Skin cancer is currently the most common type of cancer among Caucasians. The increase in life expectancy, along with new diagnostic tools and treatments for skin cancer, has resulted in unprecedented changes in patient care and has generated a great burden on healthcare systems. Early detection of skin tumors is expected to reduce this burden. Artificial intelligence (AI) algorithms that support skin cancer diagnoses have been shown to perform at least as well as dermatologists' diagnoses. Recognizing the need for clinically and economically efficient means of diagnosing skin cancers at early stages in the primary care attention, we developed an efficient computer-aided diagnosis (CAD) system to be used by primary care physicians (PCP). Additionally, we developed a smartphone application with a protocol for data acquisition (i.e., photographs, demographic data and short clinical histories) and AI algorithms for clinical and dermoscopic image classification. For each lesion analyzed, a report is generated, showing the image of the suspected lesion and its respective Heat Map; the predicted probability of the suspected lesion being melanoma or malignant; the probable diagnosis based on that probability; and a suggestion on how the lesion should be managed. The accuracy of the dermoscopy model for melanoma was 89.3%, and for the clinical model, 84.7% with 0.91 and 0.89 sensitivity and 0.89 and 0.83 specificity, respectively. Both models achieved an area under the curve (AUC) above 0.9. Our CAD system can screen skin cancers to guide lesion management by PCPs, especially in the contexts where the access to the dermatologist can be difficult or time consuming. Its use can enable risk stratification of lesions and/or patients and dramatically improve timely access to specialist care for those requiring urgent attention.

中文翻译：

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在初级保健环境中实施用于黑色素瘤筛查的人工智能算法。


皮肤癌是目前白种人中最常见的癌症类型。预期寿命的延长，以及新的皮肤癌诊断工具和治疗方法，导致患者护理发生了前所未有的变化，并给医疗保健系统带来了巨大的负担。早期发现皮肤肿瘤有望减轻这种负担。支持皮肤癌诊断的人工智能 (AI) 算法已被证明至少与皮肤科医生的诊断一样好。认识到在初级保健关注的早期阶段需要临床和经济有效的手段来诊断皮肤癌，我们开发了一种高效的计算机辅助诊断 (CAD) 系统，供初级保健医生 (PCP) 使用。此外，我们还开发了一款智能手机应用程序，其中包含数据采集协议（即照片、人口统计数据和简短的临床病史）以及用于临床和皮肤镜图像分类的人工智能算法。对于分析的每个病变，都会生成一份报告，显示可疑病变的图像及其各自的热图；可疑病变为黑色素瘤或恶性的预测概率；基于该概率的可能诊断；以及如何处理病变的建议。皮肤镜模型对黑色素瘤的准确率为 89.3%，临床模型的准确率为 84.7%，敏感性分别为 0.91 和 0.89，特异性为 0.89 和 0.83。两种模型的曲线下面积 (AUC) 均高于 0.9。我们的 CAD 系统可以筛查皮肤癌，以指导 PCP 进行病变管理，特别是在接触皮肤科医生可能很困难或耗时的情况下。 它的使用可以对病变和/或患者进行风险分层，并极大地改善那些需要紧急关注的人及时获得专科护理的机会。