As the use of medical imaging grows, the work involved can be more specialised and means that getting enough training to help make the best use of medical imagining solutions is a challenge.

However, new developments may help make this task easier, including a new innovation that follows on from one devised and launched only last year.

As Venturebeat reports, the launch last August of Stable Diffusion, a text-to-image foundational model, by Stability AI, prompted an idea from Stanford University radiologist Christian Bluethgen. He asked whether in fact it was possible to combine a genuine medical need with the creating of high quality images using basic text prompts.

The result was his collaboration with Stanford Graduate student Pierre Chambon, a mathematical and computational engineering researcher, which led to a study designed to establish the capacity of stable diffusion to generate X-rays. To their delight, they found it achieved this task very well.

All this, Mr Bluethgen, will help with the training of medics who might otherwise see very few scans relevant to their specialist area. He observed: “When you are working in a setting with scarce data, your performance correlates with experience – the more images you see, the better you become.”

Such a development may be just one indication of how better use of data technology is enabling medical imaging to be used with increasing effectiveness as a diagnostic tool, with improved training further boosting the capacity of specialists to identify medical problems when they emerge.

A further development in X-ray imaging may have emerged at the University of New South Wales in Sydney. In a paper published in the journal Nature Communications, researchers have revealed how a new algorithm has been devised that can enhance images of hydrogen fuel cells. It notes that the very same technique could be used to improve medical imaging.

It will come as news to nobody that the NHS faces a tough winter. Fears of a ‘twindemic’ of high rates of Covid and flu infections, the backlog of operations caused by the pandemic remains and pay strikes among nurses and ambulance staff all speak of a health service under severe pressure.

Other outstanding cost concerns faced by the NHS include the high cost of agency staff, with doctors paid as much as £5,234 for a single shift according to data uncovered by the Labour Party in a Freedom of Information request, while many hospitals have outstanding maintenance issues as well. 

The question of just how much money could be needed to fix buildings, solve shortages of resources and settle pay disputes, alongside the question of where the money will come from, arises at a time when the UK is believed to have begun a protracted recession. 

Never has it been more important for cost-efficiency and the elimination of waste to be brought to bear on the NHS. That is where the low cost of cloud based medical imaging may be critical when keeping patient scans and other records, enabling easy access without high expense.

As Tech Radar reports, cloud storage specialists Backblaze have noted that hard disk drive prices have been falling, with prices now set to fall to as low as US$0.1 per GB of data. This compared to $0.11 in 2009.

Not only is this a rare example of something getting cheaper in a world beset by inflation, but also offers a considerable cost-saving opportunity to the NHS. While this alone will not solve all the problems, it will certainly help, not least by delivering better outcomes for patients, which is the whole point of the service in the first place.

While the NHS faces many challenges, it seems that at least the services the staff will use could include some cases of very low cost. 

Disaster recovery is something every business or organisation needs to prepare for, whether it is a physical calamity on its premises like fire or flood, or a major data problem such as a power outage or cyber attack.

The capacity for cloud disaster recovery services to help is something many will have factored in when adopting the system. Buy storing and saving data in a particularly safe way it will ensure that medical imaging services are not disrupted and information from scans can still be sent to whichever practitioners need to see them.

Some may think an internet outage is an event that is unlikely to happen to them, especially with so many wireless services being available. But that is not always the case, especially in more remote corners of the UK.

Few places fit that description more than the Shetlands, which has just endured a loss of internet services as well as telecoms after two undersea cables connecting the islands to the mainland were damaged. These have now been fixed.

The incident followed another cable, linking the Shetlands with the Faroe Islands, also being damaged and affecting communications.

During this time, attempts to communicate with the mainland were futile, but any data stored in the cloud will have been safe, ensuring that once the cables were fixed it could be transmitted where necessary.

Because health services on the archipelago of 23,000 people are limited in scope, patients from the islands who need a scan – around 600 people a year – currently have to travel by ferry to Aberdeen to get one, so no scanning data would have been held up by the cable problems.

However, this situation is about to change. As Shetland News reports, NHS Shetland is moving its estates team to a site once occupied by the fish market in Lerwick to make space for the town’s Gilbert Bain Hospital to have a long-awaited MRI scanner fitted.

In 2022, the biggest leap forward for medical imaging services was the adoption of artificial intelligence and machine learning to help doctors diagnose diseases in tissue samples and diagnostic scans,

This is why, for example, a system designed to detect different types of pastries is also used to scan for cancer.

However, a team from Rutgers University led by Partho Sengupta MD FACC and Naveena Yanamala PhD discovered a breakthrough in AI-assisted ultrasonic imaging that would enable an AI to detect changes in the function and structure of the heart at a molecular level.

This could be used at a very early stage to detect for signs of heart disease, potentially saving countless lives.

The system works by analysing patterns in echocardiogram imagery to determine at an expert level the types of conditions that lead to heart failure, establishing new biological markers that can help doctors plan appropriate cardiac treatment.

The biomarker can be applied to any current ultrasound device, which includes miniature hand-held ones that can fit in a pocket, and creates what the doctors call an “ultrasonic biopsy”.

There have been several similar stories of ultrasound being used in conjunction with AI to help detect heart disease, with one particularly fascinating study detecting weak heart pumps from data recorded by an Apple Watch, a consumer-grade smart device with limited health and fitness tracking.

With diseases like heart failure, lung disease, dementia and cancer, time is a critical factor in determining potential recovery. The longer the disease goes undiagnosed, the more of an effect it can have on people’s lives and the lower the chance of a full recovery.

Time is the difference between excising a benign tumour and being unable to operate due to cancerous tissue spreading to a vital organ.

This is a similar case with heart disease, as the sooner it is discovered, the more options are available to doctors.

The constantly-adapting nature of Covid-19 made it difficult for medical staff to provide an accurate prognosis for hospitalised patients. However, scientists have now developed an artificial intelligence (AI) device that can offer a framework for continuous monitoring. 

Feinstein Institutes for Medical Research (FIMR) published their study in Nature Communications, having looked at more than 35,000 Covid-19 patients over the last two and a half years. 

Dr Theo Zanos, associate professor at the FIMR and Institute of Bioelectronic Medicine, led the research, which had to include the different changes in variants, waves and outcomes of the virus. 

He said: “Covid-19 was one of the most dynamic diseases we’ve witnessed in modern history and information about how to care for patients was constantly evolving.”

Scientists had to collect enough data to create a tool that accounted for the rapid developments. The auto-updating clinical device has been designed to assist clinicians in making decisions for patient care. 

While previous models have failed by not being able to consider fluctuations in patients’ characteristics and outcomes, the new model updates automatically when it detects a change in performance. 

President and chief executive officer of the FIMR Kevin J Tracey added the study was able to analyse data, and use modern technology to develop the tool and provide insights into the virus. 

It works by focusing on five early patient data points – age, serum urea nitrogen, lactate, serum albumin and red cell distribution width. By doing this, it was able to remain accurate across four waves and different variants. 

“Dr Zanos’ strategy provides a new model to study Covid-19 as a guide to clinical decision-making and better outcomes,” Mr Tracey added. 

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