The AI is designed to be non-invasive and uses a short language sample from a verbal cognitive test given to a patient. Using this sample, the AI model is able to predict the onset of Alzheimer’s with around 71 percent accuracy.

For comparison, standard clinical tests are correct approximately 59 percent of the time and take much longer to diagnose. Current tests analyse the descriptive abilities of people as they age for potential warning signs.

In a paper detailing IBM’s model, the company says it used data from the Framingham Heart Study.

The study first began in 1948 and spans the multiple generations required for building an AI to predict Alzheimer’s in healthy individuals with no other risk factors. 5,000 participants from Massachusetts and their families have been studied.

703 samples from 270 of the study’s participants were collected and analysed to create a dataset consisting of a single sample from 80 participants—half of whom developed Alzheimer’s symptoms before they reached 85.

The AI was trained on this dataset to spot Alzheimer’s signals such as the repetition of words and using short sentences with poor grammatical structures. IBM’s AI was able to correctly predict the onset of Alzheimer’s in every seven of ten cases.

IBM intends to expand the training of their model using more data to better reflect society including socioeconomic, racial, and geographic factors. The Alzheimer’s research is part of a broader IBM effort to better understand neurological health and chronic illnesses through biomarkers and signals in speech and language.

Around 5.5 million people in America alone are estimated to have Alzheimer’s, and some studies suggest it’s the third leading cause of death behind heart disease and cancer.

While there is no cure or prevention for Alzheimer’s yet, earlier diagnosis helps to prepare individuals and their families as much as possible. If treatments become available, Alzheimer’s will almost certainly be more effectively treated when caught earlier.

IBM published its research in The Lancet’s science journal EClinicalMedicine. Pfizer was disclosed as providing funding to obtain data from the Framingham Heart Study Consortium and supporting IBM Research’s involvement.

(Image: Jeff Rogers, global research lead for IBM Research’s Digital Health platform, at work in the IBM Home Health Lab.)

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Alzheimer’s is a brutal disease not just for sufferers, but their loved ones too. The disease currently has no cure and causes an increasing loss of memory, confusion, and difficulty completing once familiar tasks.

IBM Australia published a paper today providing details of how machine learning and AI can be used to predict the severity of the disease and help to slow its progression.

Ben Goudey, Staff Researcher of the Genomics Research Team at IBM Research, wrote:

“Neurodegenerative diseases such as Parkinson’s, Alzheimer’s and Huntington’s are affecting millions of people around the world. While these mysterious and crippling diseases do not yet have a cure, the answer to slowing their growth may lie in prevention.

At IBM Research, our mission is to use AI and technology to understand how to help clinicians better detect and ultimately prevent these diseases in their early stages.

Whether that’s through retinal imaging, blood biomarkers or minor changes in speech, we envision a future in which health professionals have a wide array of easily accessible data available to more clearly identify and track the onset and acceleration of these conditions.”

Early diagnosis helps to prepare the sufferer and their loved ones as much as possible before degeneration takes hold. Official diagnosis also helps to make the patient available for medical trials with the hope of one day finding a full cure.

Hundreds of Alzheimer’s medical trials have been conducted since the early 2000s but with a high failure rate. Some believe this failure rate is due to late detection of the disease when there’s already significant brain tissue loss.

Research suggests a peptide called amyloid-beta changes long before memory loss occurs. Analysing the concentration of this peptide from an individual’s spinal fluid could highlight the risk decades in advance.

Accessing spinal fluid is an invasive and expensive procedure. In his post, Goudey wrote: “Hence, there is a strong effort in the research community to develop a less invasive test, such as a blood test, that can yield information about Alzheimer’s disease risk.”

Using their model, IBM predicts they could help clinicians to predict the risk of Alzheimer’s with an accuracy of up to 77 percent. Goudey said his team’s approach can be extended to other spinal fluid-based biomarkers.

The full paper is published in the science journal Nature here.

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Conditions such as Alzheimer’s, Parkinson’s, Huntington’s, and cystic fibrosis are suspected to be caused by misfolded proteins. Being able to predict a protein’s shape enables a greater understanding of its role within the body.

