Tag: imaging AI

AI Speeds Up MRI Scans

Posted on by Brian Casey

In our last issue, we reported on a new study underscoring the positive return on investment when deploying radiology AI at the hospital level. This week, we’re bringing you additional research that confirms AI’s economic value, this time when used to speed up MRI data reconstruction. 

While AI for medical image analysis has garnered the lion’s share of attention, AI algorithms are also being developed for behind-the-scenes applications like facilitating staff workflow or reconstructing image data. 

  • For example, software developers have created solutions that enable scans to be acquired faster and with less input data (such as radiation dose) and then upscaled to resemble full-resolution images. 

In the new study in European Journal of Radiology, researchers from Finland focused on whether accelerated data reconstruction could help their hospital avoid the need to buy a new MRI scanner. 

  • Six MRI scanners currently serve their hospital, but the radiology department will be losing access to one of them by the end of the year, leaving them with five. 

They calculated that a 20% increase in capacity per remaining scanner could help them achieve the same MRI throughput at a lower cost; to test that hypothesis they evaluated Siemens Healthineers’ Deep Resolve Boost algorithm. 

  • Deep Resolve Boost uses raw-data-to-image deep learning reconstruction to denoise images and enable rapid acceleration of scan times; a total knee MRI exam can be performed in just two minutes. 

Deep Resolve Boost was applied to 3T MRI scans of 78 patients acquired in fall of 2023, with the researchers finding that deep learning reconstruction… 

  • Reduced annual exam costs by 399k euros compared to acquiring a new scanner
  • Enabled an overall increase in scanner capacity of 20-32%
  • Had an acquisition cost 10% of the price of a new MRI scanner, leading to a cost reduction of 19 euros per scan
  • Was a lower-cost option than operating five scanners and adding a Saturday shift

The Takeaway

As with last week’s study, the new research demonstrates that AI’s real value comes from helping radiologists work more efficiently and do more with less, rather than from direct reimbursement for AI use. It’s the same argument that was made to promote the adoption of PACS some 30 years ago – and we all know how that turned out.

Study Shows AI’s Economic Value

Posted on by Brian Casey

One of the biggest criticisms of AI for radiology is that it hasn’t demonstrated its return on investment. Well, a new study in JACR tackles that argument head on, demonstrating AI’s ability to both improve radiologist efficiency and also drive new revenues for imaging facilities. 

AI adoption into radiology workflow on a broad scale will require significant investment, both in financial cost and IT resources. 

  • So far, there have been few studies showing that imaging facilities will get a payback for these investments, especially as Medicare and private insurance reimbursement for AI under CPT codes is limited to fewer than 20 algorithms. 

The new paper analyzes the use of an ROI calculator developed for Bayer’s Calantic platform, a centralized architecture for radiology AI integration and deployment. 

  • The calculator provides an estimate of AI’s value to an enterprise – such as by generating downstream procedures – by comparing workflow without AI to a scenario in which AI is integrated into operations.

The study included inputs for 14 AI algorithms covering thoracic and neurology applications on the Calantic platform, with researchers finding that over five years … 

  • The use of AI generated $3.6M in revenue versus $1.8M in costs, representing payback of $4.51 for every $1 invested
  • Use of the platform generated 1.5k additional diagnoses, resulting in more follow-up scans, hospitalizations, and downstream procedures
  • AI’s ROI jumped to 791% when radiologist time savings were considered
  • These time savings included a reduction of 15 eight-hour working days of waiting time, 78 days in triage time, 10 days in reading time, and 41 days in reporting time  

Although AI led to additional hospitalizations, it’s possible that length of stay was shorter: for example, reprioritization of stroke cases resulted in 264 fewer hospital days for patients with intracerebral hemorrhage. 

  • Executives with Bayer told The Imaging Wire that while the calculator is not publicly available, the company does use it in consultations with health systems about new AI deployments. 

The Takeaway

This study suggests that examining AI through the lens of direct reimbursement for AI-aided imaging services might not be the right way to assess the technology’s real economic value. Although it won’t settle the debate over AI’s economic benefits, the research is a step in the right direction.

Real-World AI Experiences

Posted on by Brian Casey

Clinical studies showing that AI helps radiologists interpret medical images are great, but how well does AI work in the real world – and what do radiologists think about it? These questions are addressed in a new study in Applied Ergonomics that takes a deep dive into the real-world implementation of a commercially available AI algorithm at a German hospital. 

A slew of clinical studies supporting AI were published in 2023, from the MASAI study on AI for breast screening to smaller studies on applications like opportunistic screening or predicting who should get lung cancer screening. 

