Cancer Research UK has called for more screening for lung cancer as a key means of reducing the number of people dying from the disease.

The charity has produced its own ‘manifesto’ in anticipation of a general election next year, in which it has issued a series of commitments concerning cancer care and research that it would like to see adopted by whoever forms a government after the votes are counted.

On the issue of screening, it said: “The UK Government should transform and optimise cancer screening programmes and accelerate the roll-out of the lung cancer screening programme in England.”  

Such a measure could require more capacity for storing medical images to ensure it works efficiently, but the benefit could be to detect far more cases than at present.

While smoking numbers are falling and the government is raising the legal age for smoking by a year on an annual basis, screening could capture some unexpected cases of lung cancer, such as among those who do not smoke.

The BBC recently reported on such an instance, in which Lorraine Wingert-Scheeres from Pembrokeshire discussed how her father Jack Cordwell died from lung cancer despite giving up smoking 20 years earlier.

Cancer Research UK has argued that 2,400 more cases a year could be treated in Wales with early diagnosis thanks to more screening.

The Welsh government has said Public Health Wales is looking at how screening can be improved.

While the NHS is devolved in Wales, Cancer Research UK has said that work should be undertaken to overcome regional differences in treatment and early diagnosis, not least in different parts of England. It may be that a more comprehensive screening programme could do this.

Some cancers are already getting this sort of attention, with the government choosing International Men’s Day on November 19th to launch the biggest prostate cancer screening programme in decades.

The problem of long NHS waiting lists does not appear to be going anywhere. In fact, it seems to be getting worse, year on year. 

However, using medical imaging technology could help shorten waiting list times and enable hospitals to help more patients. 

With 7.71 million people waiting for appointments, NHS waiting lists remain at their third-highest levels. What’s more, only 58.2 per cent of patients were waiting less than the target of 18 weeks. 

NHS Nightingale Hospital Exeter, however, has bucked the trend, recently admitting a 300 per cent increase in patients due to installing medical imaging and diagnostic technology. 

Its equipment, which includes Aplio i800 Prism Edition machines, is able to provide high resolution images that allow precision scanning. It also comes with applications, such as shear wave elastrography, contrast enhanced ultrasound, and superb microvascular imaging. 

This can help doctors with the diagnostic procedure, helping to give answers to patients quicker and relieve the backlog of those waiting for a diagnosis. 

Principal songographer at NHS Nightingale Jane Baker said: “We have already seen improved productivity, with faster examinations and more accurate diagnoses due to superb image quality.”

Patients have also noted that their referral from the GP to their appointment has been “quick” and “efficient”. 

Indeed, the new technology has helped reduce the waiting list from six to less than two weeks. 

This is due to the fact the ultrasound department now has four outpatient scanning suites, having previously just had one. It can, therefore, see three times as many patients as it used to, helping to get through the waiting list at a much faster rate.

To find out more about cloud medical image storage, get in touch with us today.

A major new deep learning breakthrough that enables CT scans to segment tumours, lymph nodes and surrounding tissue has won an international prize.

The development, by postdoctoral researcher in medical radiation physics at Stockholm University Mehdi Astaraki, won his prize at the International Conference on Medical Image Computing and Computer Assisted Intervention (MICCAI).

Dr Astaraki’s work, which was focused on head and neck cancer, won the top prize in the Segmentation of Organs-at-Risk and Gross Tumor Volume of Nasopharyngeal Carcinoma for Radiotherapy Planning category, triumphing in a field of 395 entrants.

He presented his work at the event in the Canadian city of Vancouver in October, where his algorithms demonstrated outstanding performance, coming first in one assessment category and second in another.

His work will mean medical practitioners sharing images using the technology will have better segmented details to study, meaning the data will help inform the treatment paths of patients more accurately and appropriately.

Indeed, a priority for Dr Astarki and his team will be to integrate his developed model with PACS systems, enabling it to play a vital role in planning radiation treatment. His presentation highlighted his desire for collaboration to become recognised as a crucial element of the development of medical image analysis.

Having participated in the contest in October and subsequently picking up the prize, he will present the findings in a news and views session in the spring, offering an update on the development of the system.

The development of new PACS-compliant medical imaging systems may also feature heavily next year, when the 2024 edition of MICCAI takes place in the Moroccan city of Marrakesh.

According to the organisation, the event will be “nothing short of groundbreaking”, although this is both for the new science on show and the fact it is the first time the annual event will have been held in Africa.

Artificial intelligence (AI) is being used in more and more industries these days, including the medical sector, as it can boost efficiency and speed at a time when healthcare professionals are overstretched. 

One way AI has been able to support the industry is through diagnostic tools, scanning images against data it has been programmed with to be able to predict certain health outcomes in patients. 

Although many are sceptical of relying on machinery for diagnoses, the technology has become so intelligent lately that AI could even be better at diagnosing conditions than doctors. 

This has been shown recently with the Queen of Hearts AI model, which has outperformed doctors at diagnosing heart attacks, being able to do so three hours before physicians. 

