Tag Archives: AI

Exploring the Future of Ultrasound: 5 Trends to Watch

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Ultrasound technology has come a long way since its inception and continues to evolve at a rapid pace. As we look ahead to the near future, it’s clear that ultrasound will play an even more vital role in healthcare. In this blog post, we’ll explore 5 trends (in no particular order) that are set to shape the field of ultrasound in the coming years.

1. Portable and Handheld Ultrasound Devices

The trend of portable and handheld ultrasound devices is on the rise. In the past, ultrasound machines were hundreds of pounds, carted around on wheels, and costly to manufacture. These new, compact, and lightweight devices offer healthcare professionals the convenience of conducting ultrasound examinations at the patient’s bedside, in remote areas, or during emergency situations, and wearable devices will become part of the ultrasound tool kit. Their affordability and ease of use make them accessible to a broader range of healthcare providers, expanding the potential applications of ultrasound. I predict that, under a doctor’s care and orders, the ways in which ultrasound is used will expand!

2. Artificial Intelligence (AI) Integration

AI is revolutionizing the field of medical imaging, and ultrasound is no exception; however, sonographers and doctors are not going anywhere. AI algorithms can assist in image analysis, automate measurements, enhance quantitative imaging, and aid in the detection of abnormalities. In the near future, we can anticipate more sophisticated AI integration into ultrasound systems, which will not only enhance diagnostic accuracy but also improve workflow efficiency. AI will play a significant role in making ultrasound more accessible and reliable in terms of scanning, reading images, and delivering accurate results.

3. 3D and 4D Imaging

Three-dimensional (3D) and real-time 3D (4D) ultrasound imaging will continue to advance, providing clinicians with more detailed and interactive views of anatomical structures. This trend will be particularly valuable in obstetrics for capturing fetal development and in various other medical specialties where enhanced visualization and quantification are crucial. Expect to see more applications for complex anatomical assessments and dynamic studies.

4. Point-of-Care Ultrasound (POCUS)

Point-of-care ultrasound, or POCUS, is transforming the way medical professionals diagnose and manage patients. POCUS is expected to see increased adoption in various clinical settings, including emergency medicine, anesthesiology, primary care, and critical care. As training programs expand, more healthcare providers will be equipped to use POCUS for rapid and accurate assessments, which can lead to improved patient care and outcomes on the spot. With increased adoption, interest in ultrasound practice accreditation in this area is rising.

5. Therapeutic Ultrasound Applications

Beyond its diagnostic role, ultrasound is making great advances in therapeutic applications. Techniques like High-Intensity Focused Ultrasound (HIFU) are being employed for noninvasive surgeries, cancer treatments, and targeted drug delivery. In the coming years, we can expect to see further developments in therapeutic ultrasound, offering less invasive treatment options for a wide range of medical conditions and increasing the potential for ultrasound theranostics.

The future of ultrasound is incredibly promising with these 5 trends at the forefront of its evolution. From portable devices and AI integration to advanced imaging techniques and expanding applications in point-of-care and therapeutics, ultrasound is set to become even more integral to modern healthcare. Stay tuned as these trends continue to shape the landscape of medical imaging and patient care. We’re excited to witness the many possibilities that lie ahead for this versatile technology.

Therese Cooper, BS, RDMS, is a sonographer and the Director of Accreditation at the American Institute of Ultrasound in Medicine.

Using AI and Ultrasound to Diagnose COVID-19 Faster

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Coronavirus disease 2019 (COVID-19) is a newly identified virus that has caused a recent outbreak of respiratory illnesses starting from an isolated event to a global pandemic. As of July 2020, there are over 2.8 million confirmed COVID-19 cases in the U.S. and over 11.4 million worldwide. In the United States alone, over 130,000 Americans have died from COVID-19, with no end in sight. A major cause of this rapid and seemingly endless expansion can be traced back to the inefficiency and shortage of testing kits that offer accurate results in a timely manner. The lack of optimized tools necessary for rapid mass testing produces a ripple effect that includes the health of your loved ones, jobs, education, and on the national level, a country’s Gross Domestic Product (GDP), but artificial intelligence and ultrasound may help.

STATE OF ART IN DIAGNOSIS

Prof. Alper Yilmaz, PhDCurrently, there are two types of tests that are conducted by healthcare professionals–diagnostic tests and antibody tests. The diagnostic test, as the name implies, helps diagnose an active coronavirus infection in a patient. The ideal diagnostic test and the “gold standard” according to the United States Center for Disease Control (CDC) is the Reverse Transcription Polymerase Chain Reaction, or simply, RT-PCR. RT-PCR is a molecular test not only capable of diagnosing an active coronavirus infection, but it can also indicate whether the patient has ever had COVID-19 or were infected with the coronavirus in the past. However, the time required to conduct the test limits its effectiveness when mass deployed.

