Addenbrooke's Hospital in Cambridge along with 20 other hospitals from across the world and healthcare technology leader, NVIDIA, have used artificial intelligence (AI) to predict Covid patients' oxygen needs on a global scale. The research was sparked by the pandemic and set out to build an AI tool to predict how much extra oxygen a Covid-19 patient may need in the first days of hospital care, using data from across four continents. The technique, known as federated learning, used an algorithm to analyze chest x-rays and electronic health data from hospital patients with Covid symptoms. To maintain strict patient confidentiality, the patient data was fully anonymized and an algorithm was sent to each hospital so no data was shared or left its location. Once the algorithm had'learned' from the data, the analysis was brought together to build an AI tool which could predict the oxygen needs of hospital Covid patients anywhere in the world.

The research was sparked by the pandemic and set out to build an AI tool to predict how much extra oxygen a Covid-19 patient may need in the first days of hospital care, using data from across four continents. The technique, known as federated learning, used an algorithm to analyse chest x-rays and electronic health data from hospital patients with Covid symptoms. To maintain strict patient confidentiality, the patient data was fully anonymised and an algorithm was sent to each hospital so no data was shared or left its location. Once the algorithm had'learned' from the data, the analysis was brought together to build an AI tool which could predict the oxygen needs of hospital Covid patients anywhere in the world. Published today in Nature Medicine, the study dubbed EXAM (for EMR CXR AI Model), is one of the largest, most diverse clinical federated learning studies to date.

Addenbrooke's Hospital in Cambridge along with 20 other hospitals from across the world and healthcare technology leader, NVIDIA, have used artificial intelligence (AI) to predict Covid patients' oxygen needs on a global scale. The research was sparked by the pandemic and set out to build an AI tool to predict how much extra oxygen a Covid-19 patient may need in the first days of hospital care, using data from across four continents. The technique, known as federated learning, used an algorithm to analyse chest x-rays and electronic health data from hospital patients with Covid symptoms. To maintain strict patient confidentiality, the patient data was fully anonymised and an algorithm was sent to each hospital so no data was shared or left its location. Once the algorithm had'learned' from the data, the analysis was brought together to build an AI tool which could predict the oxygen needs of hospital Covid patients anywhere in the world.

Addenbrooke's Hospital in Cambridge and 20 other hospitals from across the world and healthcare technology leader NVIDIA have used artificial intelligence (AI) to predict COVID patients' oxygen needs on a global scale. The research was sparked by the pandemic and set out to build an AI tool to predict how much extra oxygen a COVID-19 patient might need in the first days of hospital care, using data from across four continents. The technique, known as federated learning, used an algorithm to analyze chest X-rays and electronic health data from hospital patients with COVID symptoms. To maintain strict patient confidentiality, the patient data was fully anonymized and an algorithm was sent to each hospital so no data was shared or left its location. Once the algorithm had "learned" from the data, the analysis was brought together to build an AI tool which could predict the oxygen needs of hospital COVID patients anywhere in the world.

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.

My nephew, Michael, died on 22 May 2019. He was 15 years old. He loved his family, tractors, lorries, tanks, spaceships and video games (mostly about tractors, lorries, tanks and spaceships), and confronted every challenge in his short, difficult life with a resolute will that earned him much love and respect. Online in his favourite game, Elite Dangerous by Frontier Developments, he was known as CMDR Michael Holyland. In Michael's last week of life, thanks to the Elite Dangerous player community, a whole network of new friends sprang up in our darkest hour and made things more bearable with a magnificent display of empathy, kindness and creativity.

The tumour is hard to miss on the scan. The size of a golf ball, it sits bold and white on the brain stem, a part of the organ that sends messages back and forth between body and brain. In many ways it is the master controller: from the top of the spinal cord, the brain stem conducts every heartbeat, every swallow, every breath. For this young man, the cancer came to light in dramatic fashion. The growing tumour blocked fluid draining from his brain, triggering a huge seizure.

The tumour is hard to miss on the scan. The size of a golf ball, it sits bold and white on the brain stem, a part of the organ that sends messages back and forth between body and brain. In many ways it is the master controller: from the top of the spinal cord, the brain stem conducts every heartbeat, every swallow, every breath. For this young man, the cancer came to light in dramatic fashion. The growing tumour blocked fluid draining from his brain, triggering a huge seizure.