Martin Herman, chief executive officer of Powerful Medical cardio (PMcardio), which has created the Queen of Hearts AI model, noted that the economic cost of heart attacks in the US is $150 billion (£119.63 billion) a year. 

He stated: “Through our clinical trials, we have demonstrated that the Queen of Hearts can significantly reduce these costs by enabling accurate diagnosis and prioritisation of patients.”

PMcardio’s machinery has been programmed with intelligent algorithms, based on data from more than 500,000 patients. 

It was then successfully used in clinical trials of over 20,000 patients, demonstrating its ability to diagnose heart attacks before they occur. 

With 460 people dying from a circulatory disease every day in the UK, and 260 hospital admissions being due to a heart attack, the PMcardio AI technology could become a very useful tool to reduce these numbers and help more patients before they need emergency care.

Want to find out more about clinical imaging? Give us a call today.

A new AI innovation promises to overcome one of the most notable current issues with the management of medical images; the maintenance of privacy while sharing data for diagnostic purposes.

Balancing the need for the right diagnosis to be reached as quickly as possible with the data privacy rules in various jurisdictions is a challenge researchers in South Korea and the US believe they have achieved using a new AI system, Medical Xpress reports.

Researchers from Daegu Gyeongbuk Institute of Science and Technology (DGIST) worked with their counterparts from Stanford University to alleviate concerns that when data is shared with deep learning models, there is a risk of privacy violations.

The new system, called federated learning, avoids collating all data at a central server and instead only sends the learned models from each of the hospitals providing details to the server.

An additional concern is that repeated data transfers can cost time and money, but this has been addressed by building into the system limits on how often data needs to be sent. This is done not by placing a strict limit on data transfers, but by enhancing the process of model learning, which means less data needs to be sent.

Professor Sang-hyun Park of the DGIST team said: “This research will allow models to learn universally across all institutions participating in learning without sharing personal information or data.”

This will enable emerging AI technology to do what it does best – using learned knowledge to evaluate data images better and thus improve diagnostic accuracy and success.

An example of the advances this technology offers in diagnostics was recently highlighted by new research from the Institute of Cancer Research (ICT) and The Royal Marsden NHS Foundation Trust, which found that AI is nearly twice as accurate as biopsies at grading the aggressiveness of sarcomas.

This greatly improves the diagnosis and potential for successful treatment of these rare soft tissue cancers. Dr Paul Huang of the ICT said it has the “potential to transform the lives of people with sarcoma”.

In today’s rapidly evolving healthcare landscape, the efficient management of medical imaging data is more critical than ever before. Traditional methods of storing and sharing medical images have long been plagued by inefficiencies, high costs and accessibility issues. However, a new solution is emerging that promises to change the game entirely.

Imagine a healthcare system where the burden of maintaining costly, redundant hardware is lifted and where medical images are seamlessly accessible anytime via the cloud within hospitals. This is the promise of modern cloud-based solutions within medical imaging and it’s reshaping the way healthcare institutions operate.

One of the most significant advantages of adopting this way of working is the substantial cost savings. In the past, hospitals and medical facilities had to invest millions in purchasing and maintaining expensive hardware for their picture archiving and communication systems (PACS).

This hardware often became outdated, requiring costly upgrades or replacements, and posed challenges in terms of scalability. Enter cloud based PACS, where the need for local hardware becomes obsolete. These systems operate in a secure, centralised cloud environment, eliminating the financial burden associated with maintaining infrastructure.

Furthermore, the cloud can enhance accessibility in the healthcare sector. Imagine a scenario where medical images can be easily and securely shared, not only between healthcare institutions but also with patients and external collaborators. This newfound accessibility streamlines patient care and accelerates diagnostic processes.

The benefits extend beyond cost savings and-accessibility. They enable hospitals to stay up to date with the latest technology without worrying about legacy issues.

In conclusion, the shift towards this technology in medical imaging is revolutionising healthcare by cutting costs, enhancing accessibility and improving overall efficiency. 

Hospitals and healthcare institutions are embracing this transformation to provide better care, reduce operational expenses and meet the ever growing demand for advanced medical imaging services.

Biomedical research companies can only advance their products if they test them, which is why clinical trials on humans are essential. 

However, they need to adhere to strict rules and regulations to make sure they are as safe as possible and ethically sound. 

According to the UK Research and Innovation, the organisation wanting to conduct the trial must show they have received approval from certain bodies before beginning the study. 

These include regulatory authorisations, such as the Medicines and Healthcare products Regulatory Agency (MHRA); research ethics committee and Health Research Authority; and the NHS, if within the UK. 

The head of the study must also inform the Medical Research Council (MRC), which funds the research, if there are likely to be amendments that alter the methodology, costs or research query. 

MRC might also ask to see the required approvals before providing the finance if the research subject is particularly sensitive. 

However, it does not need to be shown evidence of submissions, permissions or amendments, or be kept informed of changes to the study unless they are substantial. 

Before going ahead with clinical trials, it is advisable for researchers to take a close look at their systems for good clinical practice and safe reporting.

They need to consider what the risks to the participants might be, develop systems to manage the risks, and make sure they monitor and manage the trial safely and effectively. 

