Oita University

A research team at the Korean government-backed Electronics and Telecommunications Research Institute in Daejeon, a city south of Seoul, has developed a predictive AI technology for screening mild cognitive impairment. HOW IT WORKS Developed by combining existing voice and text analysis models with a large language model, the integrated AI analyses recorded conversations of senior people to evaluate their cognitive function and predict their risk of developing dementia. It improves on similar existing AI models, which have been inaccurate in assessing an elder's thinking capabilities due to imprecise pronunciation and dialects, ETRI claimed. A person's speech provides insights that can be used to detect and predict declines in a person's cognitive, language, and speaking functions. The AI reportedly achieved 87.3% accuracy in recognising Alzheimer's disease, a common form of dementia, based on a test using an open-access dataset of spontaneous speech from DementiaBank. Following this test, the predictive AI was incorporated into a mobile application, which is now being prepared for a trial at senior welfare centres. The research team is also working to expand the LLM used in the AI to include images and videos, besides text and speech inputs. WHY IT MATTERS By 2025, South Korea is projected to become a super-aged society where people over the age of 65 account for a fifth of the entire population. That figure can also rise to about 40% by 2050. About 10% of the country's senior population or around 840,000 people are living with dementia, based on the latest South Korea Dementia Status report in 2021. Their number is expected to swell to over 3 million by 2050. ETRI's development of a dementia screening tool contributes to various efforts to prepare for such a future. The mobile application enables monitoring at the community level and thus helps in the early detection of mild cognitive impairment among the elderly. It also supports health management to halt the progression of dementia. THE LARGER TREND A growing body of research highlights the use of AI in predicting people's risk of developing dementia, particularly Alzheimer's disease. Fujifilm announced the development of AI Technology for AD Progression Prediction in 2022. It was reported that the technology can predict the likelihood of a person's mild cognitive impairment progressing to Alzheimer's in two years. Eisai and Oita University in Japan also built what could be the world's first AI model that analyses wearables data to predict Alzheimer's. Meanwhile, another Korean organisation, the Korea Institute of Machinery and Materials, created an intraocular eye lens that reacts to an Alzheimer's biomarker, potentially enabling early detection.

NeuroX, a neurology-focused online consultation service, and its physician network ATP Partners announced it will participate in the Centers for Medicare and Medicaid Services (CMS) GUIDE Program to enhance care for dementia patients. NeuroX, American TelePhysician's brain health division, offers a virtual-care platform that connects individuals with neurologists, psychiatrists and sleep specialists to provide what the company touts as a "complete brain health ecosystem." The company works with employers, individuals and facilities, offering online screening and consultations, neurodiagnostics, AI-enabled remote patient monitoring and a speech-based cognitive-assessment test dubbed LANGaware. CMS' GUIDE Program, which will officially launch July 1, aims to support individuals living with dementia and their caregivers by providing care coordination and care-management services, respite services, and caregiver education and support. Individuals will also have 24/7 access to a support line. Additionally, the program will provide guidelines for a standard approach to care with the aim of keeping dementia patients living in their homes. Through the GUIDE Program, individuals suffering from dementia will have access to NeuroX and ATP's technology platform, including its diagnostics, treatment and home care services. "GUIDE is the latest innovative CMS value-based program focused on improving clinical quality while lowering health care expenditures for Medicare," ATP CFO David Spalding said in a statement. "As a GUIDE participant, ATP will deploy our scalable, award-winning brain health care management platform to support our proprietary specialist telemedicine network and in conjunction with community-based physician groups focused on senior populations with chronic conditions." THE LARGER TREND CMS announced the GUIDE model in July of last year in response to President Biden's executive order issued three months earlier to increase access to high-quality care and support caregivers. According to the CDC, approximately 5.8 million people in the U.S. have Alzheimer's disease or another form of dementia, and by 2060 that number is expected to jump to 14 million people. Other organizations focused on providing dementia care are Japanese pharmaceutical company Eisai and Oita University, also in Japan, which built an AI model that uses data from wearable devices to predict Alzheimer's disease, the most common form of dementia. St. Louis-based Harmonic Health is another tech-enabled dementia-care platform, as is Fujifilm Corporation, which developed AI technology to predict whether patients with mild cognitive impairment will progress to Alzheimer's disease within two years.

