Imaging In 2021

Congrats on wrapping up a truly wild year for radiology and medical imaging, everyone. Here are some of the top storylines from the last 12 months that might explain why it felt more like 18 months.

Mid-COVID – This time last year radiology teams and vendors were preparing for a post-COVID future, but that obviously wasn’t what happened in 2021. Instead, they battled their way through a second pandemic year and accelerated some major imaging-related trends that might extend well into the future (cloud IT, portable imaging, remote reading, backlogs, burnout, tele/home care, and more).

Big Acquisitions – It might not seem like it, but 2021 included an unusually high number of industry-changing acquisitions. These acquisitions turned two imaging leaders into parts of much bigger non-imaging companies (Nuance & Microsoft; Change & UnitedHealthcare), transformed Intelerad into a top-tier PACS player (Ambra, Insignia, HeartIT, LUMEDX), created a pair of new public companies through SPAC mergers (Butterfly & Hyperfine), brought the first big AI acquisition (Zebra-Med & Nanox), gave Canon its own photon-counting detectors (Redlen), and added surgical ultrasound to GE’s portfolio (BK Medical). Of course, there were plenty of practice and imaging center acquisitions too.

AI Maturation – AI is still super young, but there were plenty of signs that it’s growing up fast. 2021 saw imaging AI make its way into far more clinical workflows and curriculums, created a wider divide between the AI leaders and the 2nd/3rd-tier players, and drove a lot more AI vendor consolidation than it might appear. 

Burnout – Burnout remained a dominant theme again this year, making workflow efficiency the top focus area for most radiology team leaders, product developers, and marketers. 

Developing World Imaging – The developing world’s lack of medical imaging is definitely not new, but it seems like imaging players started paying more attention to the half of the world that still doesn’t have enough imaging access. We saw a sustained focus on low/middle income countries from Hyperfine/Butterfly/Nanox/Qure.ai, new developing world strategies from Siemens and Fujifilm, and a major tuberculosis CXR AI endorsement from the World Health Organization.

Population Health Pivot – 2021 also brought a major increase in population health AI activity, including commercial launches from Nanox AI and Cleerly, an increased research focus from academia, and UCSF deploying an automated CAC scoring system for all chest CTs.

Detecting the Radiographically Occult

A new study published in European Heart Journal – Digital Health suggests that AI can detect aortic stenosis (AS) in chest X-rays, which would be a major breakthrough if confirmed, but will be met with plenty of skepticism until then.

The Models – The Japan-based research team trained/validated/tested three DL models using 10,433 CXRs from 5,638 patients (all from the same institution), using echocardiography assessments to label each image as AS-positive or AS-negative.

The Results – The best performing model detected AS-positive patients with an 0.83 AUC, while achieving 83% sensitivity, 69% specificity, 71% accuracy, and a 97% negative predictive value (but… a 23% PPV). Given the widespread use and availability of CXRs, these results were good enough for the authors to suggest that their DL model could be a valuable way to detect aortic stenosis.

The Response – The folks on radiology/AI Twitter found these results “hard to believe,” given that human rads can’t detect aortic stenosis in CXRs with much better accuracy than a coin flip, and considering that these models were only trained/validated/tested with internal data. The conversation also revealed a growing level of AI study fatigue that will likely become worse if journals don’t start enforcing higher research standards (e.g. external validation, mentioning confounding factors, addressing the 23% PPV, maybe adding an editorial).

The Takeaway – Twitter’s MDs and PhDs love to critique study methodology, but this thread was a particularly helpful reminder of what potential AI users are looking for in AI studies — especially studies that claim AI can detect a condition that’s barely detectable by human experts.

Trained to Underdiagnose

A new Nature study suggests that imaging AI models might underdiagnose patient populations who are also underdiagnosed in the real world, revealing new ethical and clinical challenges for AI development, regulation, and adoption.

The Study – The researchers trained four AI models to predict whether images would have positive diagnostic findings using three large/diverse public CXR datasets (one model w/ each dataset, one w/ combined dataset, 707k total images). They then analyzed model performance across various patient populations.

