Fluorescent Imaging May Aid Musculoskeletal Treatments

The importance of medical imaging sharing has been well established for years, but the value of using the cloud and other innovations to facilitate easier access to images for all who need them will only increase as new developments provide better image data.

A new form of imaging could add significantly to this by advancing the field of care for musculoskeletal disorders.

As Science X reports, researchers in Japan have developed a technique for tracking two genes in mice that are associated with the foetal stage development of the musculoskeletal system. These have provided insights into how the tendons, ligaments and cartilage form connections during fetal development.

It was done because one gene expressed red proteins and the other green, enabling the imaging of these to produce a clear picture of the development taking place in the embryonic stage. The work was published in the journal Development.

This imaging provides a 3D view of this development in a way that other imaging methods cannot. This could prove useful not only in detecting problems in foetal development, but also in adult musculoskeletal disorders and regeneration, especially when age or sporting injury is involved. This could, in turn, help inform and develop treatments.

“Traditional methods using thin tissue sections have limitations in preserving structural integrity, which makes it difficult to study the 3D organisation of these tissues,” said Professor Chisa Shukunami of Hiroshima University, where the work was carried out.

He added: “Our approach overcomes these challenges by combining tissue clearing of a newly established double fluorescent reporter mouse model with high-resolution fluorescence imaging.”

Data produced by the World Health Organization in 2022 indicated that 1.7 billion people around the world had musculoskeletal disorders, indicating that this form of imaging may prove valuable for a significant proportion of the global population, not least in countries like the UK, where life expectancy is greater and thus age-related issues are more common.

Medical Imaging Reveals Why Music Affects People So Strongly

There are few art forms that affect people emotionally and physically as much as music and a recent study used multiple medical imaging technologies to find out why.

The first line of William Shakespeare’s Twelfth Night described music as the “food of love” but whilst the power of music can be fairly easily intuited, it is far more difficult to ascertain exactlyhow it affects people.

The answer might be found in a study by a team from the University of Turku in Finland, published in the European Journal of Nuclear Medicine.

Through the use of positron emission tomography (PET) and functional magnetic resonance imaging (fMRI), the team led by Vesa Putkinen noted that the reaction engages the same hedonic systems of the brain as delicious food and positive social interactions, which triggers a neurochemical reaction to reward and reinforce that behaviour.

The neurochemical reactions in similar areas may explain why when people listen to music they like, they can get strong physical reactions such as accumulating goosebumps, feeling chills, and crying tears of joy.

As it turns out, Mr Shakespeare may have been right to compare music to food; certainly, they affect the pleasure circuitry of the brain in a similar way according to this study.

The results, particularly those found using the PET scan, found a direct connection between music and the brain’s opioid system, and this discovery could have greater implications for treatment and pain relief.

The opioid system plays a role not only in pleasure sensations but also in pain relief, which means potentially that listening to music one enjoys could affect the sensation of pain.

There have been previous studies in that field but they have focused on distraction, stress relief and more general dopamine regulation, rather than the specific neurochemical processes that make music so powerful.

By contrast, medical imaging allowed researchers to see the specific processes involved.

Study Highlights Role Of AI In Improving Scan Diagnostics

The importance of medical image sharing will be well known to many involved in patient care, with the ease with which this information can be transmitted between different settings enabling greater access for all those delivering treatment or seeking to carry out a diagnosis.

Since early and accurate diagnosis is so often the key to successful treatment outcomes, the usefulness of medical images produced by scans can be increased further by enhancements in diagnostics. This is an area in which the use of artificial intelligence has produced some exciting developments, often spotting problems the human eye would usually miss.

These have been highlighted in a new study conducted by India-based healthcare technology researcher Sriram Sitaraman, Tech Bullion reports.

His research, published in the International Journal of Scientific Research, highlighted just how much advancement AI is bringing to the area of medical diagnostics.

