Tag: RSNA

Snow Doesn’t Slow RSNA 2025

Posted on by Brian Casey

RSNA 2025 is wrapping up this week in what’s been a cold and snowy Chicago. While many attendees experienced travel delays getting into the show on Sunday, the disruptions didn’t slow the blistering pace of radiology innovation on display.

As has been the case all year in radiology, AI has been a hot topic at McCormick Place, both in the presentation rooms and on the technical exhibit floor.

  • Much of the conversation is shifting away from individual point sources of AI – such as for analyzing images – and toward solutions that provide operational efficiencies such as faster radiology reporting.

But big iron has always been RSNA’s bread and butter, and RSNA 2025 didn’t disappoint. 

  • Major new product launches took place in the vendor exhibits, especially in helium-free MRI, photon-counting and spectral CT, and angiography, showing that vendors continue to invest in hardware development. Check out our coverage of the major OEMs in The Wire section below.

What were the other trends at RSNA 2025? They included…

  • Growing buzz around new AI technologies like foundation and vision language models.
  • Real-world clinical applications of AI such as triaging mammography screening.
  • Growing momentum of CT lung cancer screening, both internationally and in the U.S.
  • Use of generative AI to improve radiology reporting.
  • Imaging’s contribution to greenhouse gas emissions – and how to reduce them. 
  • Imaging-based biomarkers that can predict future disease incidence.
  • Opportunistic screening with imaging tests that can detect multiple diseases in one exam.

The Takeaway

Despite weather-related challenges, RSNA 2025 once again showed the importance of radiology’s showcase annual conference for bringing together academics, private-practice providers, vendors, and allied health professionals to meet, exchange ideas, and work together toward providing better patient care. It was great seeing everyone in Chicago – safe travels home!

CT Lung Screening Leads RSNA’s First Day

Posted on by Brian Casey

Day 1 of RSNA 2025 is in the books, and new research into CT lung cancer screening dominated the scientific sessions at Chicago’s McCormick Place.

Lung cancer screening is drawing attention as screening programs go into effect internationally.

  • In the U.S., lung screening is hampered by low completion rates (18-19%), but providers are finding that participation can be improved with aggressive identification and outreach to eligible patients.

Some highlights from Sunday (with handy session numbers to help you follow along) include…

  • The ScreenLungNet AI model predicted three-year lung cancer risk from CT lung screening scans with AUCs from 0.93-0.94 (S4-SSCH02-1).
  • In a study of 2.6k patients with lung cancer, only 36% met 2021 USPSTF lung cancer screening criteria, and just 5% actually got screened. Only 23% had data on their smoking history in the EMR (S4-SSCH02-2).
  • Risk assessment scores were used to perform CT lung screening of lower-risk people every two years rather than annually, reducing screening’s harms without missing many cancers (S4-SSCH02-4).
  • Compared to the landmark NLST study, a real-world CT lung screening program had fewer benign surgeries (12% vs. 18%), lower complication rates (24% vs. 32%), and better recurrence-free survival (HR = 0.60) (S4-SSCH02-5).
  • CT radiation dose was reduced 51% and contrast iodine use 61% through a triple-optimized protocol that included 80-kVp scanning, GE HealthCare’s TrueFidelity deep learning reconstruction, and low-iodine adaptive contrast injection (S2-SSCA01-1).
  • Using AI for automated patient positioning and scan range in CT exams cut positioning time 41% with 10-13% lower radiation dose and no discernible impact on image quality (S4-SSIN01-1).
  • Measures of adiposity acquired opportunistically from coronary artery calcium CT scans using HeartLung Technologies’ AI-CVD algorithm predicted adults at risk of diabetes in a study of 2.9k people (S5-SSCA02-6). 
  • The Promedius AI algorithm for osteoporosis assessment of chest radiographs had an AUC of 0.84 in a study of 1k adults from three countries (M3-SSCH03-1).
  • A real-world study of 2.1k patients found that DeepTek’s chest X-ray AI algorithm had an AUROC of 0.95 for detecting any of 13 clinically significant findings (M3-SSCH03-2).
  • Researchers presented a feasibility study of a compression-free spectral DBT mammography system, finding spatial resolution close to state-of-the-art systems (S4-SSPH02-6).
  • Researchers presented their protocol for MRI scanning of patients with cardiac implanted electronic devices. Over 10 years they scanned 7.3k patients with no major adverse events (S5-SSCA02-1).
  • Adding MRI data to a multimodal transformer AI model improved its ability to predict five-year breast cancer risk in intermediate- and high-risk women (S2-SSBR01-6).

