Objective: Although Coronavirus disease 2019 (COVID-19) is primarily a respiratory infectious disease, it has also been associated with a wide range of other clinical manifestations. It is widely accepted in the scientific community that many patients after recovery continue to experience COVID-19-related symptoms, including cognitive impairment. The aim of this systematic review was to investigate the cognitive profile of patients with long-COVID syndrome. Methods: A systematic search of empirical studies was conducted through the PubMed/Medline and Scopus electronic databases. Cross-sectional and longitudinal studies published between 2020 and 2023 were included. Results: Of the 516 studies assessed for eligibility, 36 studies met the inclusion criteria. All included studies support the presence of persistent cognitive changes after COVID-19 disease. Executive function, memory, attention, and processing speed appear to be the cognitive domains that are predominantly associated with long-COVID syndrome, whereas language is an area that has not been sufficiently investigated. Conclusions: In this review, the high frequency of cognitive impairment after COVID-19 is evident. If we consider that cognitive functioning affects our ability to live independently and is a key determinant of our quality of life, it is imperative to precisely define those factors that may induce cognitive impairment in COVID-19 survivors, with the ultimate goal of early diagnosis of cognitive changes and, consequently, the development of targeted rehabilitation interventions to address them.

The improved treatment of knee injuries critically relies on having an accurate and cost-effective detection. In recent years, deep-learning-based approaches have monopolized knee injury detection in MRI studies. The aim of this paper is to present the findings of a systematic literature review of knee (anterior cruciate ligament, meniscus, and cartilage) injury detection papers using deep learning. The systematic review was carried out following the PRISMA guidelines on several databases, including PubMed, Cochrane Library, EMBASE, and Google Scholar. Appropriate metrics were chosen to interpret the results. The prediction accuracy of the deep-learning models for the identification of knee injuries ranged from 72.5–100%. Deep learning has the potential to act at par with human-level performance in decision-making tasks related to the MRI-based diagnosis of knee injuries. The limitations of the present deep-learning approaches include data imbalance, model generalizability across different centers, verification bias, lack of related classification studies with more than two classes, and ground-truth subjectivity. There are several possible avenues of further exploration of deep learning for improving MRI-based knee injury diagnosis. Explainability and lightweightness of the deployed deep-learning systems are expected to become crucial enablers for their widespread use in clinical practice.