A much faster but less reliable diagnostic test alternative to RT-PCR is an antigen test. Much like the gold standard, the antigen test is capable of detecting an active coronavirus infection in a much shorter timeframe. Although antigen tests produce rapid results, usually in about an hour, the results are deemed highly unreliable, especially with patients who were tested negative according to the US FDA.

In contrast, the antibody test is designed to search for antibodies produced by the immune system of a patient in response to the virus and is limited by its ability to only detect past infections, which is less than ideal to prevent an ongoing pandemic.

THE PROBLEM 

To combat the rapid expansion of an airborne virus such as COVID-19, or future variations of a similar virus, rapid and reliable solutions must be developed that aim at improving the limitations of current methods. Although highly accurate, methods such as RT-PCR do not meet the speed requirements needed for testing on a large scale. Depending on the location, diagnosis of an active coronavirus infection with RT-PCR may take anywhere between several hours and up to a week. When the number of daily human-to-human interactions are considered, the lack of speed in diagnosing an active coronavirus patient could be the difference between a pandemic or an isolated local event.

As an alternative to molecular tests, Computed Tomography (CT) scans of a patient’s chest have shown promising results in detecting an infection. However, in addition to not being recommended by the CDC to diagnose COVID-19 patients, there are many unwanted consequences with the use of CT scans. With CT scans used to diagnose multiple illnesses, some of which relate to serious emergencies such as brain hemorrhaging, they cannot be used as the primary tool for diagnosing COVID-19. This is especially true in rural areas where the healthcare infrastructure is underfunded. Mainly due to the required deep cleaning of the machine and room after each patient, which usually requires 60 to 120 minutes, many institutions are unable to provide CT scans as a viable primary diagnostic tool. Ultimately, given the need for CT scanners for several other health complications combined with limited patient capacity at each hospital, alternative methods must be developed to diagnose an active coronavirus patient.

THE SOLUTION 

Recently Point-of-Care (POC) devices have started to be adopted by many healthcare professionals due to its reliability and portability. An emerging popular technique, which adopts improvements made in mobile ultrasound technology, allows for healthcare professionals to conduct rapid screenings on a large scale.

Working since mid-March, when early cases of physicians adopting mobile ultrasound technology emerged, the research team at The Ohio State University, Dr. Alper Yilmaz and PhD student Shehan Perera, started developing a solution that can automate an already well-established process. Dr. Yilmaz is the director of the Photogrammetric Computer Vision lab at Ohio State. Dr. Yilmaz’s expertise in machine learning, artificial intelligence, and computer vision combined with the research experience of Shehan Perera laid a strong foundation to tackle the problem at hand. As it stands, the screening of a new patient, with the use of a mobile ultrasound device takes about 13 minutes, with the caveat that it requires a highly trained professional to interpret the results generated by the device. With the combination of deep learning and computer vision, the research team was able to use data generated from the ultrasound device to accurately identify COVID-19 cases. The current network architecture, which is the product of many iterations, is capable of detecting the presence of the virus in a patient with a high level of accuracy.

Many fields have been revolutionized with modern deep learning and computer vision technologies. With the methods developed by the research team, this technology can now allow any untrained worker to use a handheld ultrasound device, and still be able to provide a service that rivals that of a highly trained doctor. In addition to being extremely accurate, the automated detection and diagnosis process takes less than 10 minutes, which includes scanning time, and sanitation is as simple as removing a plastic seal that covers the device. The benefits of this technology can not only be useful for countries such as the United States, with a well-established healthcare system, but, more importantly, can significantly help countries and areas where medical expertise is rare.

CONCLUSION 

The United States healthcare system is among the best in the world, yet we are failing to provide the necessary treatment patients clearly need. The developments made in artificial intelligence, deep learning, and computer vision offer proven benefits, which can not only be leveraged to improve the current state of the global pandemic but can lay the foundation to prevent the next. Alternative testing methods such as mobile ultrasound devices combined with novel artificial intelligence algorithms that allow for mass production, distribution, and testing could be the innovation that could help decelerate the spread of the virus, reducing the strain on the global healthcare infrastructure.