Perhaps the most telling finding was that suspicious lesions are now detected 2.3 years earlier on average when AI is used than when it is not.

It is not just the all-important early diagnosis rate that has been improved, but workflows for busy staff, with the typical time taken to interpret an image falling from 6.5 minutes to 3.2 minutes, while maintaining a diagnostic accuracy of 96.2 per cent. Anatomical segmentation rates are also high, at 89.2 per cent accuracy.

The report highlighted that while upfront costs for such AI systems are high, they will pay for themselves in two or three years. Such a concern may apply most to the private medical sector, but within a state-run body like the NHS it may be considered that it brings the wider social and economic value of a healthier population.

Indeed, as Public Technology recently reported, the NHS is planning to set up a £180 million framework to enable health bodies to invest in AI for diagnostics. It is set to start taking bids this summer.

Does Medical Imaging Show That Athletes Think Differently?

There are a lot of uses for medical imaging in research, but one of the most unusual recent studies has explored the neurological differences between elite-level athletes and those who play sports at an amateur or recreational level.

The study, published in the Journal of Exercise Science and Fitness, used MRI scans on 60 different people. This included 20 professional-level basketball players, 20 intermediate-skilled amateur players and 20 people of the same age who were not athletes and had no experience in basketball.

The team measured the voxel-mirrored homotopic connectivity (VMHC) of each person, as well as their grey matter density (GM) and amplitude of low-frequency fluctuations, which are quantitative indicators linked to the motor skills, coordination and executive function seen as vital to sporting success.

Compared to the 20 non-athletes, basketball players of both skill groups were shown to have a greater connection between areas that were responsible for control, motor function and decision-making ability, all factors vital for success in a team sport.

The key to a player doing well in a team sport is not just the ability to think quickly but to think clearly and coordinate with other players during a playing or training session.

This could have significant implications for athletes at all levels, as the difference between skill levels could be seen as much in the brain as it is in the body, in a way that can be seen through imaging and quantified.

This is not necessarily a new phenomenon; the National Football League has regularly used cognitive ability testing as part of its scouting and recruitment process, with certain playing positions seen as more mentally taxing than others.

There have historically been issues with cognitive testing, as whilst it can determine a form of basic general intelligence, it is not always suited for determining the types of intelligence needed in an elite player.

Using MRI or another imaging benchmark could potentially be a better alternative, but a lot more research needs to be done to determine this.

Could A Tracing Compound Reveal Treatment-Resistant Tumours?

One of the most important parts of any cancer treatment is diagnosis, which starts with effective, regular medical imaging techniques.

These help to show the location of tumours, but cannot always be used to diagnose a cancerous tumour’s characteristics, something that can fundamentally change the types of treatment that are suitable for a patient.

Given that some forms of cancer treatment such as chemotherapy take a long time, a delay caused by a treatment-resistant tumour could be potentially life-threatening.

However, a team at King’s College London led by Tim Witney, believe that they might have found a solution by taking advantage of a system regularly used for clinical trials known as a radiotracer.

A radioactive tracer is commonly used to trace biochemical reactions such as metabolic processes, but using a molecule that is designed to target a tumour-associated protein named xCT, researchers noticed that cancer cells resistant to treatment would light up brighter on imaging systems.

This means that it could reduce the amount of time it takes to see if an aggressive cancerous growth is resistant to treatment from 12 weeks to a matter of hours.

Given that with aggressive cancers time is everything, this means that months might no longer be wasted with treatments that are ultimately found to be unnecessary, allowing for contingencies for treatment resistance to be used as soon as possible to ensure they are at their most effective.

This not only has a lot of benefits in terms of treatment prognosis but also in terms of the mental health of patients, who all too often are left devastated and worried that the treatment they have endured for months at a time has been meaningless.

It also potentially could provide even more hope for the future, as a type of targeted therapy known as antibody-drug conjugate (ADC) could be used to selectively kill the resistant cells without harming healthy tissue alongside it.

