In the context of advancing healthcare, the diagnosis and treatment of cognitive disorders, particularly Mild Cognitive Impairment (MCI) and Alzheimer’s Disease (AD), pose significant challenges. This review explores Artificial Intelligence (AI) and Machine Learning (ML) in neuropsychological assessment for the early detection and personalized treatment of MCI and AD. The review includes 37 articles that demonstrate that AI could be an useful instrument for optimizing diagnostic procedures, predicting cognitive decline, and outperforming traditional tests. Three main categories of applications are identified: (1) combining neuropsychological assessment with clinical data, (2) optimizing existing test batteries using ML techniques, and (3) employing virtual reality and games to overcome the limitations of traditional tests. Despite advancements, the review highlights a gap in developing tools that simplify the clinician’s workflow and underscores the need for explainable AI in healthcare decision making. Future studies should bridge the gap between technical performance measures and practical clinical utility to yield accurate results and facilitate clinicians’ roles. The successful integration of AI/ML in predicting dementia onset could reduce global healthcare costs and benefit aging societies.

The impact of stroke on the sexual functioning of female survivors can arise from direct neurological effects or be triggered indirectly through various psychophysiological processes. Although an increasing number of publications exist, the topic of sexuality remains seldom discussed in the stroke literature, even though patients have expressed a need for sexual rehabilitation services. A literature review on sexual functioning in post-stroke females examining existing rehabilitation programs addressing patients’ sexuality and exploring the perspectives of nurses and caregivers on sexual issues constitutes a novel approach. Therefore, we conducted a scoping review to better investigate this hot topic. Studies were identified by searching Scopus, PubMed, Web of Science, Cochrane Library, PsychINFO, and Embase databases. Current data indicates substantial connections between stroke and female sexual dysfunction (SD), including factors like desire/libido, sexual satisfaction, and sexual intercourse. Some intervention programs have been created to provide specific guidance to healthcare professionals in addressing patients’ requirements for sexual recovery, although their adequacy remains uncertain. However, to date, there are neither specific rehabilitation programs for post-stroke female SD nor healthcare personnel trained to deal with post-stroke sexual issues adequately and efficiently. The incorporation of sexual rehabilitation into the overall rehabilitation process for stroke patients is crucial, ideally within an interdisciplinary framework. Despite being a fundamental aspect of post-stroke women’s lives, sexuality remains taboo.

Background/Objectives: Huntington’s disease (HD) is an autosomal dominant genetic disorder causing progressive neurodegeneration which, aside from symptomatic therapies for controlling psychological and motor problems, currently has no effective treatment. People who receive this diagnosis often feel disoriented and lost without guidance. Furthermore, HD patients are estimated to have a two to seven times greater risk of suicide death compared to the general population. The current review investigates the complex relationship between HD and suicide, seeking to identify key risk factors influencing suicidal ideation and behaviour in affected individuals. Methods: We conducted a systematic review following the PRISMA guidelines. Studies were searched for on the PubMed, Cochrane, and Web of Science databases, and 17 articles met the inclusion criteria. Results: The findings reveal that emotional strain, neuropsychiatric symptoms, and the absence of a cure contribute to heightened suicidal tendencies in HD patients. Critical periods for suicide risk coincide with early symptomatic stages of disease or the successive phase, with the loss of independence impacting on daily functioning. Risk factors associated with HD include a depressive mood, cognitive impairments, and a history of suicide attempts. Conclusions: From a prevention perspective, a comprehensive multidisciplinary and multidimensional approach could enhance the overall well-being of people with HD. In particular, screening for suicidal thoughts in people with HD could mitigate suicide risk.

Background/Objectives: Several studies have shown a relation between obesity and cognitive decline, highlighting a significant global health challenge. In recent years, artificial intelligence (AI) and machine learning (ML) have been integrated into clinical practice for analyzing datasets to identify new risk factors, build predictive models, and develop personalized interventions, thereby providing useful information to healthcare professionals. This systematic review aims to evaluate the potential of AI and ML techniques in addressing the relationship between obesity, its associated health consequences, and cognitive decline. Methods: Systematic searches were performed in PubMed, Cochrane, Web of Science, Scopus, Embase, and PsycInfo databases, which yielded eight studies. After reading the full text of the selected studies and applying predefined inclusion criteria, eight studies were included based on pertinence and relevance to the topic. Results: The findings underscore the utility of AI and ML in assessing risk and predicting cognitive decline in obese patients. Furthermore, these new technology models identified key risk factors and predictive biomarkers, paving the way for tailored prevention strategies and treatment plans. Conclusions: The early detection, prevention, and personalized interventions facilitated by these technologies can significantly reduce costs and time. Future research should assess ethical considerations, data privacy, and equitable access for all.

