The researchers, from WVU Medicine and the Rockefeller Neuroscience Institute, first announced the potentially groundbreaking project in April.

At the time, the researchers found they could predict COVID-19 symptoms – including fever, cough, and fatigue – up to 24 hours before their onset.

“The holistic and integrated neuroscience platform developed by the RNI continuously monitors the human operating system, which allows for the accurate prediction of the onset of viral infection symptoms associated with COVID-19,” said Ali Rezai, M.D., executive chair of the WVU Rockefeller Neuroscience Institute.

“We feel this platform will be integral to protecting our healthcare workers, first responders, and communities as we adjust to life in the COVID-19 era.”

Participants in the study were asked to log neurological symptoms like stress and anxiety in an app. The Oura ring, meanwhile, automatically tracks physiological data like body temperature, heart rate, and sleep patterns.

“We are hopeful that Oura’s technology will advance how people identify and understand our body’s most nuanced physiological signals and warning signs, as they relate to infectious diseases like COVID-19,” explained Harpreet Rai, CEO of Oura Health.

“Partnering with the Rockefeller Neuroscience Institute on this important study helps fulfil Oura’s vision of offering data for the public good and empowering individuals with the personal insights needed to lead healthier lives.”  

Using an AI prediction model, the researchers have improved their ability to track COVID-19 symptoms from 24 hours before their onset to three days.

The accuracy rate for the current system is 90 percent. While impressive, that does mean 100 people in every 1000 patients could be misdiagnosed if such a system was widely rolled out.

This isn’t the only research into the use of wearables to help tackle the COVID-19 pandemic – Fitbit is also conducting a large study into whether its popular wearables can detect markers which may indicate that a user is infected with the novel coronavirus and should therefore quarantine and seek a professional test.

With the COVID-19 pandemic looking set to disrupt our lives for the foreseeable future, it seems AI and wearables provide some hope of diagnosing cases earlier, limiting reinfection, and helping people return to some degree of normality.

The post AI uses data from Oura wearables to predict COVID-19 three days early appeared first on AI News.

Cancer has a higher successful treatment rate the earlier it’s diagnosed. The later the diagnosis, the greater the risk of death or long-term debilitating effects.

In her speech, Mrs May will say:

“Late diagnosis of otherwise treatable illnesses is one of the biggest causes of avoidable deaths.

The development of smart technologies to analyse great quantities of data quickly, and with a higher degree of accuracy than is possible by human beings, opens up a whole new field of medical research and gives us a new weapon in our armoury in the fight against disease.

Achieving this mission will not only save thousands of lives, it will incubate a whole new industry around AI-in-healthcare. It will create high-skilled science jobs across the country – drawing on existing centres of excellence in places like Edinburgh, Oxford, and Leeds – and help to grow new ones.”

At least 50,000 people a year suffering from lung, prostate, ovarian, or bowel cancer will be diagnosed earlier due to AI, May will claim.

To achieve this goal, researchers will require access to large amounts of medical records to cross-reference patients’ lifestyles, genetics, and prior conditions to highlight when individuals are most at risk.

The UK’s National Health Service (NHS) has vast amounts of data. Every time a patient visits a service anywhere in the country, a record is made.

A patient’s medical record can include:

• • treatments received or ongoing

• • information about allergies

• • current medication(s)

• • any reactions to medications in the past

• • any known long-term conditions, such as diabetes or asthma

• • medical test results such as blood tests, allergy tests, and other screenings

• • any clinically relevant lifestyle information, such as smoking, alcohol or weight

• • personal data, such as age, name, and address

• • consultation notes, which a doctor takes during an appointment

• • hospital admission records, including the reason

• • hospital discharge records, which will include the results of treatment and whether any follow-up appointments or care are required

• • X-rays

• photographs and image slides, such as MRI scans or CT scans

How this data is shared and used to improve medical care remains a controversial topic. For example, the NHS’ sharing of data with Google-owned DeepMind has often come under scrutiny.

An independent panel last year found the deal between DeepMind and the Royal Free NHS Foundation Trust to develop an app for diagnosing kidney disease was ‘illegal’ and did not do enough to safeguard patient data.

Theresa May’s party, the Conservatives, have also faced widespread criticism over under-funding and privatisation of the NHS — leading to increased staff pressure and longer waiting times for patients.

Two-thirds of NHS trusts reported having at least one cancer patient waiting more than six months last year, while almost seven in 10 (69%) trusts said they had a worse longest wait than in 2010. One cancer patient waited 541 days for treatment.

If employed correctly, the automation offered by AI has the potential to greatly reduce staff pressure and improve patient care.

“Earlier detection and diagnosis could fundamentally transform outcomes for people with cancer, as well as saving the NHS money,” comments Sir Harpal Kumar, CEO of Cancer Research. “Advances in detection technologies depend on the intelligent use of data and have the potential to save hundreds of thousands of lives every year.”

“We need to ensure we have the right infrastructure, embedded in our health system, to make this possible.”

What are your thoughts on the use of AI in healthcare?

The post Theresa May: AI is a ‘new weapon’ against cancer appeared first on AI News.

Fully understanding the ‘internal GPS’ used by humans and other mammals to navigate from point A to B has eluded neuroscientists for decades. By analysing a new AI, which developed ‘grid cells’ similar to our brains, researchers believe we could be closer than ever.

The new AI was designed by a team from Google DeepMind and University College London to navigate a virtual environment from one point to another in the most efficient way possible.

In findings posted to science journal Nature, the AI developed grid cells similar to mammals. Grid cells were first discovered in 2005 by Norwegian neuroscientists May-Britt and Edvard Moser, earning them a share of the 2014 medicine Nobel Prize.

The neuroscientists made their discovery after observing rats navigating and finding grid cells in their brains firing at points which formed a hexagonal pattern.

Animation provided by DeepMind

Grid cells work in combination with other brain cells. This includes ‘place cells’ which activate when a mammal is in a specific location, and ‘head direction cells’ which fire when the head is pointed in a specific direction.

How all of these cells work together is less well known, but the researchers are hoping to find some answers by observing the AI. They expect this will be just the start in using AI to gain a greater understanding of biology.

You can find the formatted Nature paper here, but note that it’s behind a controversial paywall. Alternatively, the unformatted full paper is available free here (PDF).

What are your thoughts on the use of AI to gain a deeper understanding of biology?

The post Virtually Brainy: AI wires itself to navigate like mammals appeared first on AI News.