Artificial Intelligence Models in the Diagnosis of Adult-Onset Dementia Disorders: A Review.

Background: The progressive aging of populations, primarily in the industrialized western world, is accompanied by the increased incidence of several non-transmittable diseases, including neurodegenerative diseases and adult-onset dementia disorders. To stimulate adequate interventions, including treatment and preventive measures, an early, accurate diagnosis is necessary. Conventional magnetic resonance imaging (MRI) represents a technique quite common for the diagnosis of neurological disorders. Increasing evidence indicates that the association of artificial intelligence (AI) approaches with MRI is particularly useful for improving the diagnostic accuracy of different dementia types. Objectives: In this work, we have systematically reviewed the characteristics of AI algorithms in the early detection of adult-onset dementia disorders, and also discussed its performance metrics. Methods: A document search was conducted with three databases, namely PubMed (Medline), Web of Science, and Scopus. The search was limited to the articles published after 2006 and in English only. The screening of the articles was performed using quality criteria based on the Newcastle-Ottawa Scale (NOS) rating. Only papers with an NOS score ≥ 7 were considered for further review. Results: The document search produced a count of 1876 articles and, because of duplication, 1195 papers were not considered. Multiple screenings were performed to assess quality criteria, which yielded 29 studies. All the selected articles were further grouped based on different attributes, including study type, type of AI model used in the identification of dementia, performance metrics, and data type. Conclusions: The most common adult-onset dementia disorders occurring were Alzheimer's disease and vascular dementia. AI techniques associated with MRI resulted in increased diagnostic accuracy ranging from 73.3% to 99%. These findings suggest that AI should be associated with conventional MRI techniques to obtain a precise and early diagnosis of dementia disorders occurring in old age.

Migrants seeking help for cognitive disturbances: exploratory data from an Italian memory clinic.

INTRODUCTION: The phenomenon of dementia occurring in migrants and minority groups constitutes an emerging issue for Western countries. Nevertheless, it has been poorly explored from the perspective of "real-world" clinical services. We aimed to quantify the number of migrants from LMIC attending an Italian university memory clinic and to document its modifications over time. METHODS: All the subjects undergoing a first neurological and cognitive assessment between 2001 and 2017 were considered for the present analyses. RESULTS: The proportion of subjects from LMIC performing a first cognitive evaluation was found to remain substantially stable between 2001 and 2017. No statistically significant difference was found between "HIC" and "LMIC" individuals with regard to sociodemographic and clinical characteristics. CONCLUSION: These findings seem to indicate that cognitive disorders in LMIC migrants still constitute a marginal public health issues for Italian dementia services. Nevertheless, the identification of eventual sociocultural and healthcare barriers may help to understand the real magnitude and relevance of this phenomenon.