What Does Elon Musk Medical Image Request Say About AI?

There are not many things Elon Musk says or does these days that escape attention, so a request made by the owner of X (formerly Twitter) may raise attention about the growing use of AI in providing medical imaging solutions.

Mr Musk asked X users to upload their medical scans in order that Grok, the platform’s AI tool, could learn to interpret them, Fortune Magazine reported. Many did so, uploading MRI and CT scans.

“This is still early stage, but it is already quite accurate and will become extremely good. Let us know where Grok gets it right or needs work.” Mr Musk said. However, the report added, Grok actually made a lot of errors in interpretation early on.

Given his proximity to power as Donald Trump’s new best friend, some may wonder what the ultimate motive of the Tesla owner is, while there have been concerns expressed about whether patient identities will be compromised by allowing X and Grok access to such data.

However, what this exercise may also do is help to highlight the growing importance of AI in interpreting medical imaging scans, which in turn makes facilities enabling their storage and transmission all the more valuable.

At its best, an AI interpretation can help spot signs of illnesses such as cancer that will usually be missed by a human eye. This can then enable a more accurate and, crucially, early diagnosis, which in many cases can make the difference between whether a patient survives or not.

It may turn out, however, that it will always require specialised AI tools to carry out this work, rather than asking a bot like Grok, which is designed for different purposes, to adapt its role to such a function.

Others, however, may want to give Grok a break, as it has apparently identified who one of the biggest spreaders of misinformation on X is, in response to a user query – Elon Musk himself.

Who Was The First Person To Be Scanned Using MRI?

One of the main medical imaging tools that has become vital for helping to diagnose and plan potential medical procedures is magnetic resonance imaging, but unlike computed tomography scans, its history is far more condensed and characterised by fairly rapid progress.

An MRI scan is a common tool used to diagnose a wide variety of medical conditions using strong magnetic fields and radio waves to create three-dimensional images of the body without using radiation.

Outside of people who are claustrophobic, have metal implants or have a pacemaker that will not allow them to enter an MRI scanner, they are often a preferred tool for scanning the human body, particularly when planning cancer treatments.

Unlike CT, which was theorised half a century before it was first attempted, the discovery of the potential for MRI to distinguish tumours from normal tissue was in 1971. Within three years the first image had been taken of Dr Andrew Maudsley’s finger by Sir Peter Mansfield, and within four years of that, the first full-body scan of a human being was made.

This scan, undertaken by Raymond Damadian, Larry Minkoff and Michael Goldsmith, was interesting, but it would take another three years for this concept to be taken further and actually be used in the treatment of a disease.

A team led by Professor John Mallard used an MRI machine on 28th August 1980 to scan a patient for the first time to actively look for disease at the University of Aberdeen. The scan found a chest tumour, which had spread to the patient’s bones, alongside an abnormal liver.

Whilst the theory behind MRI’s effectiveness had been largely proven by his point, this scan and the full-body machine that performed it led to the widespread application and introduction of MRI throughout the 1980s, fundamentally transforming diagnostics in the process.

Could A Handheld Scanner Revolutionise Cancer Screening?

The biggest factor in cancer survival rates is early diagnosis; the sooner a lump, lesion or tumour is spotted, the more treatments are available, which allows for faster, less intrusive care and an improvement in survival rates that can be measured in years.

A big part of early diagnosis is the use of medical imaging to check for early warning signs, and a major part of that is access to detailed three-dimensional scans that can check for many early signs of tumours.

A team at University College London has developed a handheld system that has the potential to revolutionise cancer screening further, allowing for high-quality, detailed images that were produced up to 1,000 times quicker than other, similar systems.

The device used photoacoustic tomography (PAT) technology, a form of ultrasound that scans for slight changes in veins and arteries.

It has historically not been clinically viable, because the scanning procedure can take over five minutes, during which time a person needs to remain perfectly still, something that is extremely difficult for most people and impossible for others.

