Analyzing Facial and Eye Movements to Screen for Alzheimer's Disease

Analyzing Facial Asymmetry in Alzheimer's Dementia Using Image-Based Technology

Several studies have demonstrated accelerated brain aging in Alzheimer's dementia (AD). Previous studies have also reported that facial asymmetry increases with age. Because obtaining facial images is much easier than obtaining brain images, the aim of this work was to investigate whether AD exhibits accelerated aging patterns in facial asymmetry. We developed new facial asymmetry measures to compare Alzheimer's patients with healthy controls. A three-dimensional camera was used to capture facial images, and 68 facial landmarks were identified using an open-source machine-learning algorithm called OpenFace. A standard image registration method was used to align the three-dimensional original and mirrored facial images. This study used the registration error, representing landmark superimposition asymmetry distances, to examine 29 pairs of landmarks to characterize facial asymmetry. After comparing the facial images of 150 patients with AD with those of 150 age- and sex-matched non-demented controls, we found that the asymmetry of 20 landmarks was significantly different in AD than in the controls (p < 0.05). The AD-linked asymmetry was concentrated in the face edge, eyebrows, eyes, nostrils, and mouth. Facial asymmetry evaluation may thus serve as a tool for the detection of AD.

Revolutionizing the Early Detection of Alzheimer's Disease through Non-Invasive Biomarkers: The Role of Artificial Intelligence and Deep Learning

Alzheimer's disease (AD) is now classified as a silent pandemic due to concerning current statistics and future predictions. Despite this, no effective treatment or accurate diagnosis currently exists. The negative impacts of invasive techniques and the failure of clinical trials have prompted a shift in research towards non-invasive treatments. In light of this, there is a growing need for early detection of AD through non-invasive approaches. The abundance of data generated by non-invasive techniques such as blood component monitoring, imaging, wearable sensors, and bio-sensors not only offers a platform for more accurate and reliable bio-marker developments but also significantly reduces patient pain, psychological impact, risk of complications, and cost. Nevertheless, there are challenges concerning the computational analysis of the large quantities of data generated, which can provide crucial information for the early diagnosis of AD. Hence, the integration of artificial intelligence and deep learning is critical to addressing these challenges. This work attempts to examine some of the facts and the current situation of these approaches to AD diagnosis by leveraging the potential of these tools and utilizing the vast amount of non-invasive data in order to revolutionize the early detection of AD according to the principles of a new non-invasive medicine era.

A Systematic Review of Barriers and Facilitators of Pain Management in Persons with Dementia

Approximately 50% of persons living with dementia experience pain, yet it is frequently undetected and inadequately managed resulting in adverse consequences. This review aims to synthesize evidence on the barriers and facilitators of pain management in persons living with dementia. PubMed, CINAHL, PsycINFO, and Web of Science datasets were used for article searching. Inclusion criteria were peer-reviewed original articles written in English that examined the barriers and facilitators of pain management for persons living with dementia. The Mixed Methods Appraisal Tool was used to evaluate the quality of the studies. A total of 26 studies were selected, including 18 qualitative and 3 quantitative (all high quality), as well as 5 mixed methods studies (low-to-high quality). Results were categorized into intrapersonal, interpersonal, environmental, and policy categories. Factors that impact pain management in dementia include cognitive and functional impairment, healthcare workers' knowledge, collaboration and communication, healthcare workers' understanding of patients' baseline behaviors, observation of behaviors, pain assessment tool use, pain management consistency, staffing level, pain guideline/policy, and training. Overall, pain management is challenging in persons living with dementia. The results indicate that there is a need for multi-component interventions that involves multidisciplinary teams to improve pain management in persons living with dementia at the intrapersonal, interpersonal, environmental, and policy levels. PERSPECTIVES: This review systematically synthesized barriers and facilitators of providing pain management in persons living with dementia. Results were presented in intrapersonal, interpersonal, environmental, and policy categories and suggests that multicomponent interventions involving multidisciplinary teams are needed to systematically improve pain management in persons living with dementia.