Although there are quite a few regulations to adhere to, clinical trials can have a huge impact on the release of medicines and vaccines. 

More than half a million people in the UK took part in Covid-19 research, which helped develop vaccinations and treatments for the disease at the height of the pandemic in 2020. 

For clinical trial management software to monitor and analyse the data effectively, call us today.

Doctor woman in uniform sitting in office in hospital with ultrasound diagnostic machine equipment and ready to examine patients doing ultrasound of thyroid gland. Ultrasound scanning diagnostic

The dawn of the modern medical imaging world, where doctors can examine and diagnose illnesses without necessarily requiring biopsies or physical examination, started at the end of the 19th century.

After Wilhelm Röntgen captured an image of his wife’s hand in 1895, it unlocked an entire field of medical diagnosis, which would later include CAT scans and ultrasound.

Whilst the history of the X-ray and computer tomography are well-documented and well-known, ultrasound in a medical context had a far more complex history, in part because it took so many steps to even realise it could be used in a medical context.

Ultrasound has existed for as long as bats have, but the echolocation ability of these flying mammals was only discovered in 1794 by scientist Lazzaro Spallanzani, and it took another century for the Galton whistle, one of the first ultrasound devices ever made, to be invented.

However, people wanted to know if it would be possible to make a device that took advantage of ultrasound and echolocation to inspect objects without opening them where an X-ray would be unusable.

This led in the late 1930s to several scientists in a wide range of fields attempting to piece together this functionality.

Floyd Firestone would create a testing device known as the Supersonic Reflectoscope in 1940, but it would not be used for medical purposes.

Meanwhile, Karl Theo Dussik and his brother Friedrich completed the first partial scan of a human body part in 1941, creating a faint outline of the brain.

The first successful ultrasound used for clinical purposes rather than research purposes was by John Wild in 1949 to check the intestines and bowels of patients who had been the victims of bomb strikes.

This work was refined and first published in The Lancet medical journal in March of 1951, and it very quickly was found to have particular promise for identifying tumours.

This early pioneering work has steered the imaging world ever since, as ever more advanced technology is used to help save lives.

Doctor Showing Mri Image Of Spine To Patient During Medical Cons

Since the discovery of the X-ray at the end of the 19th century, medical imaging technologies have evolved and developed intensely, providing doctors with the increasingly detailed information they need to diagnose diseases and prescribe appropriate treatments.

However, improvements in imaging technologies have not only helped the understanding of doctors but also of patients as well, with a three-dimensional image often helping shed a brighter light on a medical issue than an X-ray to someone without the radiography knowledge to interpret this.

In some cases, that is not necessary, as certain conditions and medical maladies are obvious with an X-ray, but sometimes it can be difficult to visualise either a medical concern or a potential treatment without a three-dimensional aid, and some doctors are adding tactility to this as well.

Three-dimensional printers have seen a lot of use in the medical world, particularly in the field of dentistry where 3D printing is sometimes used in place of traditional dental impressions and as part of the CEREC process of creating dental crowns.

Increasingly, however, doctors in other fields are printing 3D models of CAT scans to show the location of issues such as blood clots, tumours, ulcers and other issues that are sometimes difficult to visualise even with the help of a visual display.

They can also, according to a 2022 study, help with the planning of complex medical procedures, as well as with education, as it highlights the specific differences between the anatomy of different patients and allows for more involved patient-centred planning.

Whilst outside the scope of imaging, diagnosis and treatment planning, 3D printing is also used to create patient-specific medical devices that are designed specifically with their body in mind with exceptional potential for tailored medical treatments in the future.

In the present, whilst not every doctor needs or wants to use 3D imaging as part of their explanations of diagnoses to patients, it can sometimes be beneficial to thier understanding to use a visual aid.

A doctor wears a VR headset and records the patient's holographic diagnostic data on the virtual interface, innovation, science and technology.

The government has announced it will invest £21 million in rolling out artificial intelligence (AI) tools across the NHS.

It hopes the technology will help diagnose and treat patients more quickly, helping to improve healthcare across the country. 

Health and social care secretary Steve Barclay stated: “AI tools are already making a significant impact across the NHS in diagnosing conditions earlier, meaning people can be treated more quickly.”

NHS Trusts can apply to the AI Diagnostic Fund for AI imaging and decision support tools for patients suffering from cancers, heart conditions and strokes. 

The technology will be able to analyse chest X-rays, of which more than 600,000 are performed every month in England alone. 

Being able to assess the tests quickly could help lung cancer patients get the diagnosis and help they need faster.

Mr Barclay also committed to providing AI stroke-diagnosis technology to all stroke networks by the end of the year. 

Currently, 86 per cent of centres have this technology, which is thought to halve the time for stroke patients to get treatment. This can triple the chance of stroke sufferers being able to live independently. 

The NHS is also piloting AI software to reduce the number of missed hospital appointments. 

By analysing data and external insights like traffic and weather conditions, it can provide patients with more convenient times for their appointments, so they are less likely to miss it. 

If you’re looking for medical imaging management that can support AI tools, get in touch with us today.