Japanese pharmaceutical company Eisai, together with Oita University in Japan, has built what could be the world's first AI model that uses data from wearable devices to predict Alzheimer's disease, the most common form of dementia. Their research team embarked on a study to create a cost-effective, practical tool to pre-screen people suspected of developing the disease. Their findings were published in the journal Alzheimer's Research & Therapy. FINDINGS The study collected biological and lifestyle data from 122 individuals aged 65 and older with mild cognitive impairment or subjective memory impairment. Biological data, including physical activity, sleep, and heart rate, were collected from wristband sensors they were made to wear for seven days every three months from 2015 to 2019. The following lifestyle data were also collected from their medical consultations: employment status, frequency of going outdoors, means of transportation, number of days participating in community activity, and backgrounds (age, education history, history of alcohol use, and medical history). Moreover, participants underwent annual amyloid PET (positron emission tomography) examinations, which detect the accumulation of amyloid-beta protein in the brain – a crucial biomarker of Alzheimer's. Researchers developed a predictive model that combined three machine learning technologies: support vector machine, Elastic Net, and logistic regression, to integrate all the collected data. Its purpose is to determine how likely each participant will test positive by amyloid PET screening. An evaluation of the AI model showed that it has a "sufficient" capability to predict amyloid-beta protein accumulation. Through this model, the study was able to identify 22 factors that contribute to amyloid-beta protein accumulation: physical activity, sleep, heart rate, amount of conversation, age, length of education, living with or without children, means of transportation, presence of accompanying person for hospital visits, communication frequencies, and number of outings. WHY IT MATTERS Progress in Alzheimer's research led to the discovery of amyloid-beta protein accumulation in the brain as a significant biomarker of Alzheimer's. This further resulted in the development of therapeutic drugs targeting it. Still, for incurable diseases such as Alzheimer's, screening is imperative. Currently, testing is possible through amyloid PET and cerebrospinal fluid (CSF) testing, which are costly, limited, and invasive, according to Eisai and Oita University researchers. AI has been applied to explore ways to make Alzheimer's testing accessible for many, especially now that countries like Japan are ageing at a faster pace. While there are already studies that have applied AI to predict amyloid-beta protein accumulation, these have so far only used cognitive function tests, blood tests, and brain imaging tests. The latest research by the Eisai and Oita University team provides value to this growing area of study as it focuses on lifestyle and biological data. Following their pioneering work, the team sees a potential for wider application of their AI model to pre-screen people for Alzheimer's, particularly those with little access to amyloid PET and CSF testing. THE LARGER TREND The Japanese research team was not the first to contemplate using wearable devices to track or predict biomarkers of Alzheimer's. In 2020, Alzheimer’s Research UK announced a project to develop a wearable diagnostic device for the early detection of neurodegenerative diseases with funding from Microsoft founder Bill Gates. In recent years, more projects have been carried out to improve Alzheimer's detection by leveraging AI technology. Researchers from Australia's CSIRO and Queensland University of Technology recently came out with an AI-based benchmark for measuring brain atrophy, touted to be a global first. Fujifilm also developed an AI tool that was found to accurately predict patients who would progress to Alzheimer's within two years. Lotte Healthcare from South Korea recently announced a partnership with iMediSync to explore developing new AI-driven healthcare services, including screening for neuropsychiatric disorders. Meanwhile, Eisai continues its involvement in Alzheimer's research. Recently, it launched a new digital health business called Theoria, which has a specific focus on dementia. Launching in April, Theoria is coming out with its flagship solution, a risk prediction algorithm for the early detection of mild cognitive impairment.