Author: Jake Fishman

Annalise.ai’s Pneumothorax Performance

Posted on by Jake Fishman

A new Mass General Brigham study highlighted Annalise.ai’s pneumothorax detection solution’s strong diagnostic performance, including across different pneumothorax types and clinical scenarios.

The researchers used Annalise Enterprise CXR Triage Pneumothorax to “analyze” 985 CXRs (435 positive), detecting simple and tension pneumothorax cases with high accuracy:

  • Simple pneumothorax – 0.979 AUC (94.3% sensitivity, 92.0% specificity)
  • Tension pneumothorax – 0.987 AUC (94.5% sensitivity, 95.3% specificity)

The study also suggests that Annalise Enterprise CXR should maintain this strong performance when used outside of Mass General, as it surpassed standard accuracy benchmarks (>0.95 AUC, >80% sensitivity & specificity) across nearly all of the study’s clinical scenarios (CXR manufacturer, CXR projection type, patient sex/age/positioning). 

The Takeaway

The clinical benefits of early pneumothorax detection are clear, so studies like this are good news for the growing number of FDA-approved pneumothorax AI vendors who are working on clinical adoption. 

However, this study feels like even better news for Annalise.ai, noting that it is one of the few pneumothorax AI vendors that detects both simple and tension pneumothorax, and considering that Annalise Enterprise CXR is capable of detecting 122 other CXR indications (even if it’s currently only FDA-cleared for pneumothorax).

The Case for Pancreatic Cancer Radiomics

Posted on by Jake Fishman

Mayo Clinic researchers added to the growing field of evidence suggesting that CT radiomics can be used to detect signs of pancreatic ductal adenocarcinoma (PDAC) well before they are visible to radiologists, potentially allowing much earlier and more effective surgical interventions.

The researchers first extracted pancreatic cancer’s radiomics features using pre-diagnostic CTs from 155 patients who were later diagnosed with PDAC and 265 CTs from healthy patients. The pre-diagnostic CTs were performed for unrelated reasons a median of 398 days before cancer diagnosis.

They then trained and tested four different radiomics-based machine learning models using the same internal dataset (training: 292 CTs; testing: 128 CTs), with the top model identifying future pancreatic cancer patients with promising results:

  • AUC – 0.98
  • Accuracy – 92.2%
  • Sensitivity – 95.5%
  • Specificity – 90.3% 

Interestingly, the same ML model had even better specificity in follow-up tests using an independent internal dataset (n= 176; 92.6%) and an external NIH dataset (n= 80; 96.2%).

Mayo Clinic’s ML radiomics approach also significantly outperformed two radiologists, who achieved “only fair” inter-reader agreement (Cohen’s kappa 0.3) and produced far lower AUCs (rads’ 0.66 vs. ML’s 0.95 – 0.98). That’s understandable, given that these early pancreatic cancer “imaging signatures” aren’t visible to humans.

The Takeaway

Although radiomics-based pancreatic cancer detection is still immature, this and other recent studies certainly support its potential to detect early-stage pancreatic cancer while it’s treatable. 
That evidence should grow even more conclusive in the future, noting that members of this same Mayo Clinic team are operating a 12,500-patient prospective/randomized trial exploring CT-based pancreatic cancer screening.

Content-Based AI Efficiency

Posted on by Jake Fishman

A new study out of Austria provided solid evidence that content-based image retrieval systems (CBIRS) enhance radiologists’ reading efficiency, while potentially improving their diagnostic accuracy.

Eight radiologists reviewed chest CTs from 108 patients with suspected diffuse parenchymal lung disease (DPLD), leveraging contextflow’s AI-based SEARCH Lung CT CBIRS with half of the exams. 