The Underdiagnosed – The AI models were mostly likely to underdiagnose patients who are female, young (0-20yrs), Hispanic and Black, and covered by Medicaid (low-income). AI underdiagnosis rates were even more extreme among patients who belonged to multiple underserved groups, such as Hispanic females or younger Black patients.

The Overdiagnosed – As you might expect, healthy patients who were incorrectly flagged by the AI models as unhealthy were usually male, older, White, and higher income.

The Clinical Impact – In clinical use, a model like this would result in traditionally underserved patients experiencing more missed diagnoses and delayed treatments, while traditionally advantaged patients might undergo more unnecessary tests and treatments. And we know from previous research that AI can independently detect patient race in scans (even if we don’t know why).

The Takeaway – AI developers have been working to reduce racial/social bias in their models by using diverse datasets, but it appears that they could be introducing more systemic biases in the process (or even amplifying them). These biases certainly aren’t AI developers’ fault, but they still add to the list of data source problems that developers will have to solve.

The State of AI

A group of radiology leaders starred in Canon Medical’s recent State of AI in Radiology Today Roundtable, sharing insights into how imaging AI is being used, where it’s needed most, and how AI might assume a core role in medical imaging.

The panelists were largely from the user/clinical side of imaging (U of Maryland’s Eliot Siegel, MD; UC Irvine’s Peter Chang, MD; UHS Delaware’s Cindy Siegel, CRA; U of Toronto’s Patrik Rogalla, MD; and Canon’s Director of Healthcare Economics Tom Szostak), with deeper AI experience than many typical radiology team members.

Here are some of the big takeaways:

We’re Still Early – The panel started by making sure everyone agrees on the definition of AI and much of ensuing discussions focused on AI’s future potential, which says a lot about where we are in AI’s lifecycle.

Do We Need AI? – The panelists agreed that radiology does indeed need AI, largely because it can improve the patient experience (shorter scans, faster results, fewer call-backs), help solve radiology’s inefficiency problems, and improve diagnostic accuracy.

Does AI Really Improve Efficiency? – Outside of image reconstruction, none of the panelists were ready to say that AI currently makes radiologists faster. However, they still believe that AI will improve future radiology workflows and outcomes.

Finding The Killer App – Things got a lot more theoretical at the halfway point, when the conversation shifted to what “killer apps” might bring imaging AI into mainstream use, including AI tools that:

  • Identify and exclude normal scans with extremely high accuracy (must be far more accurate than humans and limit false positives)
  • Curate and submit all CMS quality reporting metrics (eliminates admin work, generates revenue)
  • Identify early-stage diseases for population health programs (keeps current diagnostic workflows intact)
  • Interpret and diagnose all X-ray exams (eliminates high volume/repetitive exams, rads don’t read some XRs in many countries)
  • Improve image quality, allow faster scans, reduce dosage (aka DL image reconstruction)

AI’s Radiologist Impact – The panelists don’t see AI threatening radiologist jobs in the short to mid-term given AI’s current immaturity, the “tremendous inefficiencies” that still exist in radiology, and the pace of imaging volume growth. They also expect volume growth to drive longer term demand for both AI and rads, suggesting that AI adoption might even amplify future volume growth (if AI expands bandwidth and cuts interpretation costs, the laws of economics suggest that more scans would follow).

What AI Needs – With most of the technical parts of building algorithms now figured out, AI’s evolution will depend on getting enough training data, improving how AI is integrated into workflows, and making sure AI is solving radiology’s biggest problems. Imaging AI also needs healthcare to be open to change, which would require clear clinical, operational, and financial upsides.

One-Stop Cardiac CT

A new Radiology Journal study found that combining Triple-rule-out CT (TRO CT) with Late Contrast Enhancement CT (LCE CT) significantly improves acute chest pain diagnosis.

Background – It’s traditionally been challenging to diagnose patients with acute chest pain and mild troponin rise, as TRO CT is effective for several key diagnoses (coronary artery disease, acute aortic syndrome, pulmonary embolism) but can’t identify nonvascular causes of myocardial injury.

The Study – The researchers examined 84 troponin-positive patients with acute chest pain using TRO CT, and then performed LCE CT exams on the 42 patients who had negative/inconclusive results. 

