Month: May 2024

Is Radiology’s AI Edge Fading?

Posted on by Brian Casey

Is radiology’s AI edge fading, at least when it comes to its share of AI-enabled medical devices being granted regulatory authorization by the FDA? The latest year-to-date figures from the agency suggest that radiology’s AI dominance could be declining. 

Radiology was one of the first medical specialties to go digital, and software developers have targeted the field for AI applications like image analysis and data reconstruction.

  • Indeed, FDA data from recent years shows that radiology makes up the vast majority of agency authorizations for AI- and machine learning-enabled medical devices, ranging from 86% in 2020 and 2022 to 79% in 2023. 

But in the new data, radiology devices made up only 73% of authorizations from January-March 2024. Other data points indicate that the FDA …

  • Authorized 151 new devices since August 2023
  • Reclassified as AI/ML-enabled 40 devices that were previously authorized 
  • Authorized a total of 882 devices since it began tracking the field 

      In an interesting wrinkle, many of the devices on the updated list are big-iron scanners that the FDA has decided to classify as AI/ML-enabled devices. 

      • These include CT and MRI scanners from Siemens Healthineers, ultrasound scanners from Philips and Canon Medical Systems, an MRI scanner from United Imaging, and the recently launched Butterfly iQ3 POCUS scanner. 

      The additions could be a sign that imaging OEMs increasingly are baking AI functionality into their products at a basic level, blurring the line between hardware and software. 

      The Takeaway

      It should be no cause for panic that radiology’s share of AI/ML authorizations is declining as other medical specialties catch up to the discipline’s head start. The good news is that the FDA’s latest figures show how AI is becoming an integral part of medicine, in ways that clinicians may not even notice.

      Slashing CT Radiation Dose

      Posted on by Brian Casey

      Cutting CT radiation dose should be the goal of every medical imaging facility. A new paper in European Radiology offers a promising technique that slashed CT dose to one-tenth of conventional CT – and just twice that of a standard chest X-ray.

      CT’s wide availability, excellent image quality, and relatively low cost make it an invaluable modality for many clinical applications.

      • CT proved particularly useful during the COVID-19 pandemic for diagnosing lung pathology caused by the virus, and it continues to be used to track cases of long COVID.

      But patient monitoring can involve multiple CT scans, leading to cumulative radiation exposure that can be concerning, especially for younger people.

      • Researchers in Austria wanted to see if they could use commercially available tools to produce ultra-low-dose CT scans, and then assess how they compared to conventional CT for tracking patients with long COVID.

      Using Siemens Healthineers’ Somatom Drive third-generation dual-source CT scanner, they adjusted the parameters on the system’s CAREDose automated exposure control and ADMIRE iterative reconstruction to drive down dose as much as possible.

      • Other ultra-low-dose CT settings versus conventional CT included fixed tube voltage (100 kVp vs. 110 kVp), tin filtration (enabled vs. disabled), and CAREDose tube current modulation (enabled – weak vs. enabled – normal). 

      They then tested the settings in a group of 153 patients with long COVID seen from 2020 to 2021; both ultra-low-dose and conventional CT scans were compared by radiologists, finding … 

      • Mean entrance-dose radiation levels with ultra-low-dose CT were less than one-tenth those of conventional CT in (0.21 mSv vs. 2.24 mSv); a two-view chest X-ray is 0.1 mSv
      • Image quality was rated 40% lower on a five-point scale (3.0 vs. 5.0)
      • But all ultra-low-dose scans were rated as diagnostic quality
      • Intra-reader agreement between the two techniques was “excellent,” at 93%

      The findings led the researchers to conclude that ultra-low-dose CT could be a good option for tracking long COVID, such as in younger patients. 

      The Takeaway

      The study demonstrates that CT radiation dose can be driven down dramatically through existing commercially available tools. While this study covers just one niche clinical application, such tools could be applied to a wider range of uses, ensuring that the benefits of CT will continue to be made available at lower radiation doses than ever.

      Fine-Tuning AI for Breast Screening

      Posted on by Brian Casey

      AI has shown in research studies it can help radiologists interpret breast screening exams, but for routine clinical use many questions remain about the optimal AI parameters to catch the most cancers while generating the fewest callbacks. Fortunately, a massive new study out of Norway in Radiology: Artificial Intelligence provides some guidance. 

      Recent research such as the MASAI trial has already demonstrated that AI can help reduce the number of screening mammograms radiologists have to review, and for many low-risk cases eliminate the need for double-reading, which is commonplace in Europe. 

      • But growing interest in breast screening AI is tempered by the field’s experience with computer-aided detection, which was introduced over 20 years ago but generated many false alarms that slowed radiologists down. 

      Fast forward to 2024. The new generation of breast AI algorithms seems to have addressed CAD’s shortcomings, but it’s still not clear exactly how they can best be used. 

      • Researchers from Norway’s national breast screening program tested one mammography AI tool – Lunit’s Insight MMG – in a study with data obtained from 662k women screened with 2D mammography from 2004 to 2018. 