The Takeaway

RSNA 2025 is off to a great start. Be sure to check back with Thursday’s newsletter for more radiology news from Chicago, and follow along on our social media channels for ongoing video updates. 

AI As Malpractice Safety Net

Posted on by Brian Casey

One of the emerging use cases for AI in radiology is as a safety net that could help hospitals avoid malpractice cases by catching errors made by radiologists before they can cause patient harm. The topic was reviewed in a Sunday presentation at RSNA 2024. 

Clinical AI adoption has been held back by economic factors such as limited reimbursement and the lack of strong return on investment. 

  • Healthcare providers want to know that their AI investments will pay off, either through direct reimbursement from payors or improved operational efficiency.

At the same time, providers face rising malpractice risk, with a number of recent high-profile legal cases.

  • For example, a New York hospital was hit with a $120M verdict after a resident physician working the night shift missed a pulmonary embolism. 

Could AI limit risk by acting as a backstop to radiologists? 

  • At RSNA 2024, Benjamin Strong, MD, chief medical officer at vRad, described how they have deployed AI as a QA safety net. 

vRad mostly develops its own AI algorithms, with the first algorithm deployed in 2015. 

  • vRad is running AI algorithms as a backstop for 13 critical pathologies, from aortic dissection to superior mesenteric artery occlusion.

vRad’s QA workflow begins after the radiologist issues a final report (without using AI), and an algorithm then reviews the report automatically. 

  • If discrepancies are found the report is sent to a second radiologist, who can kick the study back to the original radiologist if they believe an error has occurred. The entire process takes 20 minutes. 

In a review of the program over one year, vRad found …

  • Corrections were made for about 1.5k diagnoses out of 6.7M exams.
  • The top five AI models accounted for over $8M in medical malpractice savings. 
  • Three pathologies – spinal epidural abscess, aortic dissection, and ischemic bowel due to SMA occlusion – would have amounted to $18M in payouts over four years.
  • Adding intracranial hemorrhage and pulmonary embolism creates what Strong called the “Big Five” of pathologies that are either the most frequently missed or the most expensive when missed.

The Takeaway

The findings offer an intriguing new use case for AI adoption. Avoiding just one malpractice verdict or settlement would more than pay for the cost of AI installation, in most cases many times over. How’s that for return on investment?

RSNA 2024 Video Highlights

Posted on by Brian Casey

Last week’s RSNA 2024 meeting saw a major bounce in attendance, with early numbers indicating an 18% jump in the number of radiology professionals wandering the halls of McCormick Place. The increase brought total attendance at midweek to 40k. 

As in past years, AI dominated the discussion, both in the presentation rooms and on the exhibit floor. Researchers presented the latest findings on AI’s ability to aid radiologists, while vendors showcased new algorithms for use cases from mammography screening to fracture detection. New technologies like foundation models for AI training bubbled under the surface and promise to have a major impact in years to come.

It was our privilege to speak with many of the most interesting vendors exhibiting at RSNA 2024, from multinational vendors to small but promising start-ups.

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.

RSNA Goes All-In on AI

Posted on by Brian Casey

CHICAGO – It’s been AI all the time this week at RSNA 2024. From clinical sessions packed with the latest findings on AI’s utility to technical exhibits crowded with AI vendors, artificial intelligence and its impact on radiology was easily the hottest trend at McCormick Place.

Radiology greeted AI with initial skepticism when the first applications like IBM Watson were introduced at RSNA around a decade ago.

  • But the field’s attitude has been evolving to the point where AI is now being viewed as perhaps the only technology that can save the discipline from the vicious cycle of rising exam volume, falling reimbursement, and pervasive levels of burnout.