Effects of different virtual reality technology driven dual-tasking paradigms on posture and saccadic eye movements in healthy older adults

Postural sway and eye movements are potential biomarkers for dementia screening. Assessing the two movements comprehensively could improve the understanding of complicated syndrome for more accurate screening. The purpose of this research is to evaluate the effects of comprehensive assessment in healthy older adults (OA), using a novel concurrent comprehensive assessment system consisting of stabilometer and virtual reality headset. 20 healthy OA (70.4 ± 4.9 years) were recruited. Using a cross-sectional study design, this study investigated the effects of various dual-tasking paradigms with integrated tasks of visuospatial memory (VM), spatial orientation (SO), and visual challenge on posture and saccades. Dual-task paradigms with VM and SO affected the saccadic eye movements significantly. Two highly intensive tests of anti-saccade with VM task and pro-saccade with SO task also influenced postural sway significantly. Strong associations were seen between postural sway and eye movements for the conditions where the two movements theoretically shared common neural pathways in the brain, and vice versa. This study suggests that assessing posture and saccades with the integrated tasks comprehensively and simultaneously could be useful to explain different functions of the brain. The results warrant a cross-sectional study in OA with and without dementia to explore differences between these groups.

Biosensors toward behavior detection in diagnosis of alzheimer’s disease

In recent years, a huge number of individuals all over the world, elderly people, in particular, have been suffering from Alzheimer’s disease (AD), which has had a significant negative impact on their quality of life. To intervene early in the progression of the disease, accurate, convenient, and low-cost detection technologies are gaining increased attention. As a result of their multiple merits in the detection and assessment of AD, biosensors are being frequently utilized in this field. Behavioral detection is a prospective way to diagnose AD at an early stage, which is a more objective and quantitative approach than conventional neuropsychological scales. Furthermore, it provides a safer and more comfortable environment than those invasive methods (such as blood and cerebrospinal fluid tests) and is more economical than neuroimaging tests. Behavior detection is gaining increasing attention in AD diagnosis. In this review, cutting-edge biosensor-based devices for AD diagnosis together with their measurement parameters and diagnostic effectiveness have been discussed in four application subtopics: body movement behavior detection, eye movement behavior detection, speech behavior detection, and multi-behavior detection. Finally, the characteristics of behavior detection sensors in various application scenarios are summarized and the prospects of their application in AD diagnostics are presented as well.

The Transcranial Light Therapy Improves Synaptic Plasticity in the Alzheimer’s Disease Mouse Model

Alzheimer’s disease (AD) is the main cause of dementia worldwide. Emerging non-invasive treatments such as photobiomodulation target the mitochondria to minimize brain damage, improving cognitive functions. In this work, an experimental design was carried out to evaluate the effect of transcranial light therapy (TLTC) on synaptic plasticity (SP) and cognitive functions in an AD animal model. Twenty-three mice were separated into two general groups: an APP/PS1 (ALZ) transgenic group and a wild-type (WT) group. Each group was randomly subdivided into two subgroups: mice with and without TLTC, depending on whether they would undergo treatment with TLTC. Cognitive function, measured through an object recognition task, showed non-significant improvement after TLTC. SP, on the other hand, was evaluated using four electrophysiological parameters from the Schaffer-CA1 collateral hippocampal synapses: excitatory field potentials (fEPSP), paired pulse facilitation (PPF), long-term depression (LTD), and long-term potentiation (LTP). An improvement was observed in subjects treated with TLTC, showing higher levels of LTP than those transgenic mice that were not exposed to the treatment. Therefore, the results obtained in this work showed that TLTC could be an efficient non-invasive treatment for AD-associated SP deficits.

A novel deep learning approach for diagnosing Alzheimer's disease based on eye-tracking data

Eye-tracking technology has become a powerful tool for biomedical-related applications due to its simplicity of operation and low requirements on patient language skills. This study aims to use the machine-learning models and deep-learning networks to identify key features of eye movements in Alzheimer's Disease (AD) under specific visual tasks, thereby facilitating computer-aided diagnosis of AD. Firstly, a three-dimensional (3D) visuospatial memory task is designed to provide participants with visual stimuli while their eye-movement data are recorded and used to build an eye-tracking dataset. Then, we propose a novel deep-learning-based model for identifying patients with Alzheimer's Disease (PwAD) and healthy controls (HCs) based on the collected eye-movement data. The proposed model utilizes a nested autoencoder network to extract the eye-movement features from the generated fixation heatmaps and a weight adaptive network layer for the feature fusion, which can preserve as much useful information as possible for the final binary classification. To fully verify the performance of the proposed model, we also design two types of models based on traditional machine-learning and typical deep-learning for comparison. Furthermore, we have also done ablation experiments to verify the effectiveness of each module of the proposed network. Finally, these models are evaluated by four-fold cross-validation on the built eye-tracking dataset. The proposed model shows 85% average accuracy in AD recognition, outperforming machine-learning methods and other typical deep-learning networks.