Previous techniques used for determining the shapes of proteins – such as cryo-electron microscopy, nuclear magnetic resonance, and X-ray crystallography – takes years and costs tens of thousands of dollars per structure.

AI, the researchers hope, will enable target shapes to be modelled from scratch without requiring previously solved proteins to be used as templates.

DeepMind calls their AI-powered folding efforts AlphaFold.

AlphaFold uses two different methods to construct predictions of protein structures:

1. 1. The first method repeatedly replaces pieces of a protein structure with new protein fragments, building on a technique commonly used in structural biology. A neural network invents new fragments.

1. The second method is called ‘gradient descent’ which is a mathematical technique applied to entire protein chains rather than pieces and makes small, incremental improvements.

Image Credit: DeepMind

DeepMind says its work is a successful demonstration of how AI can reduce the complexity of tasks such as protein folding; speeding up the diagnosis and treatment of some of the world’s most debilitating conditions.

In a contest organised by the Protein Structure Prediction Centre, AlphaMind was judged the winner among a total 98 algorithms by predicting the shapes of 25 out of 43 proteins. The runner-up, in comparison, could only predict three of the 43 proteins.

“For us, this is a really key moment,” said Demis Hassabis, co-founder and CEO of DeepMind. “This is a lighthouse project, our first major investment in terms of people and resources into a fundamental, very important, real-world scientific problem.”

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More reliable predictions

Starting with Alzheimer’s disease, the most common cause of dementia, a new algorithm is able to predict its onset with 84 percent accuracy. The author of the research, Sulantha Sanjeewa, a computer scientist at McGill University in Canada, believes it could help to slow or one day even stop the onset of debilitating symptoms which can include memory loss and difficulties with thinking, problem-solving or language.

“If you can tell from a group of individuals who is the one that will develop the disease, one can better test new medications that could be capable of preventing the disease,” said co-lead study author Dr. Pedro Rosa-Neto, an associate professor of neurology, neurosurgery, and psychiatry, also at McGill University.

Patients who are deemed at risk of developing Alzheimer’s could be prioritised for new trials of treatments which aim to slow its progress. Clinical trials run between 18 and 24 months but if those selected for treatment never go on to develop Alzheimer’s than it’s difficult to determine whether it was effective.

While still in its early stages, the findings suggest the AI’s ability to analyse brain scans offer more reliable predictions than humans alone. It was trained by being shown PET scans of nearly 200 patients up to 24 months before they developed the disease. This was compared with their scans after which showed the buildup of amyloid in areas of their brain – a protein often present in patients with cognitive impairment.

The study’s findings were published online in July in the journal Neurobiology of Aging.

Able to predict the severity of symptoms

Schizophrenia affects just 1.2 percent of the American population (around 3.2 million people) but it has severe effects. The inability to distinguish what is real, something often characterised by the condition, can pose a danger both to the individual and others.

Groundbreaking research conducted by IBM and the University of Alberta could soon help doctors diagnose the onset of the disease. It can even determine the severity of symptoms using a simple MRI scan and a neural network built to look at blood flow within the brain.

“This unique, innovative multidisciplinary approach opens new insights and advances our understanding of the neurobiology of schizophrenia, which may help to improve the treatment and management of the disease,” says Dr. Serdar Dursun, a Professor of Psychiatry & Neuroscience at the University of Alberta.

The neural network behind the AI was trained on a dataset of 95 fMRI images from the Function Biomedical Informatics Research Network. Both scans of patients with schizophrenia and those of a healthy control group were included. From this data, it was possible for the neural network to build a predictive model able to correctly determine those with schizophrenia with 74 percent accuracy.

“We’ve discovered a number of significant abnormal connections in the brain that can be explored in future studies,” Dursun continued, “and AI-created models bring us one step closer to finding objective neuroimaging-based patterns that are diagnostic and prognostic markers of schizophrenia.”

Furthermore, the model was able to predict the severity of symptoms once they set in. With this information, treatment options can be planned ahead of time as well as getting conscious authorisation from the individual to restrain them if needed to protect themselves and others.

Both pieces of research provide an exciting look at how AI can be used to predict and diagnose medical conditions, and hopefully provide more effective treatment.

What are your thoughts on the use of AI to diagnose and predict medical conditions?

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