RSNA telegraphed the shift last year by announcing that Stanford University’s Curtis Langlotz, MD, PhD, would be RSNA 2024 president. 

  • Langlotz is one of the most respected AI researchers and educators in radiology, and even coined the phrase that while AI would not replace radiologists, radiologists with AI would replace those without it. 

In his president’s address, Langlotz echoed this theme, painting a picture of a future radiology in which humans and machines collaborate to deliver better patient care than either could alone.

  • Langlotz’s talk was followed by a presentation by another prominent AI luminary – Nina Kottler, MD, of Radiology Partners.

Kottler took on the concerns that many in radiology (and in the world at large) have about AI as a disruptive force in a field that cherishes its traditions.

  • She advised radiology to take a leading role in AI adoption, repeating a famous quote that the best way to predict the future is to create it yourself. 

What were the other trends besides AI at RSNA 2024? They included…

  • Photon-counting CT, which is likely to see new market entrants in 2025.
  • Total-body PET, with PET scanners that have extra-long detector arrays.
  • Theranostics, a discipline that integrates diagnosis and therapy and promises to breathe new life into SPECT.
  • CT colonography and CCTA, which will see positive reimbursement changes in 2025.
  • Continued growth of CT lung screening, especially as a tool for opportunistic screening of other conditions.
  • Continued expansion of AI for breast screening.

The Takeaway

The RSNA meeting has been called radiology’s Super Bowl and World Cup all rolled into one, and this year didn’t disappoint. RSNA 2024 showed that radiology is prepared to fully embrace AI – and a future in which humans and machines collaborate to deliver better patient care.

Mammo AI Kicks Off RSNA 2024

Posted on by Brian Casey

Welcome to RSNA 2024! This year’s meeting is starting with a bang, with two important sessions highlighting the key role AI can play in breast screening. 

Sunday’s presentations cap a year that’s seen the publication of several large studies demonstrating that AI can improve breast cancer screening while potentially reducing radiologist workload. 

  • That momentum is continuing at RSNA 2024, with morning and afternoon sessions on Sunday dedicated to mammography AI. 

Some findings from yesterday’s morning session include … 

  • Two AI algorithms were better than one when supporting radiologists in breast screening, with cancer detection ratios relative to historic performance rising from 0.97 to 1.08 with one AI to 1.09 to 1.14 with two algorithms.
  • ScreenPoint Medical’s Transpara algorithm was able to prioritize the worklist for 57% of breast screening exams by assigning risk scores to mammograms, helping reduce report turnaround times. 
  • iCAD’s ProFound AI software helped radiologists detect 7.8% more breast cancers on DBT exams, and cancers were detected at an earlier stage. 
  • Applying AI for breast screening to a racially diverse population yielded evenly distributed performance improvements.

Meanwhile, the Sunday afternoon session also included significant mammography AI presentations, such as …

  • A hybrid screening strategy – with suspicious breast cancer cases only recalled if the AI exhibits high certainty – reduced workload 50%. 
  • Lunit’s Insight DBT AI showed potential to reduce interval cancer rates in DBT screening by identifying 27% of false-negative and 36% of interval cancers.
  • In the ScreenTrustCAD trial in Sweden, using Lunit’s Insight MMG algorithm to replace a double-reading radiologist reduced workload 50% with comparable cancer detection rates.
  • A German screening program found that ScreenPoint Medical’s Transpara AI boosted the cancer detection rate by 8.7% (from 0.68% to 0.74%), with 8.8% of cancers solely detected by AI.
  • Researchers took a look back at abnormality scores from three commercially available AI algorithms after cancer diagnosis, finding evidence that cancers could be detected earlier. 

The Takeaway

Breast screening seems to be the clinical use case where radiologists need the most help, and Sunday’s sessions show the progress AI is making toward achieving that reality. 

Be sure to check back on our X, LinkedIn, and YouTube pages for more coverage of this week’s events in Chicago. And if you see us on the floor of McCormick Place, stop and say hello!

