The risk of infections for multiple sclerosis and neuromyelitis optica spectrum disorder disease-modifying treatments: Eighth European Committee for Treatment and Research in Multiple Sclerosis Focused Workshop Review. April 2021.

Over the recent years, the treatment of multiple sclerosis (MS) and neuromyelitis optica spectrum disorder (NMOSD) has evolved very rapidly and a large number of disease-modifying treatments (DMTs) are now available. However, most DMTs are associated with adverse events, the most frequent of which being infections. Consideration of all DMT-associated risks facilitates development of risk mitigation strategies. An international focused workshop with expert-led discussions was sponsored by the European Committee for Treatment and Research in Multiple Sclerosis (ECTRIMS) and was held in April 2021 to review our current knowledge about the risk of infections associated with the use of DMTs for people with MS and NMOSD and corresponding risk mitigation strategies. The workshop addressed DMT-associated infections in specific populations, such as children and pregnant women with MS, or people with MS who have other comorbidities or live in regions with an exceptionally high infection burden. Finally, we reviewed the topic of DMT-associated infectious risks in the context of the current SARS-CoV-2 pandemic. Herein, we summarize available evidence and identify gaps in knowledge which justify further research.

COVID-19 pneumonia accurately detected on chest radiographs with artificial intelligence.

PURPOSE: To investigate the diagnostic performance of an Artificial Intelligence (AI) system for detection of COVID-19 in chest radiographs (CXR), and compare results to those of physicians working alone, or with AI support. MATERIALS AND METHODS: An AI system was fine-tuned to discriminate confirmed COVID-19 pneumonia, from other viral and bacterial pneumonia and non-pneumonia patients and used to review 302 CXR images from adult patients retrospectively sourced from nine different databases. Fifty-four physicians blind to diagnosis, were invited to interpret images under identical conditions in a test set, and randomly assigned either to receive or not receive support from the AI system. Comparisons were then made between diagnostic performance of physicians working with and without AI support. AI system performance was evaluated using the area under the receiver operating characteristic (AUROC), and sensitivity and specificity of physician performance compared to that of the AI system. RESULTS: Discrimination by the AI system of COVID-19 pneumonia showed an AUROC curve of 0.96 in the validation and 0.83 in the external test set, respectively. The AI system outperformed physicians in the AUROC overall (70% increase in sensitivity and 1% increase in specificity, p < 0.0001). When working with AI support, physicians increased their diagnostic sensitivity from 47% to 61% (p < 0.001), although specificity decreased from 79% to 75% (p = 0.007). CONCLUSIONS: Our results suggest interpreting chest radiographs (CXR) supported by AI, increases physician diagnostic sensitivity for COVID-19 detection. This approach involving a human-machine partnership may help expedite triaging efforts and improve resource allocation in the current crisis.