Role of Artificial Intelligence (AI) art in care of ageing society: focus on dementia

open access articleBackground: Art enhances both physical and mental health wellbeing. The health benefits include reduction in blood pressure, heart rate, pain perception and briefer inpatient stays, as well as improvement of communication skills and self-esteem. In addition to these, people living with dementia benefit from reduction of their noncognitive, behavioural changes, enhancement of their cognitive capacities and being socially active. Methods: The current study represents a narrative general literature review on available studies and knowledge about contribution of Artificial Intelligence (AI) in creative arts. Results: We review AI visual arts technologies, and their potential for use among people with dementia and care, drawing on similar experiences to date from traditional art in dementia care. Conclusion: The virtual reality, installations and the psychedelic properties of the AI created art provide a new venue for more detailed research about its therapeutic use in dementia

Artificial intelligence approaches to predicting and detecting cognitive decline in older adults: A conceptual review.

Preserving cognition and mental capacity is critical to aging with autonomy. Early detection of pathological cognitive decline facilitates the greatest impact of restorative or preventative treatments. Artificial Intelligence (AI) in healthcare is the use of computational algorithms that mimic human cognitive functions to analyze complex medical data. AI technologies like machine learning (ML) support the integration of biological, psychological, and social factors when approaching diagnosis, prognosis, and treatment of disease. This paper serves to acquaint clinicians and other stakeholders with the use, benefits, and limitations of AI for predicting, diagnosing, and classifying mild and major neurocognitive impairments, by providing a conceptual overview of this topic with emphasis on the features explored and AI techniques employed. We present studies that fell into six categories of features used for these purposes: (1) sociodemographics; (2) clinical and psychometric assessments; (3) neuroimaging and neurophysiology; (4) electronic health records and claims; (5) novel assessments (e.g., sensors for digital data); and (6) genomics/other omics. For each category we provide examples of AI approaches, including supervised and unsupervised ML, deep learning, and natural language processing. AI technology, still nascent in healthcare, has great potential to transform the way we diagnose and treat patients with neurocognitive disorders

Macro-Level Cognitive and Linguistic Function in Early Stage Alzheimer’s Disease and Mild Cognitive Impairment

Alzheimer’s disease (AD) is a global health concern, particularly as there is currently no cure for the disease. Interventions to slow progression of disease, pharmacological or non-pharmacological, need to be targeted early on before any significant neurodegeneration has occurred, as these changes are irreversible, and lost cognitive function cannot be recovered. This makes it imperative to detect pathological cognitive decline as early as possible. Although biomarkers have received a lot of attention in this regard, they have several limitations, particularly outside of research settings, such as cost and availability. Cognitive markers, other than traditional neuropsychological test measures, on the other hand, have received comparatively less attention with regards to early detection; and, particularly cognitive markers that are rooted in real-world, everyday cognition, have been lacking. Due to the disease being incurable, interventions are aimed at maintaining independent living and good quality of life for as long as possible. This necessitates outcomes that can measure meaningful change in cognition and everyday functioning. The goal of the present dissertation was to identify gaps in the current literature on cognitive and linguistic assessments that are embedded in aspects of everyday cognition in AD, and work towards developing paradigms to address the gaps. Due to the emphasis on early detection, the work focused on patients in the very early stage of AD and on its preceding stage of Mild Cognitive Impairment (MCI). In light of evidence reporting the inability of AD patients to follow narratives, be it verbal or non-verbal, a systematic review of text comprehension studies was conducted to characterize and evaluate macro-level measures of discourse comprehension in their sensitivity to early stage AD, and their ability to distinguish pathological ageing due to AD or MCI from cognitive ageing. Results showed that, not only AD patients, but also MCI patients were significantly more impaired on macro-level measures of comprehension compared to cognitively healthy older adults. These findings were consistent across all eight studies included in the review, indicating a robust effect, though there were minor differences in the sensitivity of different measures. Next, moving towards non-verbal narratives, a novel picture-based paradigm assessing event cognition, with a focus on event integration and macro-event recognition, was introduced. This study aimed to examine the macro-level processing of events by using a format requiring integration of micro-events, depicted in pictures, into a larger macro-event. AD and MCI patients’ ability to connect the micro-events temporally and causally to identify the depicted macro-event was assessed. As hypothesized, the findings showed that patient groups had significant difficulties in determining temporal order of micro-events, even when provided with a verbal cue, as well as in conceptualizing the macro-event from the presented micro-events, when compared to healthy older adults. Finally, using traditional neuropsychological tests, the cognitive processes involved in performing the macro-event recognition task were determined by examining correlations. Primarily, semantic memory and executive functioning appear to play a role. However, the strength of correlations was fairly moderate, indicating added value of event recognition task in cognitive assessment. Taken together, these findings show the sensitivity of macro-level cognitive and linguistic markers based in everyday cognition in the early stages of AD, and highlight the positive role of such cognitive assessment methods in bringing together objective assessment methods and everyday cognition