      Researchers tested AI with a variety of specificity and sensitivity settings based on AI risk scores; in one scenario, 50% of the highest risk scores were classified as positive for cancer, while in another that threshold was set to 10%. The group found …

      • At the 50% cutoff, AI would correctly identify 99% of screen-detected cancers and 85% of interval cancers. 
      • At the 10% cutoff, AI would detect 92% of screen-detected cancers and 45% of interval cancers 
      • AI understandably performed better in identifying false-positive cases as negative at the 10% threshold than 50% (69% vs. 17%)
      • AI had a higher AUC than double-reading for screen-detected cancers (0.97 vs. 0.88)

      How generalizable is the study? It’s worth noting that the research relied on AI of 2D mammography, which is prevalent in Europe (most mammography in the US employs DBT). In fact, Lunit is targeting the US with its recently cleared Insight DBT algorithm rather than Insight MMG. 

      The Takeaway

      As with MASAI, the new study offers an exciting look at AI’s potential for breast screening. Ultimately, it may turn out that there’s no single sensitivity and specificity threshold at which mammography AI should be set; instead, each breast imaging facility might choose the parameters they feel best suit the characteristics of their radiologists and patient population. 

      Headwinds Slow AI Funding

      Posted on by Brian Casey

      Venture capital funding of medical imaging AI developers continues to slow. A new report from Signify Research shows that funding declined 19% in 2023, and is off to a slow start in 2024 as well. 

      Signify tracks VC funding on an annual basis, and previous reports from the UK firm showed that AI investment peaked in 2021 and has been declining ever since. 

      • The report’s author, Signify analyst Ellie Baker, sees a variety of factors behind the decline, chief among them macroeconomic headwinds such as tighter access to capital due to higher interest rates. 

      Total Funding Value Drops – Total funding for 2023 came in at $627M, down 19% from $771M in 2022. Funding hit a peak in 2021 at $1.1B.

      Deal Volume Declines – The number of deals in 2023 fell to 35, down 30% from 50 the year before. Deal volume peaked in 2021 at 63. And 2024 isn’t off to a great start, with only five deals recorded in the first quarter. 

      Deals Are Getting Bigger – Despite the declines, the average deal size grew last year, to $19M, up 23% versus $15M in 2022. 

      HeartFlow Rules the Roost – HeartFlow raised the most in 2023, fueled by a massive $215M funding round in April 2023, while Cleerly held the crown in 2022.

      US Funding Dominates – On a geographic basis, funding is shifting away from Europe (-46%) and Asia-Pacific (no 2023 deals) and back to the Americas, which generated over 70% of the funding raised last year. This may be due to the US providing faster technology uptake and more routes to reimbursement.

      Early Bird Gets the Worm – Unlike past years in which later-stage funding dominated, 2024 has seen a shift to early-stage deals with seed funding and Series A rounds, such as AZmed’s $16M deal in February 2024. 

      $100M Club Admits New Members – Signify’s exclusive “$100M Club” of AI developers has expanded to include Elucid and RapidAI. 

      The Takeaway

      Despite the funding drop, Signify still sees a healthy funding environment for AI developers ($627M is definitely a lot of money). That said, AI software developers are going to have to make a stronger case to investors regarding revenue potential and a path to ROI. 

      USPSTF’s Mammography Letdown?

      Posted on by Brian Casey

      Last year’s relief that the USPSTF would lower its recommended starting age for breast screening to 40 gave way to frustration this week that the group did not go farther in its final decision on mammography recommendations. 

      In a series of papers in JAMA journals this week, the USPSTF tackled a range of breast screening issues, from the age at which screening should start to whether modalities like ultrasound and MRI should be used to supplement conventional mammography.

      That was the good news. The bad news is that breast screening advocates mostly got shut out on a variety of other issues, with the USPSTF … 

      • Advising that breast screening be conducted biennially (every two years), rather than annually as most women’s imaging advocates would prefer
      • Declining to raise the recommended upper limit for screening from 74 to 79
      • Declining to recommend supplemental screening with MRI or ultrasound for women with dense breast tissue, even as women express frustration with the lack of reimbursement for these exams

      On the positive side, the USPSTF finally weighed in on DBT, stating that the 3D mammography technology is equivalent to digital mammography for breast screening. 

      • But in another disappointment, the group said it couldn’t find any studies stating that DBT was better than 2D digital mammography. 

      Given the fierce battles that have been fought over screening guidelines in the last 15 years, what made the USPSTF change its mind on mammography’s starting age? 

      • One big factor is the 2% annual rise in breast cancer incidence in women in their 40s from 2015 to 2019; the higher mortality rates among Black women was another issue (see story below in The Wire).

      The Takeaway

      The USPSTF’s move to lower its recommended starting age for screening mammography is a welcome – if overdue – change for women, who for 15 years have borne the brunt of the group’s conservative approach to guideline formation. The question remains, is the USPSTF making the same mistake all over again when it comes to supplemental imaging and annual screening? And how long will women have to wait this time until it sees the light?

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