J. Dement. Alzheimer's Dis., Volume 2, Issue 4 (December 2025) – 6 articles

Dementia, the most common subtype of which is Alzheimer’s disease, represents a significant global and social health challenge. Its effective management is currently hindered by poor access to diagnostic services, a lack of effective treatments and limited post-diagnostic monitoring. This review will explore recent advances in our understanding of key biomarkers underlying the development and progression of Alzheimer’s disease and its associated comorbidities. It will also highlight major data collection efforts in the area and emerging artificial intelligence-based approaches, including imaging, speech, movement, and cognitive data that are being used to improve the risk assessment, diagnosis, and monitoring of Alzheimer’s disease. The development of simple, scalable, and cost-effective artificial intelligence-based tools offers the potential to transform Alzheimer’s disease care through early intervention, more personalised treatment, and improved access to care, offering hope to current and future Alzheimer’s disease sufferers. Full article

Background: Early diagnosis of Alzheimer’s disease (AD) is constrained by invasive and costly tests. Aggregation of $\beta$-amyloid and the A$\beta$42/A$\beta$40 ratio in cerebrospinal fluid (CSF) and blood are key biomarkers. Fluorescent probes can report aggregate states, and artificial intelligence (AI) can extract subtle patterns from spectral and blood data. This review synthesizes how probes and AI can identify aggregates and assess the A$\beta$42/A$\beta$40 ratio in body fluids to facilitate earlier AD diagnosis. Methods: PRISMA-compliant searches were conducted in Scopus, PubMed, Web of Science, and IEEE Xplore. Results: Twenty-eight studies met inclusion criteria. Plasma A$\beta$42/A$\beta$40 was lower in PET-positive individuals by ∼7–18%, with higher performance for mass spectrometry (mean AUC ≈ 0.80) than immunoassays (AUC ≈ 0.71). CSF A$\beta$42/A$\beta$40 showed larger group differences (∼50% reductions in PET+) and stronger PET concordance, outperforming plasma. Fluorescent probes—including AN-SP and CRANAD-28—were sensitive to early aggregates and showed in vivo imaging potential, but evidence is largely preclinical or from small cohorts. AI/ML approaches frequently achieved within-study accuracies >90% (e.g., 94–100% in spectral tasks), yet external validation and head-to-head tests of ratio alone versus ratio + AI remain scarce. Conclusions: Plasma A$\beta$42/40 —particularly by mass spectrometry—currently provides the most reproducible fluid approximation to amyloid PET (mean AUC ≈ 0.80). Fluorescent probes sensitively detect oligomeric A$\beta$ species and show in vivo potential, but evidence remains largely preclinical or from small cohorts. AI/ML methods can extract additional signal from spectral and multivariate blood data, yet consistent incremental gains over optimized Aβ42/40 assays have not been demonstrated due to limited external validation and head-to-head comparisons. Full article

Alzheimer’s disease accounts for approximately 50% to 80% of all causes of dementia. Co-existence of AD with other diseases causing dementia poses a diagnostic challenge, as we are still far from diagnosing AD accurately in order to manage it appropriately. Neuroimaging techniques, not only help diagnose AD but also consistently feature in diagnostic and research criteria for AD as biomarkers. Molecular biomarkers including positron emission tomography (PET) and single-photon emission computed tomography (SPECT), and structural biomarkers including magnetic resonance imaging (MRI), have been used in various therapeutic and prognostic studies in AD. This review highlights the recent advances in neuroimaging biomarkers, including molecular biomarkers (PET and SPECT tracers) and structural biomarkers (MRI), for AD. For the purpose of this review, molecular biomarkers have been further subcategorized into non-specific radiotracers (FDG-PET and blood flow SPECT) and specific amyloid- and tau-related radiotracers. The aim of this review is to discuss the recent advances and evidence of molecular and structural biomarkers of AD. Full article