Alzheimer Detection System Using Hybrid Deep Convolutional Neural Network

Alzheimer’s disease of the sixth leading causes of death in the United States of America is projected to grow to the third place of all causes of death for the elderly soon to cancer and heart decease. Timely detection and prevention are crucial to it. AD detection is based on multiple medical examinations which all lead to extensive multivariate heterogeneous data. This factor makes manual comparison, evaluation, and analysis hardly possible. The hereby study proposes a new approach to the detection of AD at the earliest stage hybrid deep learning algorithms. Several feature extraction and selection draw possible features. The method involves InceptionV3 and DenseNet for both pre-processing and classification tasks, while MobileNet enables data pre-processing and object detection. Experimental results with 100 epochs and 15 hidden layers show InceptionV3 has an accuracy of 98%, which outperforms other models available. The comparative analysis with other CNN models endorses the proposed method, achieving the highest performance across the board from our system

Executive functions in the elderly with Mild Cognitive Impairment: a systematic review on motor and cognitive inhibition, conflict control and cognitive flexibility

Background: Mild Cognitive Impairment (MCI) is a syndrome characterised by mild cognitive decline, on one or more domains, but which does not compromise daily functions. Several studies have investigated the relationship between MCI and deficit in executive functions (EFs) but, unlike robust evidence in the mnestic domain, the nature of executive deficits in the MCI population remains uncertain. Objectives: This systematic review aims to evaluate EFs in patients with MCI, considering inhibition (motor and cognitive), conflict control and cognitive flexibility. Method: The databases used for the search were PUBMED, PsycINFO, PsycARTICLES and MEDLINE. Eligibility criteria: use of specific paradigms for EFs assessment ("Wisconsin Card Sorting Test", "Stroop Task", "Go/No-Go Task", "Flanker Task"); age over 65, studies published in English. Exclusion criteria: presence of dementia; psychiatric disorders; stroke; cranial trauma; inclusion of participants with MCI in groups with healthy elderly or those with dementia. Results: Fifty-five studies were selected, namely: Stroop Task (N=30), WCST (N=14), Go/No-Go (N=9), Flanker Task (N=2). Results have shown in people with MCI deficits in all the EFs considered. Conclusions: The results of this review support the applicability of the four experimental tasks examined for the study of EFs in people with MCI. These paradigms are useful in research, diagnosis and therapeutic purposes, allowing obtaining an articulated EFs profile that can compromise the daily life in elderly. These EFs are not generally evaluated by standard assessment of MCI, but their evaluation can lead to a better knowledge of MCI and help in the diagnosis and treatment

Oral Inflammation, Tooth Loss, Risk Factors, and Association with Progression of Alzheimer’s Disease

Periodontitis is a polymicrobial chronic inflammatory disease of tooth-supporting tissues with bacterial etiology affecting all age groups, becoming chronic in a subgroup of older individuals. Periodontal pathogens Porphyromonas gingivalis, Tannerella forsythia, and Treponema denticola are implicated in the development of a number of inflammatory pathologies at remote organ sites, including Alzheimer’s disease (AD). The initial inflammatory hypothesis proposed that AD hallmark proteins were the main contributors of central nervous system (CNS) inflammation. This hypothesis is expanding to include the role of infections, lifestyle, and genetic and environmental factors in the pathogenesis of AD. Periodontal disease (PD) typifies a condition that encompasses all of the above factors including pathogenic bacteria. These bacteria not only are the source of low-grade, chronic infection and inflammation that follow daily episodes of bacteremia arising from everyday tasks such as brushing, flossing teeth, chewing food, and during dental procedures, but they also disseminate into the brain from closely related anatomical pathways. The long-term effect of inflammatory mediators, pathogens, and/or their virulence factors, reaching the brain systemically or otherwise would, over time, prime the brain’s own microglia in individuals who have inherent susceptibility traits. Such susceptibilities contribute to inadequate neutralization of invading agents, upon reaching the brain. This has the capacity to create a vicious cycle of sustained local inflammatory milieu resulting in the loss of cytoarchitectural integrity and vital neurons with subsequent loss of function (deterioration in memory). The possible pathways between PD and AD development are considered here, as well as environmental factors that may modulate/exacerbate AD symptoms