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Alzheimer's: Daily power walks could help stave off the onset of disease, study claims

Daily Mail - Science & tech

A daily power walk or bike ride in old age may cut the risk of developing Alzheimer's disease, a study has claimed. Research has long shown exercise in middle age and beyond can cut the chance of dementia -- which is most commonly caused by Alzheimer's -- by up to 40 per cent. Now researchers from the University of California say the disease can be prevented if people exercise in later life as well. Exercise is thought to help stave off the disease because it improves cognitive function, keeps bodyweight low and prevents plaque forming in the arteries -- a key cause of vascular dementia. But the latest study also suggests exercise in later life can reduce inflammation in the brain, which can cause Alzheimer's to develop.

USC Viterbi Students Develop AI-based Alzheimer's Diagnosis Tool - USC Viterbi


About 6 million people in the US are currently living with Alzheimer's disease, the most common form of dementia, according to the Alzheimer's Association. Despite being the sixth-leading cause of death in the country, there is currently no known cure for the memory-robbing condition. But diagnosing the disease early can help people seek preventative care and slow its progress. That's why a team of students at USC is developing machine learning tools to detect early-onset Alzheimer's disease using speech patterns, and democratize the diagnosis process. The team started working on the system in spring 2021 as a project for CAIS, the student branch of the Center for Artificial Intelligence in Society, in collaboration with students from MEDesign, the biomedical engineering design group.

An app that measures pain could help people with dementia


London (CNN Business)When you're in pain, you can usually tell someone about it. But for people with communication difficulties, that isn't always an option, meaning pain often goes undetected, misinterpreted or wrongly treated. To give a voice to those who can't report their suffering, such as people with dementia, PainChek, an Australian startup, has developed an app that uses facial analysis and artificial intelligence (AI) to assess and score pain levels. A carer records a short video of the subject's face using a smartphone and answers questions about their behavior, movements and speech. The app's AI recognizes facial muscle movements that are associated with pain and combines this with the carer's observations to calculate an overall pain score.

FDA clears AI-powered digital test for early dementia


The FDA has approved an artificial intelligence-based test for early detection of dementia that can be carried out on an iPad in five minutes. The CognICA Integrated Cognitive Assessment (ICA) test developed by London, UK-based company Cognetivity Neurosciences has been approved by the FDA as an alternative to traditional pen-and-paper tests with some key advantages, according to its developer. Those include high sensitivity to detect early-stage cognitive impairment, which could allow early intervention with treatment or lifestyle changes that might help to slow down the progression of dementia. The digital format also helps to avoid cultural or educational bias in testing, and helps to avoid scenarios where people tested on multiple occasions learn how to score better, masking increases in impairment, said Cognetivity. It can also be carried out unsupervised, saving time and money for health systems and making it particularly suitable for assessments when access to care may be restricted, or to allow ongoing monitoring of patients without clinic visits.

Hand gesture detection in the hand movement test for the early diagnosis of dementia Artificial Intelligence

Collecting hands data is important for many cognitive studies, especially for senior participants who has no IT background. For example, alternating hand movements and imitation of gestures are formal cognitive assessment in the early detection of dementia. During data collection process, one of the key steps is to detect whether the participants is following the instruction correctly to do the correct gestures. Meanwhile, re-searchers found a lot of problems in TAS Test hand movement data collection process, where is challenging to detect similar gestures and guarantee the quality of the collect-ed images. We have implemented a hand gesture detector to detect the gestures per-formed in the hand movement tests, which enables us to monitor if the participants are following the instructions correctly. In this research, we have processed 20,000 images collected from TAS Test and labelled 6,450 images to detect different hand poses in the hand movement tests. This paper has the following three contributions. Firstly, we compared the performance of different network structures for hand poses detection. Secondly, we introduced a transformer block in the state of art network and increased the classification performance of the similar gestures. Thirdly, we have created two datasets and included 20 percent of blurred images in the dataset to investigate how different network structures were impacted by noisy data, then we proposed a novel net-work to increase the detection accuracy to mediate the influence of the noisy data.

Artificial Intelligence in Drug Discovery: An overview


According to Markets and Markets, the global AI drug discovery market is projected to reach $ 1,434 million by 2024 from $ 259 million in 2019, at a CAGR of 40.8% during the forecast period 2019–2024. For example, BioXcel Therapeutics, Inc. (Nasdaq: BTAI) is a clinical-stage company utilising AI drug discovery approaches in neuroscience and immuno-oncology and has an emerging drug named BXCL501, that is an orally dissolving thin film formulation of dexmedetomidine, a selective alpha-2a receptor agonist for the treatment of agitation associated with neuropsychiatric disorders (Via Finance Yahoo). BioXcel Therapeutics has observed anti-agitation results in multiple clinical studies with BXCL501 including: SERENITY I for schizophrenia related agitation, SERENITY II for bipolar disorder related agitation and TRANQUILITY for dementia related agitation. Accordingly, BXCL501 has been granted Breakthrough Therapy designation for the acute treatment of agitation associated with dementia and Fast Track designation for the acute treatment of agitation associated with schizophrenia, bipolar disorders and dementia. Moreover, BioXcel recently received acceptance of its New Drug Application for BXCL501 for the acute treatment of agitation associated with schizophrenia and bipolar disorders.