Aim: As the global population ages, the number of bilingual individuals living with dementia is increasing, yet their communication needs remain underrepresented in both clinical practice and research. This evidence review examines the intersection of language regression, communication challenges, and cultural–linguistic identity in bilingual dementia, with a particular focus on the role of speech–language pathologists (SLPs). Methods: Twelve peer-reviewed studies were critically reviewed and thematically analysed across four domains: (1) language regression and retention in bilingual dementia, (2) communication challenges in bilingual dementia care, (3) the marginal role of speech–language pathology, and (4) cultural–linguistic identity and health equity. The included studies span clinical case reports, experimental research, qualitative caregiver studies, and systematic reviews, with bilingual populations across Asia, Europe, North America, and the Middle East. Results: Findings reveal that language deterioration in bilingual dementia is dynamic and highly individualised, often influenced by language history, emotional context, and usage patterns. Caregivers and clinicians face persistent communication breakdowns, particularly in linguistically mismatched settings. Despite their specialised expertise in communication, SLPs remain largely peripheral in dementia care, constrained by systemic, educational, and methodological barriers. Moreover, linguistic and cultural identity play a critical role in how dementia is experienced and managed, yet are rarely integrated into care frameworks. Conclusions: This review highlights a significant knowledge–practice gap in bilingual dementia care and underscores the need to embed culturally and linguistically responsive communication practices, especially through speech–language therapy, at the centre of bilingual dementia care and support. It outlines key research and practice directions to advance equity, accuracy, and relational care in this growing population. Full article

Background: Timely intervention and effective control of Alzheimer’s disease (AD) have been shown to limit memory loss and preserve cognitive function and the ability to perform simple activities in older adults. In addition, magnetic resonance imaging (MRI) scans are one of the most common and effective methods for early detection of AD. With the rapid development of deep learning (DL) algorithms, AD detection based on deep learning has wide applications. Methods: In this research, we have developed an AD detection method based on three-dimensional (3D) convolutional neural networks (CNNs) for 3D MRI images, which can achieve strong accuracy when compared with traditional 3D CNN models. The proposed model has four main blocks, and the multi-layer fusion functionality of each block was used to improve the efficiency of the proposed model. The performance of the proposed model was compared with three different pre-trained 3D CNN architectures (i.e., 3D ResNet-18, 3D InceptionResNet-v2, and 3D Efficientnet-b2) in both tasks of multi-/binary-class classification of AD. Results: Our model achieved impressive classification results of 91.4% for binary-class as well as 80.6% for multi-class classification on the Open Access Series of Imaging Studies (OASIS) database. Conclusions: Such results serve to demonstrate that multi-stage feature fusion of 3D CNN is an effective solution to improve the accuracy of diagnosis of AD with 3D MRI, thus enabling earlier and more accurate diagnosis. Full article

Background: Transitions from hospital to long-term care (LTC) facilities are critical periods for older adults living with dementia, often involving complex medical, cognitive, and psychosocial needs. Virtual team-based care has emerged as a promising strategy to improve communication, coordination, and continuity of care during these transitions. However, there is limited evidence on how such approaches are implemented in practice, particularly with respect to inclusion, equity, and engagement of older adults and families. Objective: This study aimed to identify the enablers and barriers to delivering virtual team-based care to support older adults with dementia in transitioning from hospital to LTC. Methods: We conducted a qualitative study using semi-structured interviews, focus groups, and a policy review. Data were collected from 60 participants, including healthcare providers, older adults, and family care partners across hospital and LTC settings in British Columbia, Canada. Thematic analysis was conducted using a hybrid inductive and deductive approach. Eighteen institutional policies and guidelines on virtual care and dementia transitions were reviewed to contextualize findings. Results: Four themes were identified: (1) enhancing communication and collaboration, (2) engaging families in care planning, (3) digital access and literacy, and (4) organizational readiness and infrastructure. While virtual huddles and secure messaging platforms supported timely coordination, implementation was inconsistent due to infrastructure limitations, unclear protocols, and staffing pressures. Institutional policies emphasized privacy and security but lacked guidance for inclusive engagement of older adults and families. Many participants described limited access to reliable technology, a lack of training, and the absence of tools tailored for individuals with cognitive impairment. Conclusions: Virtual care has the potential to support more coordinated and inclusive transitions for people with dementia, but its success depends on more than technology. Structured protocols, inclusive policies, and leadership commitment are essential to ensure equitable access and meaningful engagement. The proposed VIRTUAL framework offers practical tips for strengthening virtual team-based care by embedding ethical, relational, and infrastructural readiness across settings. Full article