  • But even an AI algorithm with the best clinical evidence behind it could fall flat if it’s difficult to use and doesn’t integrate well with existing radiology workflow.

To gain insight into this issue, the new study tracked University Hospital Bonn’s implementation of Quantib’s Prostate software for interpreting and documenting prostate MRI scans (Quantib was acquired by RadNet in January 2022). 

  • Researchers described the solution as providing partial automation of prostate MRI workflow, such as helping segment the prostate, generating heat maps of areas of interest, and automatically producing patient reports based on lesions it identifies. 

Prostate was installed at the hospital in the spring of 2022, with nine radiology residents and three attending physicians interviewed before and after implementation, finding…

  • All but one radiologist had a positive attitude toward AI before implementation and one was undecided 
  • After implementation, seven said their attitudes were unchanged, one was disappointed, and one saw their opinion shift positively
  • Use of the AI was inconsistent, with radiologists adopting different workflows and some using it all the time with others only using it occasionally
  • Major concerns cited included workflow delays due to AI use, additional steps required such as sending images to a server, and unstable performance

The findings prompted the researchers to conclude that AI is likely to be implemented and used in the real world differently than in clinical trials. Radiologists should be included in AI algorithm development to provide insights into workflow where the tools will be used.

The Takeaway

The new study is unique in that – rather than focusing on AI algorithm performance – it concentrated on the experiences of radiologists using the software and how they changed following implementation. Such studies can be illuminating as AI developers seek broader clinical use of their tools. 

AI Models Go Head-to-Head in Project AIR Study

Posted on by Brian Casey

One of the biggest challenges in assessing the performance of different AI algorithms is the varying conditions under which AI research studies are conducted. A new study from the Netherlands published this week in Radiology aims to correct that by testing a variety of AI algorithms head-to-head under similar conditions. 

There are over 200 AI algorithms on the European market (and even more in the US), many of which address the same clinical condition. 

  • Therefore, hospitals looking to acquire AI can find it difficult to assess the diagnostic performance of different models. 

The Project AIR initiative was launched to fill the gap in accurate assessment of AI algorithms by creating a Consumer Reports-style testing environment that’s consistent and transparent.

  • Project AIR researchers have assembled a validated database of medical images for different clinical applications, against which multiple AI algorithms can be tested; to ensure generalizability, images have come from different institutions and were acquired on equipment from different vendors. 

In the first test of the Project AIR concept, a team led by Kicky van Leeuwen of Radboud University Medical Centre in the Netherlands invited AI developers to participate, with nine products from eight vendors validated from June 2022 to January 2023: two models for bone age prediction and seven algorithms for lung nodule assessment (one vendor participated in both tests). Results included:

  • For bone age analysis, both of the tested algorithms (Visiana and Vuno) showed “excellent correlation” with the reference standard, with an r correlation coefficient of 0.987-0.989 (1 = perfect agreement)
  • For lung nodule analysis, there was a wider spread in AUC between the algorithms and human readers, with humans posting a mean AUC of 0.81
  • Researchers found superior performance for Annalise.ai (0.90), Lunit (0.93), Milvue (0.86), and Oxipit (0.88)

What’s next on Project AIR’s testing agenda? Van Leeuwen told The Imaging Wire that the next study will involve fracture detection. Meanwhile, interested parties can follow along on leaderboards for both bone age and lung nodule use cases. 

The Takeaway

Head-to-head studies like the one conducted by Project AIR may make many AI developers squirm (several that were invited declined to participate), but they are a necessary step toward building clinician confidence in the performance of AI algorithms that needs to take place to support the widespread adoption of AI. 

Top 12 Radiology Trends for 2024

Posted on by Brian Casey

What will be the top radiology trends for 2024? We talked to key opinion leaders across the medical imaging spectrum to get their opinions on the technologies, clinical applications, and regulatory developments that will shape the specialty for the next 12 months.

