Africa faces a huge shortage of dermatologists, with less than one per million people. This is in stark contrast to the high demand for dermatologic care, with 80% of the paediatric population suffering from largely untreated skin conditions. The integration of AI into healthcare sparks significant hope for treatment accessibility, especially through the development of AI-supported teledermatology. Current AI models are predominantly trained on white-skinned patients and do not generalize well enough to pigmented patients. The PASSION project aims to address this issue by collecting images of skin diseases in Sub-Saharan countries with the aim of open-sourcing this data. This dataset is the first of its kind, consisting of 1,653 patients for a total of 4,901 images. The images are representative of telemedicine settings and encompass the most common paediatric conditions: eczema, fungals, scabies, and impetigo. We also provide a baseline machine learning model trained on the dataset and a detailed performance analysis for the subpopulations represented in the dataset. The project website can be found at https://passionderm.github.io/.

翻译：非洲面临严重的皮肤科医生短缺问题，每百万人中皮肤科医生不足一名。这与皮肤病治疗的高需求形成鲜明对比——80%的儿科人群患有基本未获治疗的皮肤病。人工智能与医疗保健的融合为治疗可及性带来了巨大希望，特别是通过开发AI支持的远程皮肤病学。当前AI模型主要基于白肤色患者数据训练，对色素沉着患者的泛化能力不足。PASSION项目旨在通过收集撒哈拉以南国家皮肤病图像来解决这一问题，并计划开源这些数据。该数据集是同类首个数据集，包含1,653名患者共计4,901张图像。这些图像具有远程医疗场景代表性，涵盖最常见的儿科病症：湿疹、真菌感染、疥疮和脓疱病。我们还提供了基于该数据集训练的基准机器学习模型，并对数据集中各亚群进行了详细的性能分析。项目网站地址：https://passionderm.github.io/。