Any movement produces a blur, which makes the image almost useless.

The UCL device, by contrast, takes less than five seconds, allowing for a much more reliable and higher quality image that can be undertaken without the need for an MRI or CT machine, although these will generally be used to confirm any findings.

A particular illustration of a clinical application of this system outside of cancer screening is inflammatory arthritis, which requires every finger joint in each hand to be scanned individually.

With older PAT machines, this took almost an hour to complete, something that was simply impossible for more frail patients, whilst the new machine is claimed to be able to do this in just a few minutes.

It also allows for changes in blood vessels that are a marker for certain types of cancer to be detected in a way impossible for other forms of conventional imaging systems.

It also makes monitoring easier; theoretically, a few seconds of scanning with the device could replace an MRI session in order to detect progression, allowing it to be done more often and more consistently.

How Did A Bakery Lead To Advanced Medical Imaging Equipment?

medical imaging - x ray

Technology evolves in rather unique ways and for rather unusual reasons, and nothing embodies this principle in medical imaging more than how an early AI-driven cancer detector originated as a way to tell pastries apart.

To explain why, it is important to understand the need for an AI scanner for bakeries in the first place.

Starting in the mid-2000s, artisan bakeries and patisseries were becoming very popular in Japan but quickly became a victim of their own success when people queued up around the block to pick up cakes, sandwiches and pastries, leaving the staff overwhelmed.

Automation could help here, but fresh pastries could not have a barcode applied to them, and wrapping them up could make it appear as though they are not actually fresh baked goods.

The solution, therefore, was BakeryScan, an AI scanner that can detect the type and quantity of baked goods on a tray, identifying often variable products by common key characteristics.

Once sufficiently trained, BakeryScan was remarkably accurate, even compared to other machine learning algorithms, and this led a doctor working for the Louis Pasteur Center for Medical Research in Kyoto to have a moment of inspiration.

Cancer cells viewed on a slide via a biopsy do not look entirely dissimilar to baked goods, so if the underlying technology could detect different types of bread, perhaps it could identify different types of cancer.

This led to the development of AI-Scan, a more variable form of the technology that could learn very quickly the different types of cells that could be cancerous from training data and provide at least a starting point if not an outright confirmation for a doctor by scanning an entire slide at once.

The system did not work the same way as modern deep learning and machine learning algorithms, but it highlighted that the biggest benefit of AI-powered medical imaging equipment was not just accuracy but speed.

Digital Health Check Pilots To Be Launched In England

 

medical cloud storage

The use of medical cloud storage enables all sorts of data to be easily shared between departments of the NHS, be they different hospitals or between hospitals and GP practices.

This may now be extended further to people’s homes under plans to pilot a new DIY digital health check, which will be linked to the NHS app.

As Pharma Forum notes, this was due to become available earlier this year, but after some delays, the system will be trialled in early 2025 in Norfolk, Medway District Council in Kent and the London borough of Lambeth.

It will offer a digital DIY check at home as an alternative to the annual in-person checks offered to patients aged between 40 and 74, with the aim being to have a million such checks carried out within four years. It will test for signs of conditions such as heart disease, diabetes, kidney problems and dementia.

According to the Department of Health and Social Care (DHSC), around 1.3 million tests have been carried out a year.

An initial pilot carried out in Cornwall in 2022 allowed patients to take a blood sample with a home testing kit, as well as fill in an online form.

“Over 16 million people are eligible for an NHS Health Check, but current data shows that only around 40 per cent of those invited went on to complete one,” A DHSC spokesperson said, noting that men were the most reluctant to seek help but are at a higher risk of cardiovascular disease.

As the Forum notes, not everyone is convinced by this digital self-reporting approach, with the Royal College of General Practitioners expressing scepticism over its reliability when carried out by those with limited medical knowledge.

The NHS app itself is, however, already in widespread use with more than 34 million users. Among its key benefits is the two-way flow of information that enables patients to see their own medical records.