Epidemiology of inherited epidermolysis bullosa in Germany

Background Epidermolysis bullosa (EB) is a rare genetic disorder manifesting with skin and mucosal membrane blistering in different degrees of severity. Objective Epidemiological data from different countries have been published, but none are available from Germany. Methods In this population-based cross-sectional study, people living with EB in Germany were identified using the following sources: academic hospitals, diagnostic laboratories and patient organization. Results Our study indicates an overall EB incidence of 45 per million live births in Germany. With 14.23 per million live births for junctional EB, the incidence is higher than in other countries, possibly reflecting the availability of early molecular genetic diagnostics in severely affected neonates. Dystrophic EB was assessed at 15.58 cases per million live births. The relatively low incidence found for EB simplex, 14.93 per million live births, could be explained by late or missed diagnosis, but also by 33% of cases remaining not otherwise specified. Using log-linear models, we estimated a prevalence of 54 per million for all EB types, 2.44 for junctional EB, 12.16 for dystrophic EB and 28.44 per million for EB simplex. These figures are comparable to previously reported data from other countries. Conclusions Altogether, there are at least 2000 patients with EB in the German population. These results should support national policies and pharmaceutical companies in decision-making, allow more precise planning of drug development and clinical trials, and aid patient advocacy groups in their effort to improve quality of life of people with this orphan disease

Dermatologist-like explainable AI enhances trust and confidence in diagnosing melanoma

Abstract Artificial intelligence (AI) systems have been shown to help dermatologists diagnose melanoma more accurately, however they lack transparency, hindering user acceptance. Explainable AI (XAI) methods can help to increase transparency, yet often lack precise, domain-specific explanations. Moreover, the impact of XAI methods on dermatologists’ decisions has not yet been evaluated. Building upon previous research, we introduce an XAI system that provides precise and domain-specific explanations alongside its differential diagnoses of melanomas and nevi. Through a three-phase study, we assess its impact on dermatologists’ diagnostic accuracy, diagnostic confidence, and trust in the XAI-support. Our results show strong alignment between XAI and dermatologist explanations. We also show that dermatologists’ confidence in their diagnoses, and their trust in the support system significantly increase with XAI compared to conventional AI. This study highlights dermatologists’ willingness to adopt such XAI systems, promoting future use in the clinic