Editorial: Alzheimer's Dementia Recognition through Spontaneous Speech


While a number of studies have investigated speech and language features for the detection of AD and mild cognitive impairment (Fraser et al., 2016), and proposed various signal processing and machine learning methods for this task (Petti et al., 2020), the field still lacks balanced benchmark data against which different approaches can be systematically compared. This Research Topic addresses this issue by exploring the use of speech characteristics for AD recognition using balanced data and shared tasks, such as those provided by the ADReSS Challenges (Luz et al., 2020(Luz et al., , 2021. These tasks have brought together groups working on this active area of research, providing the community with benchmarks for comparison of speech and language approaches to cognitive assessment. Reflecting the multidisciplinary character of the topic, the articles in this collection span three journals: Frontiers of Aging Neuroscience, Frontiers of Computer Science and Frontiers in Psychology.Most papers in this Reseach Topic target two main tasks: AD classification, for distinguishing individuals with AD from healthy controls, and cognitive test score regression, to infer the patient's Mini Mental Status Examination (MMSE) score (Folstein et al., 1975). Of the twenty papers published in this collection, 14 used the ADReSS dataset (Luz et al., 2020), by itself or in combination with other data. The ADReSS dataset is a curated subset of DementiaBank's Pitt Corpus, matched for age and ge...

Designing A Clinically Applicable Deep Recurrent Model to Identify Neuropsychiatric Symptoms in People Living with Dementia Using In-Home Monitoring Data Artificial Intelligence

Agitation is one of the neuropsychiatric symptoms with high prevalence in dementia which can negatively impact the Activities of Daily Living (ADL) and the independence of individuals. Detecting agitation episodes can assist in providing People Living with Dementia (PLWD) with early and timely interventions. Analysing agitation episodes will also help identify modifiable factors such as ambient temperature and sleep as possible components causing agitation in an individual. This preliminary study presents a supervised learning model to analyse the risk of agitation in PLWD using in-home monitoring data. The in-home monitoring data includes motion sensors, physiological measurements, and the use of kitchen appliances from 46 homes of PLWD between April 2019-June 2021. We apply a recurrent deep learning model to identify agitation episodes validated and recorded by a clinical monitoring team. We present the experiments to assess the efficacy of the proposed model. The proposed model achieves an average of 79.78% recall, 27.66% precision and 37.64% F1 scores when employing the optimal parameters, suggesting a good ability to recognise agitation events. We also discuss using machine learning models for analysing the behavioural patterns using continuous monitoring data and explore clinical applicability and the choices between sensitivity and specificity in-home monitoring applications.

Measuring Cognitive Status from Speech in a Smart Home Environment Artificial Intelligence

The population is aging, and becoming more tech-savvy. The United Nations predicts that by 2050, one in six people in the world will be over age 65 (up from one in 11 in 2019), and this increases to one in four in Europe and Northern America. Meanwhile, the proportion of American adults over 65 who own a smartphone has risen 24 percentage points from 2013-2017, and the majority have Internet in their homes. Smart devices and smart home technology have profound potential to transform how people age, their ability to live independently in later years, and their interactions with their circle of care. Cognitive health is a key component to independence and well-being in old age, and smart homes present many opportunities to measure cognitive status in a continuous, unobtrusive manner. In this article, we focus on speech as a measurement instrument for cognitive health. Existing methods of cognitive assessment suffer from a number of limitations that could be addressed through smart home speech sensing technologies. We begin with a brief tutorial on measuring cognitive status from speech, including some pointers to useful open-source software toolboxes for the interested reader. We then present an overview of the preliminary results from pilot studies on active and passive smart home speech sensing for the measurement of cognitive health, and conclude with some recommendations and challenge statements for the next wave of work in this area, to help overcome both technical and ethical barriers to success.

How IBM Is Employing AI To Predict Alzheimer's Disease


IBM researchers then used NLP to analyse the participants' language sample transcripts. The model picked up tiny subtleties and changes in discourses that are generally missed if done manually. Based on this, IBM researchers trained the ML model to account for multiple variables affecting the results. Lastly, they drew on data from the subjects at the Framingham Heart Study, where the participants are assessed through two-minute Mini-Mental State Examination speech tests every four years and neuropsychological exams every year. CTT examples from FHS, including an unimpaired sample (a), an impaired sample showing telegraphic speech and lack of punctuation (b), and an even more impaired sample showing in addition significant misspellings and minimal grammatical complexity, e.g.