Background/Objectives: Cardiovascular diseases (CVDs) encompass a wide range of heart and vascular conditions and remain the leading cause of death worldwide. Acceptance and Commitment Therapy (ACT) is a psychotherapeutic approach that integrates acceptance, mindfulness, and commitment to value-based actions. This systematic review aims to explore the current evidence on the potential role of ACT interventions in supporting psychological well-being among individuals with CVDs. Methods: A systematic review was conducted in accordance with PRISMA guidelines. A search of the literature was conducted through Scopus, PubMed, Web of Science, Cochrane, and PsycINFO databases. Six studies met the inclusion criteria. Results: The reviewed studies suggest that ACT may promote psychological flexibility, emotion regulation, and self-care behaviors in patients with CVDs. Reported outcomes include improved mindfulness, reduced distress, and enhanced quality of life. However, the evidence base is limited in both size and methodological rigor, with included studies varying in design and population. Conclusions: While preliminary findings indicate that ACT shows promise in addressing psychological aspects of CVDs, the current evidence remains insufficient to draw definitive conclusions. Further high-quality, large-scale studies are needed to evaluate the effectiveness and clinical applicability of ACT in cardiovascular populations.

IntroductionAlzheimer’s disease (AD) is the leading cause of dementia worldwide and is characterized by progressive decline in cognitive functions. Mild Cognitive Impairment (MCI) represents an intermediate stage between normal aging and dementia, with measurable cognitive decline but largely preserved functional independence. However, overlapping clinical features and heterogeneous disease patterns may limit the accuracy of conventional classification approaches. Machine learning (ML) methods can support clinical decision-making by identifying complex neuroanatomical patterns in neuroimaging data and improving the characterization of different stages of cognitive impairment. ObjectivesThis study aimed to compare different supervised ML algorithms for binary and multiclass classification of cognitive impairment using structural MRI-derived volumetric biomarkers. Specifically, we evaluated the performance of models trained using neuroanatomical features alone and models integrating MRI biomarkers with demographic variables. In addition, SHAP-based explainability analysis was applied to identify the features contributing most to model predictions and to improve the interpretability of the classification framework. MethodsThe study included 204 participants, comprising 54 patients with AD, 79 individuals with MCI, and 71 HC. T1-weighted structural MRI scans were processed using automated pipelines to extract cortical and subcortical volumetric measures. Five supervised ML algorithms were evaluated: Logistic Regression, Random Forest, XGBoost, LightGBM, and Support Vector Machine (SVM). All models were implemented within a standardized pipeline including feature scaling, feature selection using SelectKBest with ANOVA F-test, hyperparameter tuning with GridSearchCV, and stratified 5-fold cross-validation. Two experimental settings were compared: models trained only on MRI-derived volumetric features and models trained on a combination of MRI and demographic features. Performance was evaluated using accuracy, F1-score, ROC-AUC, and confusion matrices. Finally, SHAP analysis was applied to the best-performing models to assess the contribution of individual features to classification decisions. ResultsIn the binary classification task distinguishing MCI/AD from HC, all models achieved higher performance than in the multiclass setting. Using only MRI-derived volumetric features, Logistic Regression showed the best performance, reaching an accuracy of 0.70 and a ROC-AUC of 0.76. The inclusion of demographic variables further improved classification performance, with SVM achieving the highest results, corresponding to an accuracy of 0.75 and a ROC-AUC of 0.80. In the multiclass classification task involving AD, MCI, and HC, Random Forest achieved the best performance when using only volumetric biomarkers, with an accuracy of 0.53 and a ROC-AUC of 0.69. When MRI and demographic features were combined, SVM achieved the best performance, reaching an accuracy of 0.56 and a ROC-AUC of 0.72. SHAP analysis identified the amygdala, hippocampus, corpus callosum subdivisions, and age as the most influential features driving model predictions. ConclusionsThe findings support the potential role of structural MRI biomarkers combined with interpretable ML approaches for the classification of cognitive impairment. The integration of demographic variables improved model performance, particularly in binary discrimination tasks, whereas multiclass classification remained more challenging, likely due to the clinical and neuroanatomical overlap between MCI and AD patterns. This framework may represent a promising approach for future clinical decision-support tools aimed at improving diagnostic stratification, although further validation in larger independent cohorts is required.