Artificial intelligence (AI) has undoubtedly been a growing presence in the healthcare industry, shaving years and billions of pounds off drug development programmes, accurately predicting A&E influxes, and even detecting early signs of disease in patients years before it was thought possible. The field of neuroscience has been no exception to this wave of technological innovation, with exciting developments cropping up in recent months and years that could potentially revolutionise diagnoses, treatments, and outcomes for patients on a global scale. The term AI covers a field of computer science that is focused upon the simulation of human intelligence and computational processes. However, there are several subfields of AI technology currently being explored in neuroscience, including machine learning (ML) and deep learning (DL). AI covers all programming systems that can perform tasks which usually require human intelligence.

Dementias are characterised by the build-up of different types of protein in the brain, which damages brain tissue and leads to cognitive decline. In the case of Alzheimer's disease, these proteins include beta-amyloid, which forms'plaques', clumping together between neurons and affecting their function, and tau, which accumulates inside neurons. Molecular and cellular changes to the brain usually begin many years before any symptoms occur. Diagnosing dementia can take many months or even years. It typically requires two or three hospital visits and can involve a range of CT, PET and MRI scans as well as invasive lumber punctures.

Artificial intelligence could spot the early signs of dementia from a simple brain scan long before major symptoms appear – and in some cases before any symptoms appear – say Cambridge researchers. Dementias are characterized by the build-up of different types of protein in the brain, which damages brain tissue and leads to cognitive decline. In the case of Alzheimer's disease, these proteins include beta-amyloid, which forms'plaques', clumping together between neurons and affecting their function, and tau, which accumulates inside neurons. Molecular and cellular changes to the brain usually begin many years before any symptoms occur. Diagnosing dementia can take many months or even years.

Dementias are characterized by the build-up of different types of protein in the brain, which damages brain tissue and leads to cognitive decline. In the case of Alzheimer's disease, these proteins include beta-amyloid, which forms'plaques," clumping together between neurons and affecting their function, and tau, which accumulates inside neurons. Molecular and cellular changes to the brain usually begin many years before any symptoms occur. Diagnosing dementia can take many months or even years. It typically requires two or three hospital visits and can involve a range of CT, PET and MRI scans as well as invasive lumber punctures. A team led by Professor Zoe Kourtzi at the University of Cambridge and The Alan Turing Institute has developed machine learning tools that can detect dementia in patients at a very early stage. Using brain scans from patients who went on to develop Alzheimer's, their machine learning algorithm learnt to spot structural changes in the brain. When combined with the results from standard memory tests, the algorithm was able to provide a prognostic score--that is, the likelihood of the individual having Alzheimer's disease. For those patients presenting with mild cognitive impairment--signs of memory loss or problems with language or visual/spatial perception--the algorithm was higher than 80% accurate in predicting those individuals who went on to develop Alzheimer's disease. It was also able to predict how fast their cognition will decline over time. Professor Kourtzi, from Cambridge's Department of Psychology, said: "We have trained machine learning algorithms to spot very early signs of dementia just by looking for patterns of gray matter loss--essentially, wearing away--in the brain.

Developed by experts from Sweden's Lund University, the approach has the potential to speed up diagnoses while removing the need for costly, specialist equipment. At present, some 20–30 per cent of patients with Alzheimer's disease are misdiagnosed in specialist care alone, let alone primary care, the team noted. A new tool -- using just a blood test (pictured) and a quick set of cognitive tests -- can predict whether someone will develop Alzheimer's in four years with 90 per cent accuracy'Our algorithm is based on a blood analysis of phosphylated rope and a risk gene for Alzheimer's, as well as testing of memory and executive ability,' said neurologist Sebastian Palmqvist of Lund University and the Skåne University Hospital. 'We have developed an online tool to calculate the risk at the individual level that a person with mild memory difficulties will develop Alzheimer's within four years.' In their study, Professor Palmqvist and colleagues examined 340 people with mild memory difficulties who had been recruited into the Swedish BioFINDER Study into neurodegenerative diseases and 543 people from North America.

