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Innovations revolutionize radiology

The field of radiology is predicted to undergo significant change over the next 20 years. The speed at which technology is developing means that these developments will occur much more quickly than they did in the previous 20 years.

Advancements in radiology are occurring at a much faster rate than in the 20th century. Technological advancements are improving the way radiologists work, providing clearer images and interacting with machines that deliver results quickly and accurately.

Radiology is now an integral part of the entire medical field, and with the use of artificial intelligence (AI) on the rise in various industries, healthcare organizations are trying to make the most out of it in radiology. As the demand for better images and the ability to manage images and analytics across the enterprise grows, physicians are looking for new innovative solutions to meet these needs.

Technology trends
Over the last few years, medical imaging has undergone a dramatic evolution owing to digital radiology solutions. Technological advancements are a crucial factor for the growth of the X-ray market. Factors, such as image quality, image-acquisition time, portability, detector form, and software create and increase the demand for digital X-ray systems. The demand for advanced digital X-ray systems increases due to benefits, such as their ability to process large volumes of data and examine patients rapidly. Various companies are developing digital systems with improved performance and efficiency. These systems enable radiologists to address a broad range of clinical applications with diagnostic-quality images and stitching functionality. Innovation and the development of advanced systems help boost the market for digital X-ray systems. Some recent developments in this market include:

Cloud computing and storage. A trend that was once viewed with suspicion but is now rapidly gaining in popularity is cloud computing and storage. There was a noticeable uptick in cloud services at the conference. As imaging volumes have ballooned, organizations have begun to question the need for, and/or wisdom of, maintaining all of those data on their own. Many smaller organizations no longer have the capacity to keep all of their data on site. Cloud storage allows them to store and access their data efficiently without the headaches associated with on-site storage.

Data security is always top of mind for radiology administrators, and cloud companies typically employ multiple security protocols. They also tend to have a significant amount of security expertise, which individual organizations may or may not possess depending on the resources that are available to them.

Artificial intelligence in digital radiography. The primary purpose of AI is for better decision-making to improve patient outcomes. Moreover, the growing population and shrinking human resources make it necessary to use technology to make the overall process of radiology more effective with fewer resources. Thus, there is an increasing demand for AI in radiology.

AI is presently advancing at a fast pace in medical care. AI algorithms, particularly deep learning, have made significant strides in image-recognition tasks and helping in low dose with quick diagnosis. AI-based X-ray solutions are used for image analysis, detection, diagnosis, and decision support. AI CAD tools assist in detecting the most common thoracic finding more effectively, such as lung nodule, consolidation, interstitial opacity, pleural effusion, and pneumothorax and BSI. AI tool enhances the visualization of lung lesion by suppressing rib signal, without the need for additional exposure, thereby lowering the dose. Recent advances in technology are providing increased efficiency as well as improved patient care.

Molecular imaging and genomics are now becoming an integral part of radiology. Physicians can study patients at the cellular and molecular level inside the body, using molecular imaging. It is possible to do so through different biological processes that molecular imaging allows. Using these techniques and processes, doctors can diagnose diseases earlier improving the care they provide.

Genomics is also a new field similar to molecular imaging in radiology. It includes the study of the body’s genes and how their functions and characteristics influence the overall effects, movement, and growth of the human body.

The primary function of genomics is to help doctors identify diseases at an early stage, enabling them to design a customized treatment for that particular disease.

However, according to a survey published December 18, 2022, in the Journal of Medical Imaging and Radiation Sciences, radiographers are split on whether they feel prepared to start using new AI technologies in daily practice.

A team led by Dr Theophilus Akudjedu from Bournemouth University in England found that the jury is out on how confident radiographers are in using AI, but most agree that teaching AI technologies should be included in the radiography curriculum. Clinical practices have progressively implemented AI, including in the field of medical imaging. Radiologists and radiographers have experienced a surge in such applications, with AI helping reduce clinical workloads as an alternative reader. But the investigators pointed out that not everyone has the education and training needed to fully understand AI’s benefits and risks.

Akudjedu and colleagues sought to assess the knowledge, perceptions, and expectations of radiographers working across various settings on the use of AI technologies. To do so, the group conducted a survey that included 314 radiographers – most reported practicing in North America.

Out of the total survey participants, 170 reported not using AI or related technologies daily, and 107 stated they had six to 10 years of radiography practice experience. The survey indicates an overall lack of understanding when it comes to AI-specific language in radiology, according to the researchers.

Their study shows that, while a considerable proportion of the worldwide radiography community expressed willingness to accept and adopt AI in practice, they, however, lack the theoretical knowledge or experience to do so.

