The AI was trained to correctly spot the difference between diseased and healthy brains, before being tested on its accuracy abilities on a second set of 148 scans – 52 of which were healthy, 48 had Alzheimer's and the other 48 had a mild cognitive impairment that was known to develop into Alzheimer's within 10 years. The algorithm correctly distinguished between healthy and diseased brains 86% of the time, according to the researchers, who added that it was also able to spot the difference between a healthy brain and a mild impairment with an 84% accuracy rating. Last month mobile game Sea Hero Quest – which uses navigation challenges to gather data about spatial movement as part of research into the disease – was expanded to virtual reality for the first time. The game sets users navigation challenges, and they can opt-in to share their data with the researchers behind the game, who can use player performance data to plot spatial navigation skills of different ages groups and genders.
Now, researchers are using AI scans to detect Alzheimer's almost a decade earlier than doctors making a diagnosis based on symptoms alone. In a study, published earlier this month, researchers developed a machine-learning algorithm to detect Alzheimer's in brain scans 86 percent of the time. Nicola Amoroso, Marianna La Rocco, and colleagues from the University of Bari, Italy, taught AI software to tell the difference between healthy and unhealthy brains using MRI scans from the Alzheimer's Disease Neuroimaging Initiative. The researchers discovered that the algorithm was most effective at analyzing brain regions of 2,250 to 3,200 cubic millimeters – which just so happens to be the same size as anatomical structures associated with the disease (e.g.
The team from the University of Bari trained the AI by feeding in 67 MRI scans - 38 from Alzheimer's patients and 29 healthy patients - then asked it to analyse the neuronal connectivity to form an algorithm. Following the training, the AI was then asked to process brains from 148 subjects - 52 were healthy, 48 had Alzheimer's disease and 48 had mild cognitive impairment (MCI) but were known to have developed Alzheimer's disease two and a half to nine years later. Although there is no cure for Alzheimer's disease, early diagnosis can allow people to start making lifestyle choices to slow the progression of the disease. The Bari University research team now intends to extend the technique to help with the early diagnosis of neurodegenerative conditions such as Parkinson's disease.
Various researchers around the globe are developing ways to detect Alzheimer's as early as possible. After all, early detection gives people the power seek treatment that can slow down the condition's effects, as well as enough time to get their legal and financial affairs in order. Out of the total number, 48 were scans of people with the disease, while 48 were scans of people who suffered from mild cognitive impairment and eventually developed full-blown Alzheimer's. More importantly, it was able to detect mild cognitive impairment 84 percent of the time, making it a potentially effective tool for early diagnosis.
A devastating chronic neurodegenerative disease, Alzheimer's disease (AD) currently affects around 5.5 million people in the United States alone. "We used publicly available data, consisting of 67 brain MRI scans from the Alzheimer's Disease Neuroimaging Initiative, including healthy controls and AD patients," Nicola Amoroso, one of the lead researchers on the project, told Digital Trends. "We used this cohort to feed [our] artificial intelligence, then an independent test of about 148 subjects -- including controls, Alzheimer's disease and mild cognitive impairment (MCI) subjects -- was performed. You can read a research paper on the University of Bari's machine learning project here.
Researchers have developed an AI that is able to detect the neurodegenerative disease - which leads to loss of memory and cognitive functions - almost 10 years earlier than doctors can by looking at symptoms alone. They tested the AI on a set of scans from 148 subjects, of which 52 were healthy, 48 had Alzheimer's disease and 48 had mild cognitive impairment (MCI) but were known to have developed Alzheimer's disease 2.5 to nine years later. These sans show reduced grey matter density in patients with Alzheimer's disease, a neurodegenerative disease - which leads to loss of memory and cognitive function. They tested the AI on a set of scans from 148 subjects, of which 52 were healthy, 48 had Alzheimer's disease and 48 had mild cognitive impairment (MCI) but were known to have developed Alzheimer's disease 2.5 to nine years later.
Artificial intelligence can identify changes in the brains of people likely to get Alzheimer's disease almost a decade before doctors can diagnose the disease from symptoms alone. This was intriguing, says La Rocca, since this is similar to the size of the anatomical structures connected with the disease, such as the amygdala and hippocampus. "Nowadays, cerebrospinal fluid analyses and brain imaging using radioactive tracers can tell us to what extent the brain is covered with plaques and tangles, and are able to predict relatively accurately who is at high risk of developing Alzheimer's 10 years later," says La Rocca. In contrast, the new technique can distinguish with similar accuracy between brains that are normal and the brains of people with MCI who will go on to develop Alzheimer's disease in about a decade – but using a simpler, cheaper and non-invasive technique.
NHS England plans to invest more of its £120bn budget in AI to speed up its application to medicine and the health service, especially the task of analysing "huge swaths" of the information collected from patients about their symptoms. "All of this takes us into very new territory and it's not a long way over there, it's actually here now," he told delegates at NHS England's Health and Care Innovation Expo in Manchester. Jeremy Hunt, the health secretary, said computers could be routinely diagnosing health conditions – even before they display symptoms – by the time the NHS turns 80 in 2028. The revolution in genomics is central to easier, potentially more accurate diagnosis, Hunt added.
The team published its study ("Cascaded Multi-view Canonical Correlation (CaMCCo) for Early Diagnosis of Alzheimer's Disease via Fusion of Clinical, Imaging and Omic Features") in Scientific Reports. "In this work, we present a combined framework, cascaded multiview canonical correlation (CaMCCo), for fusion and cascaded classification that incorporates all diagnostic categories and optimizes classification by selectively combining a subset of modalities at each level of the cascade." In predicting which patients in the study had Alzheimer's disease, CaMCCo reportedly outperformed individual indicators as well as methods that combine them all without selective assessment. It also was better at predicting who had mild cognitive impairment than other methods that combine multiple indicators.
This week: An AI baby helps us explore what it means to be human, a new metamaterial from MagicLeap and Berkeley, rejuvenating middle aged muscle tissue, bionic lenses that give better than 20/20 vision, and healing broken bones with gene therapy and microbubbles. The Bionic Lens replaces the natural lens found within the human eye, and brings with it a number of improvements, chief among them being an immediate improvement to eyesight, and clear vision regardless of distance. Too much bone loss makes regrowth impossible, and even smaller fractures make bone growth problematic if the patient is in poor health or at an advanced age. When physicians encounter these kinds of nonhealing fractures, autologous bone grafts are the gold standard for treatment.