Hereditary haemorrhagic telangiectasia (HHT) is a rare vascular multisystemic disease that leads to epistaxis, anaemia due to blood loss, and arteriovenous malformations (AVMs) in organs such as the lungs, liver and brain. HHT prevalence is estimated at 1/6000, i.e. around 85,000 European citizens, and is served by the European Reference Network for Rare Multisystemic Vascular Diseases (VASCERN). HHT treatments depend on clinical manifestations, and span multiple different medical, surgical and interventional disciplines. Separate to local treatments in the nose, in severe settings, intravenous bevacizumab has been proposed as treatment option, and the purpose of the current article is to assess the use of intravenous bevacizumab in patients with HHT in 2022 according to available data.

Skin cancer is one of the most threatening diseases worldwide. However, diagnosing skin cancer correctly is challenging. Recently, deep learning algorithms have emerged to achieve excellent performance in various tasks. Particularly, they have been applied to the skin disease diagnosis tasks. In this paper, we present a review on deep learning methods and their applications in skin disease diagnosis. We first present a brief introduction to skin diseases and image acquisition methods in dermatology, and list several publicly available skin datasets. Then, we introduce the conception of deep learning, and review popular deep learning architectures and popular frameworks facilitating the implementation of deep learning algorithms. Thereafter, performance evaluation metrics are presented. As an important part of this article, we then review the literature involving deep learning methods for skin disease diagnosis from several aspects according to the specific tasks. Additionally, we discuss the challenges faced in the area and suggest possible future research directions. The major purpose of this article is to provide a conceptual and systematically review of the recent works on skin disease diagnosis with deep learning. Given the popularity of deep learning, there remains great challenges in the area, as well as opportunities that we can explore in the future.

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With Regards
Sofia
Journal Co-ordinator
Journal of Rare Disorders: Diagnosis & Therapy