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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Earlier diagnosis of any medical problem is always good. Often it increases the chance of successful treatment, minimises suffering, and can increase life expectancy in terminal cases.

Parkinson’s is a degenerative affliction without any known cure. Caught early, however, and measures can be taken which helps to slow its progression.

Speaking to Forbes, Medopad CEO Dan Vahdat said:

“Our partnership with Tencent comes from a shared vision to change the future of healthcare as we know it.

In combining Medopad’s medical expertise and Tencent’s technical capabilities, we hope to provide the technology needed to support clinicians to predict preventable complications for people with Parkinson’s disease.”

The disease affects the central nervous system, predominately the motor system. As such, the most obvious symptoms early on are shaking, rigidity, slowness of movement, and difficulty walking.

Tencent and Medopad use AI trained on existing video footage of patients to spot for concerning symptoms. The video analysis was conducted in collaboration with Kings College Hospital in London.

Both companies hope their system will reduce how long it takes for a motor function assessment to be performed from over 30 minutes to less than three. Speeding up diagnosis helps more people to begin treatment faster.

Medopad is known for using wearables for its healthcare solutions, but the AI system will not require any physical sensors or devices to be worn. Reducing the amount of hardware required helps to reduce cost and complexity so more healthcare providers can get started.

The firms are not the only companies looking to use AI to improve healthcare (in fact, we’ve got a whole category dedicated to it – if you’re so inclined.)

Most AI healthcare developments we’ve covered focus on the scourge of cancer, which now affects one in two people. Almost everyone has either had it or knows someone close to them.

Back in August, we covered how AI is reducing how long it takes to produce cancer treatment plans from days to potentially minutes. For those undergoing treatment, AI is even stepping in to help make treatment more bearable.

Tencent and Medopad are delivering another example of how AI can have a huge impact on healthcare, and not just for cancer.

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