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.

The medical assistance to seafarers was not always improved in parallel with advances in medicine and telecommunications. Today technology offers systems including digital devices helping in collecting symptoms to be referred correctly to remote physicians. CIRM SERVIZI, the spin-off of Centro Internazionale Radio Medico (C.I.R.M.), and the Italian TMAS center, have developed an advanced system called CIRM PREMIUM. This aimed at offering extensive telemedicine solutions for the treatment of illnesses or injuries on-board ships. The objective of this paper is to present the results of offered service with CIRM PREMIUM and the standard basic TMAS service given by C.I.R.M. to ships not equipped with advanced technologies. We have considered 400 seafarers assisted on-board ships from 1 January 2020 to 31 December 2021 including 200 on-board ships equipped with CIRM PREMIUM (Test Group, TG) and 200 were on-board ships requiring the C.I.R.M. standard free telemedical assistance (Control Group, CG). The five most frequent different disorders assisted by C.I.R.M in the given period were considered. Diseases of the circulatory and respiratory systems were also considered to be the most frequent cause of medical emergencies on board. Data were analyzed comparatively by analysis of variance (ANOVA) and by the Chi-squared test. The average time for a patient’s complete recovery was 115.1 ± 12.8 h (4 to 5 days) in the TG group and 132.8 ± 14.4 h (5 to 6 days) in the CG group. From a temporal point of view, PREMIUM patients showed an improvement in 12–24 h from the first request for medical advice, versus 36–48 h for those using the standard medical service. The patients who fully recovered on board were 48.8% for the TG group and 27.46% for the CG. Telemedical assistance resulted in avoiding diversions in 73.7% of cases in the PREMIUM service and in 43.7% in the standard service. The results of our analysis revealed that the CIRM PREMIUM services provide a better and quicker outcome for patient conditions and cause significantly less ship diversions for medical reasons. This indicates that technological progress can offer relevant advantages for treating diseases or accidents on-board ships. The presence of telemedical devices on board, their larger and constant use and a closer collaboration from the ship will offer seafarers adequate health protection and will reduce the present disadvantage of being ill while at sea.

The increasing incidence of adult-onset dementia disorders and primarily Alzheimer’s disease (AD) among the aging population around the world is increasing the social and economic burden on society and healthcare systems. This paper presents three neural networking algorithms: MobileNet, Artificial Neural Networks (ANN), and DenseNet for AD classification based on MRI imaging data. The results of each model were compared in terms of performance metrics such as accuracy, true positive rate, and receiver operating curve values. Results mentioned that MNet classified AD progression with 95.41% of accuracy. Early detection and appropriate interventions, primarily on modifiable risk factors of AD, can delay the progression of cognitive impairment and other symptoms that represent a main trait of the disease.