and technicians through original articles, communications, editorials and commentaries, letters, reviews, announcements and news. Contributions should be clear, experimentally sound and novel.
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