基于图像分析人工智能的数字健康技术识别非黑色素瘤皮肤癌和其他皮肤病变的有效性：DERM-003研究结果
Effectiveness of an image analyzing AI-based Digital Health Technology to identify Non-Melanoma Skin Cancer and other skin lesions: results of the DERM-003 study.
作者信息
Marsden Helen, Morgan Caroline, Austin Stephanie, DeGiovanni Claudia, Venzi Marcello, Kemos Polychronis, Greenhalgh Jack, Mullarkey Dan, Palamaras Ioulios
机构信息
Skin Analytics Ltd., London, United Kingdom.
Dermatology Unit, University Hospitals Dorset, Poole Hospital, Poole, United Kingdom.
出版信息
Front Med (Lausanne). 2023 Oct 6;10:1288521. doi: 10.3389/fmed.2023.1288521. eCollection 2023.
INTRODUCTION
Identification of skin cancer by an Artificial Intelligence (AI)-based Digital Health Technology could help improve the triage and management of suspicious skin lesions.
METHODS
The DERM-003 study (NCT04116983) was a prospective, multi-center, single-arm, masked study that aimed to demonstrate the effectiveness of an AI as a Medical Device (AIaMD) to identify Squamous Cell Carcinoma (SCC), Basal Cell Carcinoma (BCC), pre-malignant and benign lesions from dermoscopic images of suspicious skin lesions. Suspicious skin lesions that were suitable for photography were photographed with 3 smartphone cameras (iPhone 6S, iPhone 11, Samsung 10) with a DL1 dermoscopic lens attachment. Dermatologists provided clinical diagnoses and histopathology results were obtained for biopsied lesions. Each image was assessed by the AIaMD and the output compared to the ground truth diagnosis.
RESULTS
572 patients (49.5% female, mean age 68.5 years, 96.9% Fitzpatrick skin types I-III) were recruited from 4 UK NHS Trusts, providing images of 611 suspicious lesions. 395 (64.6%) lesions were biopsied; 47 (11%) were diagnosed as SCC and 184 (44%) as BCC. The AIaMD AUROC on images taken by iPhone 6S was 0.88 (95% CI: 0.83-0.93) for SCC and 0.87 (95% CI: 0.84-0.91) for BCC. For Samsung 10 the AUROCs were 0.85 (95% CI: 0.79-0.90) and 0.87 (95% CI, 0.83-0.90), and for the iPhone 11 they were 0.88 (95% CI, 0.84-0.93) and 0.89 (95% CI, 0.86-0.92) for SCC and BCC, respectively. Using pre-determined diagnostic thresholds on images taken on the iPhone 6S the AIaMD achieved a sensitivity and specificity of 98% (95% CI, 88-100%) and 38% (95% CI, 33-44%) for SCC; and 94% (95% CI, 90-97%) and 28% (95 CI, 21-35%) for BCC. All 16 lesions diagnosed as melanoma in the study were correctly classified by the AIaMD.
DISCUSSION
The AIaMD has the potential to support the timely diagnosis of malignant and premalignant skin lesions.
引言
通过基于人工智能（AI）的数字健康技术识别皮肤癌，有助于改善可疑皮肤病变的分诊和管理。
方法
DERM-003研究（NCT04116983）是一项前瞻性、多中心、单臂、盲法研究，旨在证明一种作为医疗设备的人工智能（AIaMD）从可疑皮肤病变的皮肤镜图像中识别鳞状细胞癌（SCC）、基底细胞癌（BCC）、癌前病变和良性病变的有效性。对适合拍照的可疑皮肤病变，使用3部配备DL1皮肤镜镜头附件的智能手机相机（iPhone 6S、iPhone 11、三星10）进行拍照。皮肤科医生提供临床诊断，并获取活检病变的组织病理学结果。每张图像由AIaMD进行评估，并将输出结果与真实诊断进行比较。
结果
从4个英国国民健康服务信托基金招募了572名患者（49.5%为女性，平均年龄68.5岁，96.9%为Fitzpatrick皮肤类型I-III），提供了611个可疑病变的图像。对395个（64.6%）病变进行了活检；47个（11%）被诊断为SCC，184个（44%）被诊断为BCC。AIaMD对iPhone 6S拍摄图像上SCC的曲线下面积（AUROC）为0.88（95%置信区间：0.83-0.93），对BCC为0.87（95%置信区间：0.84-0.91）。对于三星10，SCC和BCC的AUROC分别为0.85（95%置信区间：0.79-0.90）和0.87（95%置信区间，0.83-0.90），对于iPhone 11，SCC和BCC的AUROC分别为0.88（95%置信区间，0.84-0.93）和0.89（95%置信区间，0.86-0.92）。使用iPhone 6S拍摄图像上预先确定的诊断阈值，AIaMD对SCC的敏感性和特异性分别为98%（95%置信区间，88-100%）和38%（95%置信区间，33-44%）；对BCC为94%（95%置信区间，90-97%）和28%（95%置信区间，21-35%）。该研究中诊断为黑色素瘤的所有16个病变均被AIaMD正确分类。
讨论
AIaMD有潜力支持对恶性和癌前皮肤病变的及时诊断。
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