Dermatology Department, Stanford University School of Medicine, Redwood City, California, USA.
Memorial Sloan Kettering Cancer Center, New York City, New York, USA.
J Invest Dermatol. 2023 Jul;143(7):1127-1132. doi: 10.1016/j.jid.2023.02.035.
Recent advances in artificial intelligence research have led to an increase in the development of algorithms for detecting malignancies from clinical and dermoscopic images of skin diseases. These methods are dependent on the collection of training and testing data. There are important considerations when acquiring skin images and data for translational artificial intelligence research. In this paper, we discuss the best practices and challenges for light photography image data collection, covering ethics, image acquisition, labeling, curation, and storage. The purpose of this work is to improve artificial intelligence for malignancy detection by supporting intentional data collection and collaboration between subject matter experts, such as dermatologists and data scientists.
近年来，人工智能研究的进展导致了用于从皮肤疾病的临床和皮肤镜图像中检测恶性肿瘤的算法的开发增加。这些方法依赖于训练和测试数据的收集。在获取用于转化人工智能研究的皮肤图像和数据时，有一些重要的考虑因素。在本文中，我们讨论了用于光摄影图像数据采集的最佳实践和挑战，涵盖了道德、图像采集、标记、策展和存储。这项工作的目的是通过支持有目的的数据收集和主题专家（如皮肤科医生和数据科学家）之间的协作，来改进用于恶性肿瘤检测的人工智能。
