Early diagnosis is crucial for effectively controlling the progression of Alzheimer's disease. Researchers from institutions such as the University of Cambridge in the UK have developed a new type of artificial intelligence (AI) model, which the team claims can not only avoid invasive or expensive testing, but also predict Alzheimer's disease earlier. It is reported that in order to achieve accurate early diagnosis of Alzheimer's disease, invasive or expensive testing methods such as lumbar puncture or positron emission tomography are generally required. However, not all medical institutions have such testing conditions. Therefore, up to one-third of patients may be misdiagnosed, and some patients may not receive effective treatment due to late diagnosis. The AI prediction model led by the University of Cambridge provides a non-invasive and cost-effective method for effectively predicting whether research subjects will develop Alzheimer's disease in the next three years. The relevant research has been published in the British journal Electronic Clinical Medicine. Based on cognitive testing and MRI scan data collected by a US research team from 400 patients with gray matter atrophy, the research team used machine learning algorithms to establish an AI prediction model and tested the model using real-world data from multiple clinics in the UK, Singapore, and other countries. Due to the use of multimodal data such as text and images, this model can more accurately predict the probability of early disease transitioning to Alzheimer's disease than traditional clinical diagnosis. The test results show that the model has an accuracy rate of 82% in identifying people who will develop Alzheimer's disease within three years, and an accuracy rate of 81% in identifying people who will not develop Alzheimer's disease within three years. More than 55 million people worldwide suffer from dementia, with Alzheimer's disease being the most common type. In the future, the research team hopes to extend the model to predict other types of dementia, such as vascular dementia and frontotemporal dementia, and use different types of data, such as markers in blood tests. (New Society)