The Sjögren–Larsson syndrome (SLS) is a rare autosomal recessive disorder caused by pathogenic variants in the ALDH3A2 gene, which codes for fatty aldehyde dehydrogenase (FALDH). FALDH prevents the accumulation of toxic fatty aldehydes by converting them into fatty acids. Pathogenic ALDH3A2 variants cause symptoms such as ichthyosis, spasticity, intellectual disability, and a wide range of less common clinical features. Interpreting patient‐to‐patient variability is often complicated by inconsistent reporting and negatively impacts on establishing robust criteria to measure the success of SLS treatments. Thus, with this study, patient‐centered literature data was merged into a concise genotype‐based, open‐access database ( www.LOVD.nl/ALDH3A2 ). One hundred and seventy eight individuals with 90 unique SLS‐causing variants were included with phenotypic data being available for more than 90%. While the three lead symptoms did occur in almost all cases, more heterogeneity was observed for other frequent clinical manifestations of SLS. However, a stringent genotype–phenotype correlation analysis was hampered by the considerable variability in reporting phenotypic features. Consequently, we compiled a set of recommendations of how to generate comprehensive SLS patient descriptions in the future. This will be of benefit on multiple levels, for example, in clinical diagnosis, basic research, and the development of novel treatment options for SLS.

Objectives In response to the COVID-19 pandemic, many researchers have developed artificial intelligence (AI) tools to differentiate COVID-19 pneumonia from other conditions in chest CT. However, in many cases, performance has not been clinically validated. The aim of this study was to evaluate the performance of commercial AI solutions in differentiating COVID-19 pneumonia from other lung conditions. Methods Four commercial AI solutions were evaluated on a dual-center clinical dataset consisting of 500 CT studies; COVID-19 pneumonia was microbiologically proven in 50 of these. Sensitivity, specificity, positive and negative predictive values, and AUC were calculated. In a subgroup analysis, the performance of the AI solutions in differentiating COVID-19 pneumonia from other conditions was evaluated in CT studies with ground-glass opacities (GGOs). Results Sensitivity and specificity ranges were 62–96% and 31–80%, respectively. Negative and positive predictive values ranged between 82–99% and 19–25%, respectively. AUC was in the range 0.54–0.79. In CT studies with GGO, sensitivity remained unchanged. However, specificity was lower, and ranged between 15 and 53%. AUC for studies with GGO was in the range 0.54–0.69. Conclusions This study highlights the variable specificity and low positive predictive value of AI solutions in diagnosing COVID-19 pneumonia in chest CT. However, one solution yielded acceptable values for sensitivity. Thus, with further improvement, commercial AI solutions currently under development have the potential to be integrated as alert tools in clinical routine workflow. Randomized trials are needed to assess the true benefits and also potential harms of the use of AI in image analysis. Key Points • Commercial AI solutions achieved a sensitivity and specificity ranging from 62 to 96% and from 31 to 80%, respectively, in identifying patients suspicious for COVID-19 in a clinical dataset. • Sensitivity remained within the same range, while specificity was even lower in subgroup analysis of CT studies with ground-glass opacities, and interrater agreement between the commercial AI solutions was minimal to nonexistent. • Thus, commercial AI solutions have the potential to be integrated as alert tools for the detection of patients with lung changes suspicious for COVID-19 pneumonia in a clinical routine workflow, if further improvement is made.