Dr. Chakravarty: Even before that happens, many people who believe they are at risk will go to their family doctor or a memory specialist to let them know that they've noticed changes in their cognition. For example, they can't remember appointments or how to do simple tasks. Oftentimes, in elderly populations, this is diagnosed first and foremost as geriatric depression because these early signs share a lot of features with this disorder. Certainly what we see in the studies that we've done and when we do patient and subject recruitment is a lot of their general practitioners think they have geriatric depression. Slowly, after years have gone by, they realize that they may have memory impairment.

Diagnosing Alzheimer's disease (AD) is challenging, time consuming, and costly. Currently, there is no single test, or series of tests, that can determine with 100% certainty whether an individual has developed AD. In fact, AD cannot be definitively diagnosed until after death, when the brain can be closely examined for certain microscopic changes caused by the disease. When an individual reports to a doctor that he or she has experienced bouts of memory loss or decreased cognitive function, he or she may be assessed using a variety of cognitive and physical tests, some quite invasive, to determine whether he or she "probably" has AD. However, this diagnosis requires visible symptoms that may only show up when it is too late to start preventative measures.

Alzheimer's disease has no cure, but getting a diagnosis as quickly as possible can allow patients to start symptom-delaying drugs. The problem with getting that diagnosis, though, is that the early stages of Alzheimer's can look a lot like mild cognitive impairment, which may or may not progress into Alzheimer's. A new study from McGill University may make it easier to distinguish what will develop into full-blown Alzheimer's. The researchers used data and artificial intelligence to predict whether someone with mild cognitive impairment would develop Alzheimer's, and were able to do so two years before the onset of dementia symptoms with 84 percent accuracy. The Alzheimer's algorithm takes data like memory test results, glucose metabolism in the brain, PET scans, cerebrospinal fluid and MRIs into account to predict whether patients with mild cognitive impairment will progress to Alzheimer's.

Alzheimer's disease is notoriously difficult to diagnose -- the only way doctors can tell for sure that a patient has the deadly neurodegenerative condition is to examine his or her brain during an autopsy after death. That uncertainty is hard on patients who are starting to experience memory loss, which could be an early sign of Alzheimer's or another, more treatable form of dementia. It also poses a major challenge to the researchers who are working to come up with effective treatments for the disease, which afflicts some 5 million Americans. But now artificial intelligence is learning to do what doctors can't. Separate teams of scientists at the University of Bari in Italy and McGill University in Canada have created artificial intelligence algorithms that can look at brain scans of people who are exhibiting memory loss and tell who will go on to develop full-blown Alzheimer's disease and who won't. "The technology we developed will accelerate the discovery of therapies for [Alzheimer's disease]," lead study author Sulantha Sanjeewa Mathotaarachchi, a software developer at McGill's Translational Neuroimaging Lab, told NBC News MACH in an email.

Researchers from McGill University in Canada reveal how they used machine-learning techniques and beta-amyloid imaging to predict Alzheimer's development in patients with mild cognitive impairment (MCI) up to 2 years before symptoms arose. Co-lead study author Dr. Pedro Rosa-Neto, of the departments of Neurology & Neurosurgery and Psychiatry at McGill University, and colleagues recently reported their findings in the journal Neurobiology of Aging. MCI is a condition characterized by a decline in cognitive functions - such as memory and thinking skills - that is noticeable, but which does not impact a person's ability to carry out everyday tasks. According to the Alzheimer's Association, studies have suggested that around 15 to 20 percent of adults aged 65 and older are likely to have MCI, and these individuals are at greater risk of Alzheimer's than the general population. At present, there is no way to predict which MCI patients will go on to develop Alzheimer's disease, but Dr. Rosa-Neto and colleagues believe that their algorithm has the potential to fulfill this need.