Future Trends in Medical Imaging - What to Expect in the Coming Decade

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Over the years, medical imaging has revolutionized the way doctors diagnose and treat illnesses. From the early days of X-rays and ultrasound to today's more advanced technologies like MRI, PET, and CT, medical imaging professionals have come a long way in their ability to peer inside the human body.??

Recent technological advances like artificial intelligence (AI), nanotechnology, and 3D printing have made it easier for radiologists to identify diseases earlier and with greater accuracy. And in this edition of The Imaging Review, we will explore these trends in detail.

What's Revolutionizing Medical Imaging??

Artificial Intelligence (AI)

Artificial intelligence is currently a popular topic in the medical field primarily for the purposes of improving patient outcomes and solving a wide range of problems. One area where AI technology is already making a big impact is medical imaging.??

By analyzing large amounts of data and enhancing algorithms, some AI software are able to help predict issues accurately and indicate potential problems for radiologists to focus on. Artificial intelligence can also detect details in medical imaging that may not be visible to the human eye, leading to faster and more precise diagnoses.??

According to an article published in Double Black Imaging, an example of this is ProFound AI, a cancer-detection and workflow solution that uses a specific algorithm in tomosynthesis to detect breast tissue abnormalities:?

• Saving radiologists' reading time by 52.7%,??
• Improving cancer detection rates by 8%, and??
• Reducing false positives by almost 7%?

Additionally, artificial intelligence is also being used to segment heart sections on MRI, identify abnormalities in the retina of the eye earlier than current testing, and more.?

Despite all these potential advantages and advances that AI is able provide to patients and doctors, there are also some challenges that need to be addressed before fully incorporating this technology in hospitals and clinics en masse.?

The Challenges of AI?

In a study published in Nature Medicine, researchers at the University of Maryland School of Medicine (UMSOM) have discovered that most artificial intelligence algorithms created from medical images lack patient demographics and do not check for biases.??

The researchers pointed out that there is already some partiality in American medicine towards certain groups and that even small biases in individual datasets could be amplified greatly when combining hundreds or thousands of these datasets in AI algorithms.?

“These deep learning models can diagnose things physicians can’t see, such as when a person might die or detect Alzheimer’s disease seven years earlier than our known tests — superhuman tasks,” said the senior investigator.

Paul Yi , MD, senior investigator and assistant Professor of Diagnostic Radiology and Nuclear Medicine at UMSOM, and Director of University of Maryland Medical Intelligent Imaging (UM2ii) Center, explained that “because these AI machine learning techniques are so good at finding needles in a haystack, they can also define sex, gender, and age, which means these models can then use those features to make biased decisions.”?

Similarly, lead author and Program Coordinator at the UM2ii Center, Sean Garin , added “we hope that by bringing awareness to this issue in these data competitions – and if applied in an appropriate way – that there is tremendous potential to solve these biases.”?

In a different article published in the British Journal of Radiology, the author highlighted that artificial intelligence has a lack of high-quality, longitudinal data with outcomes. The different imaging settings and protocols used in different clinical settings make it difficult to create a single, effective AI algorithm.?

In addition, the vast number of potential clinical scenarios and tasks associated with medical imaging makes it challenging to organize the data generated from different practices in a standardized way.?

Patient privacy is another concern, with hospitals tightening security and data sharing policies to prevent healthcare data breaches and security attacks. However, successful implementation of AI requires large amounts of data from multiple institutions, creating a challenge for sharing images while maintaining security.?

Nanotechnology?

Nanoparticles are increasingly used as contrast agents in molecular imaging due to their ability to generate contrast, integrate multiple properties, have lengthy circulation times, and include high payloads.??

According to an article published in Arteriosclerosis, Thrombosis, and Vascular Biology, they also allow for easy assembly of molecular imaging contrast agents in an efficient ratio, leading to exciting results such as detection by multiple imaging techniques, therapeutic delivery, and detection of specific cell types.?

However, designing effective nanoparticle contrast agents requires careful consideration of the required properties for each application, followed by identification of candidate platforms and optimization of particle synthesis to include appropriate contrast/therapeutics, surface coating, targeting properties, defined size, and high biocompatibility.?

Recent advances in nanoparticle chemistry have led to sophisticated contrast agents, including macrophage targeted quantum dots and αvβ3-targeted microemulsions that also suppress angiogenesis. These agents provide greater knowledge of disease processes and therapy effects but may also produce significant toxicity that needs to be minimized for clinical use.?

3D Printing?

An article published in the Journal of Medical Radiation Sciences explains that 3D printing is commonly used in medical imaging to create anatomical models of the human body, which can improve visualization of lesions, surgical planning, and communication with patients.?

However, the effectiveness of these 3D-printed models depends on the availability of sufficient information from volumetric image data sets. According to an issue in Current Problems in Diagnostic Radiology, DICOM is used to gather data from CT, MRI, and ultrasound for 3D printed medical models.??

Segmentation and mesh generation tools are employed to process the data images before converting them into a standard tessellation language (STL) file for printing. Various 3D printing technologies such as inkjet, fused-deposition modeling, selective laser sintering, and stereolithography are now being used.?

These models have proved useful in preoperative planning of complex surgeries, custom prosthesis creation, and physician education and training. The application of medical imaging and 3D printers has proven effective in resolving many challenging medical issues, with the potential for continued expansion as technology advances.?

Overall, these advances in medical imaging technology have brought significant changes to the healthcare field, improving patient care and enhancing workflow for practitioners.?

Without a doubt, the technological trends we discussed above are shaping the future of medicine and improving patient outcomes. As technology continues to advance, we can expect to see more innovative solutions to complex medical problems.?