AI – Generative AI to Reduce Radiology’s Workload: “New generative AI methods will summarize complex medical records, draft radiology reports from images, and explain radiology reports to patients using language they understand. These innovative systems will reduce our workload and will provide more time for us to connect with our colleagues and our patients.” — Curtis Langlotz, MD, PhD, Stanford University and president, RSNA 2024

AI – Generative AI Will Get Multimodal: “In 2024, we can expect continued innovations in generative AI with a greater emphasis on integrating GenAI into existing and new radiology and patient-facing applications with growing interests in retrieval-augmented generation, fine-tuning, smaller models, multi-model routing, and AI assistants. Medicine being multimodal, the term ‘multimodal’ will become more ubiquitous.” — Woojin Kim, MD, CMIO at Rad AI

AI – Will AI Really Reduce Radiology Burnout? “Burnout will continue to be a huge issue in radiology with no solution in sight. AI vendors will offer algorithms as solutions to burnout with catchy slogans such as ‘buy our lung nodule detector and become the radiologist your parents wanted you to be.’ Their enthusiasm will cause even more burnout.” — Saurabh Jha, MBBS, AKA RogueRad, Hospital of the University of Pennsylvania

Breast Imaging – Prepare Now for Density Reporting: “The FDA ‘dense breast’ reporting standard to patients becomes effective on September 10, 2024, and breast imaging centers should be prepared for new patient questions and conversations. A plan for a consistent approach to recommending supplemental screening and facilitating ordering of additional imaging from referring providers should be put into action.” — JoAnn Pushkin, executive director, DenseBreast-info.org

Breast Imaging – Density Reporting to Spur Earlier Detection: “In March 2023, FDA issued a national requirement for reporting breast density to patients and referring providers after mammography. Facilities performing mammograms must meet the September 2024 deadline incorporating breast density type and associated breast cancer risk in their reporting. This change can lead to earlier breast cancer detection as these patients will be informed of supplemental screening as it relates to their breast density and [will] choose to pursue it.” — Stamatia Destounis, MD, Elizabeth Wende Breast Care and chair, ACR Breast Imaging Commission

CT – Lung Cancer Screening to Build Momentum: “Uptake of LDCT screening for lung cancer will increase in the US and worldwide. AI-enabled cardiac evaluation, even on non-gated scans, will allow for prediction of illnesses such as AFib and heart failure.  Quantifying measurement error across platforms will become an important aspect of nodule management.” — David Yankelevitz, MD, Icahn School of Medicine at Mount Sinai Health System

CT – Photon-Counting CT to Expand: “In 2024, we will continue to see many papers published on photon-counting CT, strengthening the body of scientific evidence as to its many strengths. Results from clinical trials involving multiple manufacturers’ systems will also increase in number, perhaps leading to more commercial systems entering the market.” — Cynthia McCollough, PhD, director, CT Clinical Innovation Center, Mayo Clinic

Enterprise Imaging – Time is Ripe for Cloud and AI: “Healthcare has an opportunity for change in 2024, and imaging is ripe for disruption, with burnout, staffing challenges, and new technology needs. Many organizations are expanding their enterprise imaging strategy and are asking how and where they can take the plunge into cloud and AI. Vendors have got the message; now it’s time to push the gas and deliver.” — Monique Rasband, VP of strategy & research, imaging/oncology at KLAS

Imaging IT – Data Brokerage to Go Mainstream: “A new market will hit the mainstream in 2024 – radiology data brokerage. As data-hungry LLMs scale up and the use of companion diagnostics in lifesciences proliferates, health systems will look to cash in on curated radiology data. This will also be an even bigger driver for migration to cloud-based imaging IT.” — Steve Holloway, managing director, Signify Research     

MRI – Prostate MRI to Reduce Biopsies: “Prostate MRI in conjunction with PSMA PET will explode in 2024 and reduce the number of unnecessary biopsies for patients.” — Stephen Pomeranz, MD, CEO of ProScan Imaging and chair, Naples Florida Community Hospital Network 

Theranostics – New Radiotracers to Drive Diagnosis & Treatment: “Through 2024, nuclear medicine theranostics will increasingly be integrated into standard global practice. With many new radiopharmaceuticals in development, theranostics promise early diagnosis and precision treatment for a broadening range of cancers, expanding options for patients resistant to traditional therapies. Treatments will be enhanced by personalized dosimetry, artificial intelligence, and combination therapies.” — Helen Nadel, MD, Stanford University and president, SNMMI 2023-2024

Radiology Operations – Reimbursement Challenges Continue: “In 2024, we will continue to experience recruitment challenges coupled with decreases in reimbursement. Now, more than ever, every radiologist needs to be diligent in advocating for the specialty, focus on business plan diversification, and ensure all services rendered are optimally documented and billed.” — Rebecca Farrington, chief revenue officer, Healthcare Administrative Partners 

The Takeaway
To paraphrase Robert F. Kennedy, radiology is indeed living in interesting times – times of “danger and uncertainty,” but also times of unprecedented creativity and innovation. In 2024, radiology will get a much better glimpse of where these trends are taking us.