AI Powers Opportunistic Screening

Posted on by Brian Casey

The growing power of AI is opening up new possibilities for opportunistic screening – the detection of pathology using data acquired for other clinical indications. The potential of CT-based opportunistic screening – and AI’s role in its growth – was explored in a session at RSNA 2023.

What’s so interesting about opportunistic screening with CT? 

  • As one of imaging’s most widely used modalities, CT scans are already being acquired for many clinical indications, collecting body composition data on muscle, fat, and bone that can be biomarkers for hidden pathology. 

What’s more, AI-based tools are replacing many of the onerous manual measurement tasks that previously required radiologist involvement. There are four primary biomarkers for opportunistic screening, which are typically related to several major pathologies, said Perry Pickhardt, MD, of the University of Wisconsin-Madison, who led off the RSNA session:

  • Skeletal muscle density (sarcopenia)
  • Hard calcified plaque, either coronary or aortic (cardiovascular risk)
  • Visceral fat (cardiovascular risk)
  • Bone mineral density (osteoporosis and fractures) 

But what about the economics of opportunistic screening? 

  • A recent study in Abdominal Radiology found that in a hypothetical cohort of 55-year-old men and women, AI-assisted opportunistic screening for cardiovascular disease, osteoporosis, and sarcopenia was more cost-effective compared to both “no-treatment” and “statins for all” strategies – even assuming a $250/scan charge for use of AI.

But there are barriers to opportunistic screening, despite its potential. In a follow-up talk, Arun Krishnaraj, MD, of UVA Health in Virginia said he believes fully automated AI algorithms are needed to avoid putting the burden on radiologists. 

And the regulatory environment for AI tools is complex and must be navigated, said Bernardo Bizzo, MD, PhD, of Mass General Brigham.

Ready to take the plunge? The steps for setting up a screening program using AI were described in another talk by John Garrett, PhD, Pickhardt’s colleague at UW-Madison. This includes: 

  • Normalizing your data for AI tools
  • Identifying the anatomical landmarks you want to focus on
  • Automatically segmenting areas of interest
  • Making the biomarker measurements
  • Plugging your data into AI models to predict outcomes and risk-stratify patients

The Takeaway

Opportunistic screening has the potential to flip the script in the debate over radiology utilization, making imaging exams more cost-effective while detecting additional pathology and paving the way to more personalized medicine. With AI’s help, radiologists have the opportunity to place themselves at the center of modern healthcare. 

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.

Welcome to RSNA 2023

Posted on by Brian Casey

It’s off to the races at RSNA 2023 as radiology’s showcase conference kicked off on Sunday. 

“Leading Through Change” is the theme of this year’s meeting, and it’s an appropriate slogan for a specialty that seems on the cusp of disruption with the growing use of AI, deep learning, and other tools. 

  • AI is being featured prominently in scientific presentations and vendor exhibits in McCormick Place, with a particular focus on whether large language models like ChatGPT can find practical application in radiology. Early research is promising but still inconclusive.

Another major focus at RSNA 2023 has been lung cancer screening, with Sunday afternoon sessions investigating how screening can be expanded: 

  • Researchers mined a database of 32k women who got screening mammography to find eligible candidates for lung screening, finding 5% who met screening criteria. 
  • Using the USPTSF’s 2021 guideline revision to find screening candidates led to shorter smoking histories (42 vs. 29 pack-years) and slightly more women being eligible (48% vs. 46%). 
  • ChatGPT gave more correct answers than Google Bard to non-expert questions on lung screening (71% vs. 52%).
  • ChatGPT, GPT-4, and Bard needed multiple iterations to produce reports readable by patients. 

AI is also proving its value for selecting screening candidates and identifying lung pathology: 

  • An AI algorithm analyzed chest X-rays to determine whether an individual would benefit from CT lung cancer screening – even if they don’t smoke. In 17.4k patients, the model classified 28% as high risk, 2.9% of whom were later diagnosed with lung cancer, a higher level than the 1.3% six-year threshold at which guidelines recommend CT lung screening.
  • A deep learning algorithm analyzed chest X-rays in a cohort of 10k patients to predict who would develop type 2 diabetes, turning in better accuracy than a model that only looked at clinical factors like age, BMI and HbA1c levels (AUCs:  0.84 vs. 0.79). 