Feel Free to Reach the Authors at: 

Photogrammetric Computer Vision Lab – https://pcvlab.engineering.osu.edu/
Dr. Alper Yilmaz, PhD
Email: Yilmaz.15@osu.du
LinkedIn: https://www.linkedin.com/in/alper-yilmaz

Shehan Perera
Email: Perera.27@osu.edu
LinkedIn: https://www.linkedin.com/in/shehanp/

References 

https://www.fda.gov/consumers/consumer-updates/coronavirus-testing-basics

https://www.whitehouse.gov/articles/depth-look-COVID-19s-early-effects-consumer-spending-gdp/#:~:text=BEA%20estimates%20that%20real%20GDP,first%20decline%20in%20six%20years.&text=This%20drop%20in%20GDP%20serves,in%20response%20to%20COVID%2D19.

 

Interested in learning more about COVID-19 or AI? Check out the following posts from the Scan:

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The Excitement of New Ultrasound Technologies and Their Effects on Imaging-Guided Interventions

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Recent advancements in ultrasound technologies have generated excitement in the field of ultrasound-guided intervention. For me, an interventional radiologist, these developments create new potential to perform needed procedures and a complementary approach to addressing our patients’ complex medical conditions. Further, benefits from these technologies include enabling us to achieve better patient outcomes, improve patient satisfaction, gain operational efficiencies, and improve stake holder’s satisfaction.azar_nami

The new technologies to which I’m referring are ultrasound contrast and ultrasound fusion. Ultrasound fusion is an element of artificial intelligence that combines the anatomic details of cross-sectional imaging like CT scan, PET scan, and MRI with the power of real-time ultrasound and is gaining more acceptance and popularity in medicine. Similar to a car’s GPS, ultrasound fusion helps a user find something. The powerful tool enables the operator to find lesions, which normally are difficult or even impossible to find on standard ultrasound. Needle navigation in the form of virtual tracking is a bonus that identifies needle location even when it is obscured by air or bone. It’s also a great teaching tool for inexperienced physicians who are interested in interventional radiology.

Ultrasound contrast is also emerging as a powerful tool in the field of interventional radiology. It enables the operator to better visualize a lesion and characterize the lesion and surrounding tissue. Now, we also can perform an ultrasound contrast sinogram to assess any cavity or catheter location, which opens new horizons in the field of ultrasound intervention, mainly in pediatric intervention.

An additional benefit for ultrasound contrast that it can be given without worrying about renal injury. This is very valuable when it comes to avoiding the toxic effect of iodinated contrast, especially in renal transplant intervention. Also, its very sensitivity to assess bleeding when compared with that of Doppler ultrasound. This technology allows us to discharge our patients home earlier after procedures when the contrast study is negative.

This is a very exciting time in the field of interventional radiology (IR). So many procedures that we could not perform using real-time ultrasound in the past now can be safely done with only ultrasound. Our patients appreciate how convenient it is. The procedures are done quickly, without the need to move the patient from their bed onto a stiff CT scan table. The lack of ionizing radiation in IR is also an attractive concept to the patient (mainly pediatric and/or pregnant), the clinician, and our IR staff.

Our institution is very supportive of utilizing advanced ultrasound technologies, as ultrasound allows us to gain operational efficiencies and is a more cost-effective alternative to CT-guided procedures. Operational efficiencies are gained by doing interventional cases portably with ultrasound, thus allowing the interventional CT suite to be utilized for diagnostic exams, which bring additional revenue to the institution. The ordering clinicians are also cognizant of radiation dose reduction, so providing an alternative to CT-guided procedures appeals to them.

Even though the implementation of contrast-enhanced ultrasound and fusion has been slower in the United States when compared with our colleagues abroad, it has brought a lot of excitement to my colleagues and me in interventional radiology. Like any new technology, the more we use, the more we appreciate its value. I predict they will become the new norm in daily practice. These advancements will continue to evolve and be an essential part of medicine.

 

Interested in reading more about contrast ultrasound? Check out the following posts from the Scan:

 

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Nami Azar, MD, MBA, is an Associate Professor of Radiology in the Department of Radiology at University Hospitals of Cleveland Medical Center in Ohio.