Lunit’s Deal for Volpara and AI Consolidation

Posted on by Brian Casey

Is the long-awaited consolidation of the healthcare AI sector gaining steam? In a deal valued at close to $200M, South Korean AI developer Lunit announced a bid to acquire Volpara Health, a developer of software for calculating breast density and cancer risk. 

At first glance, the alliance seems to be a match made in heaven. Lunit is a well-regarded AI developer that has seen impressive results in clinical trials of its Insight family of algorithms for indications ranging from mammography to chest imaging. 

  • Most recently, Lunit received FDA clearance for its Insight DBT software, marking its entry into the US breast screening market, and it also raised $150M in a public stock offering. 

Volpara has a long pedigree as a developer of breast imaging software, although it has shied away from image analysis applications to instead focus on breast center operations and risk assessment, in particular by calculating breast density. 

  • Thus, combining Lunit’s concentration in image analysis with Volpara’s focus on operations and risk assessment enables the combined company to offer a wider breadth of products to breast centers.

Lunit will also be able to take advantage of the marketing and sales structure that Volpara has built in the US mammography sector (97% of Volpara’s sales come from the US, where it has an installed base of 2k sites). Volpara expects 2024 sales of $30M and is cash-flow positive.

The question is whether the acquisition is a sign of things to come in the AI market. 

  • As commercial AI sales have been slow to develop, AI firms have largely funded their operations through venture capital firms – which are notoriously impatient in their quest for returns.

In fact, observers at the recent RSNA 2023 meeting noted that there were very few new start-up entrants into the AI space, and many AI vendors had smaller booths. 

  • And previous research has documented a slowdown in VC funding for AI developers that is prompting start-up firms to seek partners to provide more comprehensive offerings while also focusing on developing a road to profitability. 

The Takeaway

It’s not clear yet whether the Lunit/Volpara deal is a one-off combination or the start of a renewed consolidation trend in healthcare AI. Regardless of what happens, this alliance unites two of the stronger players in the field and has exciting potential for the years to come. 

AI’s Impact on Breast Screening

Posted on by Brian Casey

One of the most exciting radiology use cases for AI is in breast screening. At last week’s RSNA 2023 show, a paper highlighted the technology’s potential for helping breast imagers focus on cases more likely to have cancer.

Looking for cancers on screening mammography has been compared to finding a needle in a haystack, and as such it’s considered to be one of the areas where AI can best help. 

  • One of the earliest use cases was in identifying suspicious breast lesions during radiologist interpretation (remember computer-aided detection?), but more recently researchers have focused on using AI as a triage tool, by identifying cases most likely to be normal that could be removed from the radiologist’s urgent worklist. Studies have found that 30-40% of breast screening cases could be read by AI alone or triaged to a low-suspicion list.

But what impact would AI-based breast screening triage have on radiologist metrics such as recall rate? 

  • To answer this question, researchers from NYU Langone Health prospectively tested their homegrown AI algorithm for analyzing DBT screening cases.

The algorithm was trained to identify extremely low-risk cases that could be triaged from the worklist while more complex cases where the AI was uncertain were sent to radiologists, who knew in advance the cases they were reading were more complicated. In 11.7k screening mammograms, researchers examined recall rates over two periods, one before AI triage and one after, finding: 

  • The overall recall rate went from 13% before the triage period to 15% after 
  • Recall rates for complex cases went from 17% to 20%
  • Recall rates for extremely low-risk studies went from 6% to 5%
  • There were no statistically significant differences in any of the comparisons
  • No change in median self-reported perceived difficulty of reading from the triage lists compared to non-triage list, regardless of years of experience

In future work, the NYU Langone researchers will continue their study to look at AI’s impact on cancer detection rate, biopsy rate, positive predictive value, and other metrics.

The Takeaway

The NYU Langone study puts a US spin on research like MASAI from Sweden, in which AI was able to reduce radiologists’ breast screening workload by 44%. Given the differences in screening protocols between the US and Europe, it’s important to assess how AI affects workload between the regions.

Further work is needed in this ongoing study, but early results indicate that AI can triage complex cases without having an undue impact on recall rate or self-perceived difficulty in interpreting exams – a surrogate measure for burnout.

RSNA 2023 Video Highlights

Posted on by Brian Casey

That’s a wrap! 

RSNA 2023 just concluded, and by most accounts it was a successful conference. Preliminary figures indicate that attendance was up 11% over 2022. While short of the glory days of RSNA, the numbers indicate that the meeting’s recovery from the COVID-19 pandemic will be slow but steady.