The Results – The added LCE CT exams revealed positive/conclusive findings in 34 of the 42 previously-negative/inconclusive patients (including 22 w/ myocarditis), improving overall diagnostic rates from 50% to 90% (from 42/84 to 76/84).

The Takeaway – This new TRO CT + LCE CT protocol could make cardiac CT a “one-stop shop” for diagnosing acute chest pain, eliminating the need for follow-up MRI exams and allowing faster diagnoses. That’s especially notable considering that CT is already recommended for patients with low-risk acute chest pain (to exclude CAD) and was recently proposed as a gatekeeper for invasive coronary angiography.

Canon’s Big Virtual RSNA

Canon Medical was among the first companies to decide to virtually exhibit at RSNA 2021, but the OEM still had quite a presence, prominently placing Canon signage throughout the convention center and announcing a range of new products and technologies. 

Vantage Fortian – Canon expanded its open bore MRI lineup, launching the Vantage Fortian 1.5T system. The FDA-cleared MRI debuts with a range of productivity enhancements, including new patient monitoring and positioning tools and planning tools for liver, prostate, and whole spine imaging. The Vantage Fortian also adopts Canon’s prioritized AiCE deep learning image reconstruction technology.

MRI Solution Expansion – The Vantage Fortian’s new automation tools will soon expand to Canon’s Vantage Orian 1.5T MR and later go into the Vantage Galan 3T system (pending regulatory approval). Canon will also make Resoundant Inc.’s advanced Magnetic Resonance Elastography (MRE) technology available with its latest MRIs.

Premium Ultrasound Overhaul – Canon Medical introduced the Aplio i-series / Prism Edition, completely redesigning its premium ultrasound family. The Aplio i-series / Prism Edition ultrasounds launch with a new interface and ergonomics, higher processing power, added image enhancement applications (microvascular, ultra wide view), and new AI-based workflow automations.

Aquilion ONE / PRISM CT Enhancements – Canon continued to enhance its Aquilion ONE / PRISM Edition CT scanner, adding its new Precise IQ Engine (PIQE, a new DLIR solution for cardiac CT image enhancement) and SilverBeam X-ray filter (reduces lung cancer CT radiation dosage close to CXRs). These FDA-pending enhancements come one year after the Aquilion ONE / PRISM Edition added Deep Learning Spectral CT scanning (allowing one-beat cardiac scans).

Hi-Def Interventional Detector – Canon also launched a new 12×16 Hi-Def detector for its range of Alphenix interventional systems (Sky +, 4D CT with Sky +, Biplane, and Core+), joining its existing 12×12 detector. The hybrid detector has the highest resolution on the market (76 micron resolution, up to 6.6 lp/mm), while achieving 2-times greater spatial resolution than conventional flat panels.

Winners Announced for 2021 Imaging Wire Awards

The Imaging Wire is thrilled to announce the winners of the 2021 Imaging Wire Awards, honoring this year’s most outstanding contributors to radiology.

The following Imaging Wire Award winners were nominated by their peers and selected by a panel of judges for their efforts to evolve radiology and improve the lives of clinicians and patients:


COVID Hero: Lt Col Giovanni Lorenz, DO; Deputy Radiology Product Line Leader, San Antonio Military Health System (SAMHS)

Lt Col Dr. Giovanni Lorenz distinguished himself and the San Antonio Military Health System during the COVID pandemic, developing new remote diagnostic programs that improved SAMHS’ cardiac imaging operations, while also leading several National military COVID-19 working groups.


Diagnostic Humanitarian: Arlene Richardson, MD; Radiology Department Chair, Jackson Park Hospital

Dr. Richardson serves as Radiology Chair for Jackson Park Hospital and Medical Center, where she upholds equitable care for the hospital’s traditionally underserved patient population, and is deeply involved in RAD-AID International, where she currently serves as Director of RAD-AID Tanzania.


AI Activator: Greg Zaharchuk, MD, PhD; Professor of Radiology, Stanford University, Founder of Subtle Medical

In addition to his clinical and academic achievements at Stanford, the image enhancement solutions that Dr. Zaharchuk developed with Subtle Medical have significantly improved hospitals and imaging centers’ productivity and their patients’ experiences. 