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. 

The Takeaway
The RSNA has always been known as the Super Bowl of radiology, and this year’s meeting is off to a great start. Be sure to check back on our Twitter/X, LinkedIn, and YouTube pages for more coverage of this week’s events in Chicago.

Vendors Enter RSNA on Q3 Roll

Posted on by Brian Casey

As RSNA 2023 approaches, medical imaging vendors appear to be on a roll when it comes to financial results. In the weeks leading up to the meeting, companies have posted numbers that for the most part are strongly positive and appear to be leaving the bad old days of the COVID-19 pandemic behind.

Agfa – Between Agfa’s two imaging divisions, healthcare IT continues to outperform the radiology solutions business. Healthcare IT saw growth in revenue (3.3% to $67M) and EBITDA (44.3% to $6.4M), but revenue declined at radiology solutions (-5.7% to $127M) as did EBITDA (-21% to $10M). 

Canon – Canon Medical Systems saw firm revenues in Japan and Europe, which propelled the business unit to higher revenues (5% to $913M) while income before taxes edged up (0.3% to $46M). 

Fujifilm – Revenues tapered off slightly in Fujifilm’s healthcare business at constant currency rates (-1.9% to $1.66B) as a 12.4% decline in its contract manufacturing business offset 1.7% growth in medical systems. Operating income in healthcare slipped due to a one-time benefit in the year-ago quarter (-6.5% to $217M).

GE HealthCare – Revenue growth in its molecular imaging and CT businesses helped propel GE HealthCare’s revenue growth (5.4% to $4.82B), assisted by 13% growth in pharmaceutical diagnostics and a 9% increase in patient care solutions. Net income was lower (-23% to $375M). 

Guerbet – Strong revenues for the third quarter in Asia (+15%) and stability in the EMEA region (0.6%) helped counter a decline in the Americas (-5.2%), enabling Guerbet to turn in overall quarterly revenue growth at constant exchange rates (2.3% to $212M). The company expects sales of its Elucirem MRI contrast agent to ramp up in the fourth quarter. 

Hologic – The semiconductor shortage that had impacted Hologic in previous quarters eased, leading to a sharp jump in revenues in the company’s breast health business (27% to $353M). The rebound didn’t extend to Hologic’s overall net income as its net margin narrowed (-24% to $91M). 

Konica Minolta – A decline in sales of X-ray systems to hospitals in its core market of Japan and a slower US hospital market produced lower revenues in Konica Minolta’s healthcare division (-5% to $238M), and the business posted an operating loss (-$5.5M).

Philips – Philips rebounded in the most recent quarter, with revenues in its diagnosis and treatment division rising sharply after currency conversion thanks to double-digit growth in all businesses (14% to $2.39B). Operating income doubled (to $272M). 

RadNet – RadNet saw a double-digit jump in revenues (15% to $402M) while net income leaped ($17.5M vs. $668k). Revenue jumped 221% in the company’s AI segment, which made progress narrowing its EBITDA loss (-$2.5M vs. -$4.5M) on higher consumer adoption of its Enhanced Breast Cancer Detection offering.  

Siemens Healthineers – Siemens Healthineers closed its financial year with “outstanding” 8.3% revenue growth at constant exchange rates, including double-digit growth in its imaging business (11% to $3.62B) while adjusted EBIT edged up (2% to $812M). Its Varian radiation therapy business saw a strong recovery in revenue (30% to $1.1B) and adjusted EBIT (90% to $207M).

Varex – Growth in Varex’s industrial X-ray imaging business propelled the company to higher overall revenues even as revenues in its medical business fell (-9.8% to $164M). The medical division’s gross profit also slipped (-7% to $53M).

The Takeaway

Not every company was a winner in this last round of quarterly earnings, but at least the macroeconomic headwinds of the COVID-19 pandemic are fading. The fourth calendar quarter is typically radiology’s strongest period due to the impact of the RSNA conference on equipment purchasing, so let’s hope the momentum continues.

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