As expected, AI was a dominant theme at McCormick Place, and that’s reflected in our video coverage of the technical exhibit floor. AI busted out of the AI Showcase to permeate both exhibit halls, a sign of the technology’s growing influence on radiology.

We profiled many of the most intriguing companies that were exhibiting at RSNA 2023 – some of them dominant players in the field while others are new entries looking to secure a foothold. 

We hope you enjoy watching our coverage as much as we enjoyed producing it! Check out the links below or visit the Shows page on our website.

AI’s Incremental Revolution

Posted on by Brian Casey

So AI dominated the discussion at last week’s RSNA 2023 meeting. But does that mean it’s finally on the path to widespread clinical use? 

Maybe not so much. For a technology that’s supposed to have a revolutionary impact on medicine, AI is taking a frustratingly long time to arrive. 

Indeed, there was plenty of skepticism about AI in the halls of McCormick Place last week. (For two interesting looks at AI at RSNA 2023, also see Hugh Harvey, MD’s list of takeaways in a post on X/Twitter and Herman Oosterwijk’s post on LinkedIn.) 

But as one executive we talked to pointed out, AI’s advance to routine clinical use in radiology is likely to be more incremental than all at once. 

  • And from that perspective, last week’s RSNA meeting was undoubtedly positive for AI. Scientific sessions were full of talks on practical clinical applications of AI, from breast AI to CT lung screening. 

Researchers also discussed the use of AI apart from image interpretation, with generative AI and large language models taking on tasks from answering patient questions about their reports to helping radiologists with dictation.

It’s fine to be a skeptic (especially when it comes to things you hear at RSNA), but for perspective look at many of the past arguments casting doubt on AI: 

  • AI algorithms don’t have FDA clearance (the FDA authorized 171 algorithms in just the past year)
  • You can’t get paid for using AI clinically (16 algorithms have CPT codes, with more on the way) 
  • There isn’t enough clinical evidence backing the use of AI (tell that to the authors of MASAI, PERFORMS, and a number of other recent studies with positive findings)
  • The AI market is overcrowded with companies and ripe for consolidation (what exciting new growth market isn’t?)

The Takeaway

Sure, it’s taking longer than expected for AI to take hold in radiology. But last week’s conference showed that AI’s incremental revolution is not only advancing but expanding in ways no one expected when IBM Watson was unveiled to an RSNA audience a mere 6-7 years ago. One can only imagine what the field will look like at RSNA 2030.

Looking for more coverage of RSNA 2023? Be sure to check out our videos from the technical exhibit floor, which you can find on our new Shows page.

AI Dominates at RSNA 2023

Posted on by Brian Casey

Take a deep breath. You survived another RSNA conference.

While a few hardy souls are still enjoying educational sessions in the cozy confines of McCormick Place, the final day of the exhibit floor yesterday marks the end of RSNA 2023 for most attendees. And what a show it was. 

Predictions were that AI would dominate the scientific sessions at RSNA 2023, a forecast that largely panned out. A November 28 session was a case in point, in which a series of top-quality papers were presented on one of the most promising use cases of AI, for breast screening:

  • A homegrown AI algorithm that analyzed screening breast ultrasound exams in addition to FFDM and DBT mammograms boosted sensitivity for detecting cancer in 12.5k patients, with better sensitivity for women with dense breasts (71% vs. 60%) and non-dense breasts (79% vs. 63%)
  • AI did a good job of detecting breast arterial calcification (BAC) when used prospectively to analyze screening mammograms in 16k women across 15 sites.  It found 15% of women had BAC, a possible marker for atherosclerotic disease
  • Swedish researchers used their VAI-B validation platform to compare three AI algorithms (Therapixel, Lunit, and Vara) in 34k women, finding that using AI with a single radiologist boosted sensitivity 10-30% compared to double reading, with a slight loss in specificity (2-7%). VAI-B could be used to validate AI implementation and guide purchasing decisions
  • Why does AI miss some breast cancers? South Korean researchers addressed this question by analyzing 1.1k patients with invasive cancers in which AI had a miss rate of 14%. Luminal cancers were missed most often
  • Adding AI analysis of prior images to current studies with FFDM and DBT boosted sensitivity for cancer detection in 30k patients, with sensitivity the highest for two years of priors compared to no priors (74% vs. 70%)

The Takeaway

This week’s research points to an exciting near-term future in which AI will help make mammography screening more accurate while helping breast radiologists perform their jobs more efficiently. Landmark studies toward this end were published in 2023 – this week’s RSNA conference shows that we can expect the momentum to continue in 2024. 

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