The accurate diagnosis of Alzheimer's disease (AD) and prognosis of mild cognitive impairment (MCI) conversion are crucial for early intervention. However, existing multimodal methods face several challenges, from the heterogeneity of input data, to underexplored modality interactions, missing data due to patient dropouts, and limited data caused by the time-consuming and costly data collection process. In this paper, we propose a novel Prompted Hypergraph Neural Network (PHGNN) framework that addresses these limitations by integrating hypergraph based learning with prompt learning. Hypergraphs capture higher-order relationships between different modalities, while our prompt learning approach for hypergraphs, adapted from NLP, enables efficient training with limited data. Our model is validated through extensive experiments on the ADNI dataset, outperforming SOTA methods in both AD diagnosis and the prediction of MCI conversion.

Alzheimer's patients gradually lose their ability to think, behave, and interact with others. Medical history, laboratory tests, daily activities, and personality changes can all be used to diagnose the disorder. A series of time-consuming and expensive tests are used to diagnose the illness. The most effective way to identify Alzheimer's disease is using a Random-forest classifier in this study, along with various other Machine Learning techniques. The main goal of this study is to fine-tune the classifier to detect illness with fewer tests while maintaining a reasonable disease discovery accuracy. We successfully identified the condition in almost 94% of cases using four of the thirty frequently utilized indicators.

A new AI program can accurately and efficiently detect cognitive impairment from voice recordings. Scientists develop an artificial intelligence program that detects cognitive impairment accurately and efficiently from voice recordings. A lot of time--and money--is required to diagnose Alzheimer's disease. After running lengthy in-person neuropsychological exams, clinicians have to transcribe, review, and analyze every response in detail. However, researchers at Boston University (BU) have developed a new tool that could automate the process and eventually allow it to move online.

It's an increasingly common experience: You wander into the kitchen, quietly muttering under your breath, when you hear a disembodied feminine voice say, "I'm sorry, I didn't quite catch that." We can all agree that Alexa's tendency to eavesdrop is, at times, a little creepy. But is it possible to harness that ability to improve our health? That's the question that researcher David Simon and his coauthors sought to answer in a recent paper published in Cell Press. Simon, a legal ethicist at Harvard University, and his team imagined a hypothetical near-future scenario in which Alexa came equipped with the power to diagnose cognitive conditions like Alzheimer's and dementia simply by analyzing an elder person's speech patterns.

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.

While Alzheimer's disease affects tens of millions of people worldwide, it remains difficult to detect early on. But researchers exploring whether AI can play a role in detecting Alzheimer's in patients are finding that it may be a valuable tool for helping spot the disease. Researchers in California recently published a study in the journal Radiology, and they demonstrated that, once trained, a neural network was able to accurately diagnose Alzheimer's disease in a small number of patients, and it did so based on brain scans taken years before those patients were actually diagnosed by physicians. The team used brain images -- FDG-PET images -- to train and test their neural network. With this type of imaging, FDG, a radioactive type of glucose, is injected into a person's bloodstream, and then that person's bodily tissue, including brain tissue, takes it up as it would regular glucose.

Alzheimers disease is a common form of dementia affecting areas of the brain, which control memory and intelligence. Cognitively normal adults exhibiting atrophy of their temporal lobe or damage to blood vessels in the brain are more likely to develop Alzheimer's disease. Study evaluates if neuroimaging can distinguish persons with depression from those with dementia or both conditions. Dementia is a leading cause of disability in older people. It is a condition where brain cells are permanently damaged or functionally impaired. Till 2015, about 47.5 million people suffer from dementia across the globe.