The use of radiomics and artificial intelligence applied for the diagnosis and monitoring of Alzheimer’s disease has developed in recent years. However, this approach is not yet completely applicable in clinical practice. The aim of this paper is to provide a systematic analysis of the studies that have included the use of radiomics from different imaging techniques and artificial intelligence for the diagnosis and monitoring of Alzheimer’s disease in order to improve the clinical outcomes and quality of life of older patients. A systematic review of the literature was conducted in February 2023, analyzing manuscripts and articles of the last 5 years from the PubMed, Scopus and Embase databases. All studies concerning discrimination among Alzheimer’s disease, Mild Cognitive Impairment and healthy older people performing radiomics analysis through machine and deep learning were included. A total of 15 papers were included. The results showed a very good performance of this approach in the differentiating Alzheimer’s disease patients—both at the dementia and pre-dementia phases of the disease—from healthy older people. In summary, radiomics and AI can be valuable tools for diagnosing and monitoring the progression of Alzheimer’s disease, potentially leading to earlier and more accurate diagnosis and treatment. However, the results reported by this review should be read with great caution, keeping in mind that imaging alone is not enough to identify dementia due to Alzheimer’s.

Informal/family caregivers (ICs) of older people with dementia (PwD) can suffer from depression and burnout. However, digital solutions can potentially provide innovative ways to facilitate care provision. The aim of this study was to analyze the opinions of end users (EUs), i.e., PwD aged 65 years and over and their ICs and healthcare professionals (HPs), on the use of digital technology to support care activities. Qualitative data were collected during the co-design phase of the European project “DemiCare—Personalized support for informal caregivers of people with dementia”. This study focused on the Italian context and included two PwD, three ICs, and seven HPs. Three focus groups were held in April–June 2022. Qualitative data were analyzed using MaxQDA 2020 software. Seven thematic areas were identified: daily activities, care tasks, information needs, support received, relationship with and expectations from technology, functionality of the DemiCare integrated system, and ethical issues. Smart devices seemed to be received positively by ICs and HPs, although older PwD seemed to have difficulty accepting the technology. Overall, despite the low number of participants, it seems important and potentially effective to consider the needs and preferences of PwD during exploratory co-design to allow social interactions with them. This study was not registered.

Background: Type 2 diabetes (T2D) is correlated to amnestic mild cognitive impairment (aMCI) and to non-amnestic mild cognitive impairment (naMCI). This study evaluated whether the T2D variable characterizes a peculiar cognitive profile in elderly patients. Moreover, it explores the association between glycated hemoglobin levels (HbA1c), T2D duration, insulin and oral hypoglycemic agent treatment, and cognition in elderly diabetic patients. Methods: Detailed neuropsychological battery was used to diagnose MCI subtypes. A total of 39 MCI subjects with T2D (T2D-MCI) and 37 MCI subjects without T2D (ND-MCI), matched for age, educational level, and Mini-Mental State Examination score, were included. Results: ND-MCI performed worse in memory and language domains than T2D-MCI. The amnestic subtype is more frequent among ND-MCI and non-amnestic subtype in T2D-MCI. In T2D-MCI, high HbA1c levels correlate with episodic memory (immediate recall) and T2D duration. Some indexes of episodic memory (immediate recall), attention, and visual-spatial ability correlate with insulin treatment. Conclusions: An association between T2D and non-amnestic MCI is suggested. In the T2D-MCI group, significant associations between insulin treatment and memory (immediate recall), complex figure copy, and attention were found.

Innovative technologies can support older adults with or without disabilities, allowing them to live independently in their environment whilst monitoring their health and safety conditions and thereby reducing the significant burden on caregivers, whether family or professional. This paper discusses the design of a study protocol to evaluate the acceptance, usability, and efficiency of the SAVE system, a custom-developed information technology-based elderly care system. The study will involve older adults (aged 65 or older), professional and lay caregivers, and care service decision-makers representing all types of users in a care service scenario. The SAVE environmental sensors, smartwatches, smartphones, and Web service application will be evaluated in people’s homes situated in Romania, Italy, and Hungary with a total of 165 users of the three types (cares, elderly, and admin). The study design follows the mixed method approach, using standardized tests and questionnaires with open-ended questions and logging all the data for evaluation. The trial is registered to the platform ClinicalTrials.gov with the registration number NCT05626556. This protocol not only guides the participating countries but can be a feasibility protocol suitable for evaluating the usability and quality of similar systems.

(1) Background: The RESILIEN-T system addresses the need for innovative solutions to support self-management in older people with Mild Cognitive Impairment (MCI). Despite the increasing prevalence of dementia and MCI, there is a lack of tailored solutions for these individuals. The RESILIEN-T system aims to empower and engage people with cognitive decline by providing a modular platform for self-management and coaching services. (2) Methods: Italian data collected for the RESILIEN-T project involved 62 older participants randomly assigned to the intervention or control group. Data were collected through questionnaires and user interactions with the system over a three-month period. (3) Results: Quantitative outcomes showed no significant differences between the intervention and control groups, except for an improvement in perceived memory capability in the intervention group. The usability assessment indicated a high level of acceptance of the RESILIEN-T system. (4) Discussions: Although no significant improvements were observed in most quantitative measures, the high user engagement and acceptance suggest the potential effectiveness of the RESILIEN-T system. Future improvements could involve integrating smart objects and interactive virtual agents. Overall, RESILIEN-T represents a promising step toward empowering individuals with cognitive impairment in their self-management and decision-making processes.

Mild cognitive impairment is frequent among people with Parkinson’s disease. Cognitive training seems effective for cognitive status and for mitigating anxiety and depression. With the COVID-19 outbreak, such therapeutic interventions were delivered online. This longitudinal mixed-method study was aimed at evaluating the effectiveness of an online cognitive treatment, carried out during COVID times and based on Parkinson’s-Adapted Cognitive Stimulation Therapy, on cognitive domains and mood of 18 older people with Parkinson’s disease. After screening, the cognitive status and mood were assessed three times by Addenbrooke’s Cognitive Examination-Revised scale and the Geriatric Depression Scale-Short Form. At the follow-up, patients were also interviewed for understanding their experience with the technology. Such treatment was effective on the participants’ cognitive functions, but not on their mood. Despite some initial problems with the technology, the online intervention was experienced as a way of not being ‘left behind’, staying in contact with others, and being safe during the lockdown. This suggests that online cognitive treatment can be adopted to integrate face-to-face interventions by increasing their efficacy, accessibility, and long-term outcomes. Suggestions for future research are given.