Review on Facial-Recognition-Based Applications in Disease Diagnosis

Diseases not only manifest as internal structural and functional abnormalities, but also have facial characteristics and appearance deformities. Specific facial phenotypes are potential diagnostic markers, especially for endocrine and metabolic syndromes, genetic disorders, facial neuromuscular diseases, etc. The technology of facial recognition (FR) has been developed for more than a half century, but research in automated identification applied in clinical medicine has exploded only in the last decade. Artificial-intelligence-based FR has been found to have superior performance in diagnosis of diseases. This interdisciplinary field is promising for the optimization of the screening and diagnosis process and assisting in clinical evaluation and decision-making. However, only a few instances have been translated to practical use, and there is need of an overview for integration and future perspectives. This review mainly focuses on the leading edge of technology and applications in varieties of disease, and discusses implications for further exploration.

Artificial Intelligence Meets Neuro-Ophthalmology

ABSTRACT

Recent advances in artificial intelligence have provided ophthalmologists with fast, accurate, and automated means for diagnosing and treating ocular conditions, paving the way to a modern and scalable eye care system. Compared to other ophthalmic disciplines, neuro-ophthalmology has, until recently, not benefitted from significant advances in the area of artificial intelligence. In this narrative review, we summarize and discuss recent advancements utilizing artificial intelligence for the detection of structural and functional optic nerve head abnormalities, and ocular movement disorders in neuro-ophthalmology.

Assessment of the Effect of Cleanliness on the Visual Inspection of Aircraft Engine Blades: An Eye Tracking Study

Background-The visual inspection of aircraft parts such as engine blades is crucial to ensure safe aircraft operation. There is a need to understand the reliability of such inspections and the factors that affect the results. In this study, the factor 'cleanliness' was analysed among other factors. Method-Fifty industry practitioners of three expertise levels inspected 24 images of parts with a variety of defects in clean and dirty conditions, resulting in a total of N = 1200 observations. The data were analysed statistically to evaluate the relationships between cleanliness and inspection performance. Eye tracking was applied to understand the search strategies of different levels of expertise for various part conditions. Results-The results show an inspection accuracy of 86.8% and 66.8% for clean and dirty blades, respectively. The statistical analysis showed that cleanliness and defect type influenced the inspection accuracy, while expertise was surprisingly not a significant factor. In contrast, inspection time was affected by expertise along with other factors, including cleanliness, defect type and visual acuity. Eye tracking revealed that inspectors (experts) apply a more structured and systematic search with less fixations and revisits compared to other groups. Conclusions-Cleaning prior to inspection leads to better results. Eye tracking revealed that inspectors used an underlying search strategy characterised by edge detection and differentiation between surface deposits and other types of damage, which contributed to better performance.

Multimodal Capture of Patient Behaviour for Improved Detection of Early Dementia: Clinical Feasibility and Preliminary Results

Non-invasive automatic screening for Alzheimer’s disease has the potential to improve diagnostic accuracy while lowering healthcare costs. Previous research has shown that patterns in speech, language, gaze, and drawing can help detect early signs of cognitive decline. In this paper, we describe a highly multimodal system for unobtrusively capturing data during real clinical interviews conducted as part of cognitive assessments for Alzheimer’s disease. The system uses nine different sensor devices (smartphones, a tablet, an eye tracker, a microphone array, and a wristband) to record interaction data during a specialist’s first clinical interview with a patient, and is currently in use at Karolinska University Hospital in Stockholm, Sweden. Furthermore, complementary information in the form of brain imaging, psychological tests, speech therapist assessment, and clinical meta-data is also available for each patient. We detail our data-collection and analysis procedure and present preliminary findings that relate measures extracted from the multimodal recordings to clinical assessments and established biomarkers, based on data from 25 patients gathered thus far. Our findings demonstrate feasibility for our proposed methodology and indicate that the collected data can be used to improve clinical assessments of early dementia.