Artificial Intelligence and Point-of-Care Ultrasound

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One of the greatest ongoing challenges of POCUS (point-of-care ultrasound) is educating existing physicians, residents, students and others. There are not even enough teachers to teach everyone who wants to learn. Clinicians would like to get the results from POCUS performed on their patients but have difficulty investing the effort required to learn, practice, and then become credentialed. Further complicating things for some is the dreaded self-doubting period, which could last months or years, where providers worry they may make a mistake and be ridiculed for it, or worse.Blaivas

One potential answer is thought to be artificial intelligence (AI); kind of like it seems to be for everything in medicine today. What good is AI in POCUS anyway? What if the education required was simply to find the correct spot on the body to apply the probe? Then the algorithm would do the rest and it would be more accurate than the best POCUS masters. Not only would training be truly minimized, maybe to minutes, but the examination would be shortened as well. A few sweeps through organs, whether it is the liver and gallbladder or the heart, may be enough for the AI algorithm to do its thing. This would mean all those busy clinicians really would get a great return on their time investment. If the algorithm is that accurate and expert, providers will not be questioned easily when they document an AI US finding.

AI is an inescapable topic of sensational news stories and movies alike. AI is simply a machine approximation of human-like intelligence in task performance. The type most associated with image interpretation is deep learning. How does it work? Programmers develop software architectures roughly resembling levels of neurons in the cerebral cortex, with multiple connections. The levels of neurons have specific functions and transmit messages to neurons in the next row via mathematical functions. They are also capable of sending messages in reverse as feedback. Such a deep network is often termed a convolutional neural network (CNN; or some variant on the name). It can learn to interpret images, whether CXR, head CT, or ultrasound, by scanning each image one tiny part at a time, then pooling all of the neuronal-like reactions to those tiny parts and coming up with an answer. Give it enough training data and such a CNN can become very accurate.

Well, imagine a CNN algorithm plugged into your favorite POCUS machine. The CNN is trained on the liver and gallbladder; it has seen millions of example images, both normal and abnormal. It can recognize liver anatomy and point it out for you, the same for every detail around the gallbladder and biliary tree. It’s great at identifying pathology and can make measurements in the correct spots for the wall, common bile duct (CBD), and more. Once again, who really cares? I spent 2 decades scanning the gallbladder, performing research studies, and publishing on it. Well, while it may not have been an issue for me, not everyone invests their free time like that. Yet, many would like to be able to put a probe on the abdomen, have the ultrasound machine tell them where to move it, point out pathology, and come up with a likely diagnosis. Did I mention it could happen in real time, at the patient’s bedside, while you are casually speaking to them? How useful would this be? It could substitute for years of training, maybe even over 2 decades worth. There are other subtle benefits too. Although some studies seem to show that CNN CT algorithms seem to catch so much pathology radiologists can miss, the individual CNN may not be as good at finding something a rare expert might pick up, at least for now. But the CNN never gets tired. It never gets hit with a massive wave of scans to read late at night or overwhelmed with clinicians calling to discuss imaging studies. Thus, even experts can benefit from such algorithms as an aid.

Not happy with the image quality due to patient body habitus or another factor? It turns out another algorithm can actually artificially improve the image clarity and quality, and do so accurately without introducing false data. This has not been introduced into clinical use of POCUS but is likely to be just around the corner. The key is to make sure nothing is invented by the algorithm that is not actually there.

Imagine incredible ultrasound expertise from a short exam that required minimal training to perform. This scenario will come, but not this year or the next. As some speakers and authors have noted, AI coupled with POCUS is a big step toward the fabled and elusive “tricorder” first depicted in the 1960s Star Trek television series. An incredible hand-held device (that does not even require body contact), which diagnoses maladies in a few short sweeps over the patient. The eventual outcome of approaching such a device is greatly increased speed, efficiency, confidence, and accuracy of patient assessment and diagnosis. The benefit of significantly decreased skill/training requirements will also pose some challenges for the workforce, but these are likely to appear gradually and may be hardly noted.

What about combining other data feeds along with the ultrasound images? AI algorithms are great at interpreting EKG tracings and even cardiac and lung auscultation. Studies analyzing digital auscultation signals using deep learning systems are able to diagnose many more abnormalities than humans are. The result could be synergistic and add redundancy in diagnosis, such as for abnormal lung or heart sounds during ultrasound evaluation. Maybe other signals could be incorporated also.

These algorithms just need data, lots of data, and that is the conundrum for people seeking to develop AI apps. What do you think about companies getting de-identified image data without provider and patient awareness? Do you think it would help you to have a smart machine that analyzed the images and made calculations within seconds? What about incorporating other diagnostic signals such as digital auscultation, EKG tracings, or maybe some other signal?

 

 

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Michael Blaivas, MD, MBA, FACEP, FAIUM, is an Affiliate Professor of Medicine in the Department of Medicine at the University of South Carolina, School of Medicine. He works in the Department of Emergency Medicine at St. Francis Hospital in Columbus, Georgia.