Insights to Action: Richard Duszak, MD; Professor and Vice Chair of Radiology, Emory University

Dr. Duszak founded the Neiman Health Policy Institute and currently leads radiology health policy and practice efforts at both Emory University and the JACR, where he and colleagues have expanded awareness of imaging overuse and volume growth.


Burnout Fighter: Chris Mattern, MD; Practice President, Greensboro Radiology

Dr. Mattern created and leads many of Radiology Partners’ mental health and burnout prevention programs, combining education, mentorship, and communication to improve the organization’s cultural wellness.


Cornerstone: Elad Walach; CEO, Aidoc Medical

Elad Walach and his teams have developed seven FDA-cleared AI products and brought AI into clinical use across 600 global sites, improving medical imaging efficiencies and quality of care.


Tech Trailblazer: Sheela Agarwal, MBA, MD; Chief Medical Information Officer, Nuance Communications

Through her work with the ACR Data Science Institute and Nuance Communications, Dr. Agarwal has championed radiology professionals’ role in bringing AI into clinical care, while providing much needed tools and frameworks to support AI adoption.


The 2021 Imaging Wire Award judges include: Bill Algee of Columbus Regional Hospital, Dr. Jared D. Christensen of Duke University Health, Dr. Keith J. Dreyer of Mass General Brigham, Dr. Allan Hoffman of Commonwealth Radiology Associates, Dr. Saurabh Jha of Penn Medicine, Dr. Ryan K. Lee of Einstein Healthcare Network, Dr. Marla B.K. Sammer of Texas Children’s Hospital, and Dr. Irena Tocino of Yale New Haven Hospital.

Congratulations to this year’s Imaging Wire Award winners and nominees, who’s efforts to elevate radiology is truly inspiring. Also, thanks to this year’s amazing judges and everyone who nominated these very deserving imaging professionals!

RSNA 2021 Reflections

The first in-person RSNA since COVID is officially a wrap. Hope you had a blast if you made it to Chicago and a productive week if you stayed home. We also hope you enjoy The Imaging Wire’s big takeaways from what might have been both the most special and most subdued RSNA ever.

Crowds & Conversations – We were already expecting 50% lower attendance than RSNA 2019, but the exhibit hall and cab lines looked more like 70% below 2019’s crowds (even less on Sunday & Wednesday). That said, most of the stronger companies had steady booth traffic and nearly every exhibitor emphasized that the attendees who did show up were ready to have high-quality conversations.

Focus on Productivity – Just about every product message at RSNA focused on productivity and efficiency, often with greater emphasis than clinical effectiveness. The modality-based efficiency enhancements seemed to be the most impactful, which is good news for technologist bandwidth and patient throughput, but might be bad news for rad burnout unless informatics/AI efficiency can catch up (it doesn’t seem like that happened this year).

Modality Milestones – The major OEMs did a good job making modalities cool again, debuting milestone innovations across both their MR (low-helium, low-field, reconstruction, coils) and CT (photon-counting, spectral, upgradability) lineups. We also saw the latest scanners take big strides in operator efficiency and patient experience. There weren’t many breakthroughs with X-ray or ultrasound, and most point-of-care ultrasound OEMs stayed home (rads aren’t their market anyway), but attendees seemed okay with that.

AI Showcase – The RSNA AI Showcase had solid traffic and high energy (especially on Mon & Tues), helped by continued AI buzz and the fact that RSNA finally let AI vendors out of the basement. The AI Showcase highlighted many of the trends we’ve been seeing all year, including larger vendors transitioning to AI platform strategies, an increased focus on workflow integration and care coordination, and a greater emphasis on radiologist efficiency. There were also far fewer brand-new AI tools than previous years, as many vendors focused on improving their current products and/or expanding their portfolio via partnerships. 

PACS Cloud Focus – PACS vendors continued to place a major emphasis on their respective cloud advantages, and there was a widespread consensus that cloud is on every imaging IT roadmap. The PACS vendors seemed to talk less about multi-ology enterprise imaging than previous years, and expanding EI beyond radiology/cardiology still seemed pretty futuristic for most players. It was also quite clear that most of the PACS players’ AI marketplaces/platforms haven’t been as prioritized as earlier announcements might have suggested.

