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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The AI-powered tool is able to analyse CT scans for signs of COVID-19 infection to help quickly diagnose the novel coronavirus which has caused havoc around the world.

With heroic medical staff under more pressure than ever caring for the huge influx of people suffering with COVID-19 – in addition to all the other ailments they have to treat – such an AI-powered tool could help to free up significant amounts of time.

M3 has been testing the solution in Japan since the end of March; with the aim of deploying it across hundreds of locations. 

Hospitals will send CT scans to M3’s system which will then return the results with a 1-5 scale indicating the likelihood of COVID-19 pneumonia.

Alibaba’s system has been used in Chinese hospitals – including in Wuhan, the expected source of the COVID-19 outbreak – for a while now. The Chinese tech giant claims its AI can diagnose COVID-19 within 20 seconds with an accuracy of 90 percent or higher.

On average, a doctor takes around 20 minutes to make a diagnosis once a CT scan is available. M3 has found that the system typically diagnoses in under a minute.

While finding the accuracy to be relatively high, M3 reports the accuracy falls short of the 90 percent claimed by Alibaba. Even at 90 percent, 100 patients in every 1000 risk being misdiagnosed.

However, reading COVID-19 scans is reportedly even tricky for skilled physicians – especially as the virus is still relatively new. An AI-powered system which frees up clinical time is sure to be welcomed by all hospitals.

Catching the smaller signs of COVID-19 early could even help with providing treatment to those who need it before they get seriously ill.

This isn’t the first time AI has been looked to for assistance in tackling the COVID-19 pandemic.

Earlier this week, researchers from WVU Medicine and the Rockefeller Neuroscience Institute said they were able to predict the onset of COVID-19 symptoms three days early using AI to analyse data from Oura’s wearable rings.

Back in April, researchers from Carnegie Mellon University launched an AI-powered voice analysis system which aims to determine whether someone is suffering from COVID-19 using just a website.

While it seems likely we’re going to be living with COVID-19 in our lives for the foreseeable future, AI technologies look ready to step in and help.

(Photo by Robina Weermeijer on Unsplash)

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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.”

 AI & >.

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