VR adoption. The increasing incorporation of computer technology by way of AI, virtual reality (VR), wearable devices, etc., is resulting in tremendous improvements in the quality of delivered healthcare, including the X-ray equipment being developed.

X-ray machines, equipped with AI-powered augmented reality, enable the extraordinary advantage of generating real-life simulations that can grant clinicians key, valuable insights exceeding the realm of human sensory perception, translating into more in-depth, accurate, and quicker diagnoses.

From breast cancer screening to cardiovascular diagnostics to radiological training, AI and VR techniques are playing transformative roles in advancing medical imaging via exceptional accuracy enhancements, real-time analytics, communication advancements, and dynamic automations.

3D radiography. Traditional X-rays are incapable of displaying the fine detail of soft tissue of the body’s organ system. However, in leveraging CERN’s sensor-chip technology, researchers have developed a 3D scanner designed to produce 3D color X-ray images with the advanced ability to outline a patient’s soft tissues, disease markers, as well as their bones and lipids.

These innovative 3D scanners are expected to revolutionize diagnostics and treatment for various fields, such as orthopedic surgery, vascular disease, bone and joint health, rheumatology, and cancer.

Digital radiography. A common requirement for healthcare providers exploring advanced X-ray technologies is a lowered radiation exposure risk. Thankfully, the several exciting advancements in recent years in digital radiography – the digitized cousin of the X-ray, so to speak – address this need as well as bolster the reduction of retakes.

These next-generation developments in digital radiography include AI-aided X-ray interpretation, dual-energy imaging, and automatic image stitching. They are fundamental in ensuring improved image quality and enhanced patient care while fostering exceptional patient outcomes.

Dynamic digital radiography. DDR is an enhanced version of a standard digital radiography system that acquires up to 15 frames per second for as long as 20 seconds, resulting in a maximum of 300 X-ray images with a dose equivalent to about two standard X-rays. With DDR, radiation is lower than fluoroscopy or CT, and requires a shorter exam time than MRI.

The benefits of DDR are being explored across a variety of disciplines. In pulmonology, DDR can be used to visualize and quantify lung function in relationship to surrounding structures. Radiologists can watch the patient’s muscles as they breathe – as they inhale and as they exhale. DDR opens up potential for AI and various analytic applications to process the exam, quantify movement, and track changes over time. It is taking the simple chest X-ray and pairing it with high-end technology to now produce a functional exam that was not present previously.

DDR use is also being explored as an effective tool for other applications, including swallow studies for speech therapy, or as an alternative to fluoroscopy for assessing diaphragmatic movement during sniff tests. Clinicians can now see more with a dynamic X-ray. Because DDR is another X-ray technique using the same X-ray system, the equipment and technician workflow is the same. It just adds an extra minute of time. DDR is available on the same digital radiography system that performs conventional X-ray exams, providing a cost-effective solution for hospitals and practices.

For many physicians, DDR provides a more complete diagnosis and has become a key differentiator for their practice. It introduces a technology with clinical value that also has the benefit of a CPT code for reimbursement.

In the hospital environment, DDR adds value to what can be done. For patients, there is a wow factor when they see the motion in their X-ray. Anecdotal feedback suggests that patients who see a dynamic X-ray are more likely to understand their condition and adhere to their rehabilitation plan.

Adding movement gives new insight, which makes it easier for clinicians to adopt technology and make it useful. There is clinical value, economic value, technological value, and patient appeal. This is a technology worth exploring.

Dark-field X-rays. Utilized by a team of researchers at the Technical University of Munich, dark-field X-ray is a novel X-ray method initially designed for early detection regarding respiratory diagnostics. This dynamic discovery centers around a unique phenomenon in relation to visible light and the principle of dark-field microscopy.

Where conventional X-ray images (generated by the depletion of the X-rays as they pass through the body) are unable to display detailed discrepancies between healthy and diseased tissue, dark-field X-ray images (leveraging the inherent wave characteristic of X-ray light) offer a comprehensive visualization of object structures that otherwise would be invisible or transparent in conventional X-ray imaging.

Equipped with dark-field X-ray images outlining such fine detail, clinicians are able to fast-track their determination and diagnostic processes, resulting in remarkably improved turnaround times and quality of patient care.

The field of radiology is predicted to undergo significant change over the next 20 years. The speed at which technology is developing – driven in large part by AI, which has the potential to completely transform the healthcare sector – means that these developments will occur much more quickly than they did in the previous 20 years.

Professionals must consider the advantages and disadvantages of using these technologies. Additionally, the influence of genomics and interventional radiology is already reshaping the entire industry. Expect to see more medical advancements as well, which will enhance patient outcomes and healthcare provider effectiveness.

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