Best RSNA Since… 2019 – We’ve heard some folks saying this was the “best RSNA ever” because it was easy to get around and it was great to see everyone, but those seem more like pandemic silver linings than “best ever” qualifications. Still, the imaging industry made the most of RSNA 2021, and everyone seemed truly happy to be together again after two long years of working from home. As long as COVID cooperates, we should be set up for an excellent RSNA 2022.

GE’s Productive RSNA

GE Healthcare had another busy RSNA, highlighted by several major modality launches and an overarching focus on helping imaging teams be more productive. 

Return to MR Hardware – After focusing on AIR Recon DL during the last two RSNAs, GE Healthcare’s MR team made sure to roll-out new hardware at this year’s show. 

  • GE’s MR section was headlined by its new SIGNA Hero 3T MR, which brings a wide range of improvements (image quality, workflows, productivity, comfort, reconstruction, helium & energy), and a major focus on operator efficiency.  
  • GE also unveiled the SIGNA Artist Evo, which allows health systems / imaging centers to upgrade their existing 1.5T 60cm-bore MRs with 70cm bore systems (w/ AIR Recon DL & AIR Coils), without the construction and downtime typically required when upgrading to a net new MR system.

GE’s Scalable CT Platform – GE unveiled the unique Revolution Apex platform, which offers the modularity and scalability to cover a wide range of current and future needs, and represents GE’s biggest CT launch since 2014. 

  • The FDA-cleared Revolution Apex CT is available with multiple detector coverage configurations (40mm, 80mm, 160mm, upgradable w/o replacing gantry) and is offered with GE’s new Smart Subscription service (allows software upgrades/downgrades, plus auto updates). 
  • True to GE’s productivity focus, the Revolution Apex also includes a range of features to improve technologist efficiency and/or expand clinical applications (e.g. “Effortless Workflow,” patient positioning camera, TrueFidelity DLIR, motion correction for cardiac).

Much More – GE Healthcare has been busy throughout 2021, so although the other products in its RSNA booth were still quite new, they’ve already been detailed in previous Imaging Wire issues. Some of these other highlights include its in-development Photon Counting CT, it’s now FDA-approved Endotracheal Tube X-ray AI tool, its StarGuide SPECT/CT scanner, and its recent alliance with Optellum.

Arterys’ Platform Expansion

At a time when many major AI companies are trying to become AI platform companies, Arterys announced a trio of 3rd party AI alliances that showed how a mature AI platform might work.

Arterys Expands Neuro AI – Arterys launched neuroradiology AI alliances with Combinostics and Cercare Medical, expanding Arterys’ already-comprehensive Neuro AI suite (also includes MRI brain tumor diagnostics, CT stroke & ICH detection, 2D/4D Flow brain MRI). Combinostics’ cNeuro cMRI supports multiple neurological disorder assessments (specifically dementia and multiple sclerosis), while Cercare Perfusion automates brain CT and MRI perfusion map generation and stoke treatment decision making. 

Arterys Adds Breast AI – Arterys launched a global distribution agreement with iCAD, making iCAD’s full suite of breast health solutions available in Arterys’ new Breast AI suite. iCAD’s portfolio is certainly broad enough to deserve its own “suite,” ranging from 2D and 3D mammography detection, personalized risk screening and decision support, and density assessments. The Arterys Breast AI suite also makes iCAD available as a cloud-based SaaS solution for the first time (previously only on-premise).

Arterys Platform Impact – Arterys’ integration of multiple complementary AI tools within curated AI Suites is unique and makes a lot of sense. It seems to be far more helpful to provide neurorads with integrated access to a suite of neuro AI tools, than to provide them with one or two tools for every subspecialty.

The Takeaway – Arterys’ new alliances reveal a far more subspecialist-targeted approach than we usually see from AI platforms or marketplaces. It also shows that Arterys is committed to its vendor neutral strategy, effectively doubling its number of active AI partners (previously: Imaging Biometrics & Avicenna.AI in Neuro suite, MILVUE in Chest MSK Suite), while highlighting the value of its cloud-native structure for integrating new partners within the same user interface.

Get every issue of The Imaging Wire, delivered right to your inbox.

You're signed up!

It's great to have you as a reader. Check your inbox for a welcome email.

-- The Imaging Wire team