Using the radiologists’ CT image regions of interest, the CBIRS would search a database of 6,542 chest CTs to identify similar scans, providing the rads with the three most likely disease patterns and supporting information (e.g. a list of potential differential diagnoses). The CBIRS’ added “context” had a notable impact on the radiologists:

  • Reducing their average reading time by 31.3% (197 vs. 287 seconds) 
  • Reducing resident and attending radiologists’ reading time by 27% and 35% 
  • Improving overall diagnostic accuracy by over 7pts (42.2% vs. 34.7%; not statistically significant)

These reading time reductions came despite the fact that radiologists were more likely to search for additional information when using the CBIRS (72% vs. 43% of cases). That’s partially because CBIRS allowed greater speed improvements when radiologists searched for more information (110 seconds faster vs. without CBIRS) than when rads didn’t search for more info (39 seconds faster).

The Takeaway
This study presents a rare example of how imaging AI can significantly improve radiologists’ efficiency, while amplifying their current workflows and diagnostic decision-making processes. It’s also the second study in the last year suggesting that CBIRS might improve diagnostic accuracy, although the authors encourage more research into CBIRS’ accuracy impact to know for sure.

Cathay’s AI Underwriting

Posted on by Jake Fishman

Cathay Life Insurance will use Lunit’s INSIGHT CXR AI solution to identify abnormalities in its applicants’ chest X-rays, potentially modernizing a manual underwriting process and uncovering a new non-clinical market for AI vendors.

Lunit INSIGHT CXR will be integrated into Cathay’s underwriting workflow, with the goals of enhancing its radiologists’ accuracy and efficiency, while improving Cathay’s underwriting decisions. 

Lunit and Cathay have reason to be optimistic about this endeavor, given that their initial proof of concept study found that INSIGHT CXR:

  • Improved Cathay’s radiologists’ reading accuracy by 20%
  • Reduced the radiologists’ overall reading time by up to 90%

Those improvements could have a significant labor impact, considering that Cathay’s rads review 30,000 CXRs every year. They might have an even greater business impact, noting the important role that underwriting accuracy has on policy profitability.

Lunit’s part of the announcement largely focused on its expansion beyond clinical settings, revealing plans to “become the driving force of digital innovation in the global insurance market” and to further expand its business into “various sectors outside the hospital setting.”

The Takeaway

Even if life insurers only require CXRs for a small percentage of their applicants (older people, higher value policies), they still review hundreds of thousands of CXRs each year. That makes insurers an intriguing new market segment for AI vendors, and makes you wonder what other non-clinical AI use cases might exist. However, it might also make radiologists who are still skeptical about AI concerned.

Imaging in H1 2022

Posted on by Jake Fishman

The first half of 2022 is now a wrap, and it was another big one within medical imaging. Here are some of the top storylines from the last 6 months and some things to keep in mind as we head into 2022’s second half:

  • Imaging Goes Home – Healthcare’s major shift into patient homes seemed to be bringing imaging along with it in H1, leading to new vendor-side efforts focused on at-home ultrasound (e.g. Caption’s home echo program, GE’s Pulsenmore investment), more providers expanding their mobile imaging capabilities, and new research efforts focused on patient-performed exams and mobile imaging operations. 
  • AI Shakeup – Everyone who has been predicting AI consolidation got to take a victory lap in H1, which brought at least two strategic pivots (MaxQ AI & Kheiron) and the acquisitions of Aidence and Quantib (by RadNet) and Nines (by Sirona). This kind of consolidation is normal for an emerging segment, but it wouldn’t be surprising if the difficult funding climate leads to above-normal consolidation in H2.
  • Photon Counting Reality – The momentum from Siemens’ photon counting CT launch in late 2021 carried into this year, leading to a series of studies suggesting that PCCT might be as good as anticipated, the launch of Samsung NeuroLogica’s own head/neck PCCT system, and increased photon counting R&D and marketing efforts from the other major CT OEMs.
  • The Patient Engagement Push – The first half seemed to bring a surge in patient engagement activity, including new investments from the major image sharing vendors, increased pressure from radiology leaders to finally achieve universal image sharing, and new efforts to make radiology reports more accessible and understandable.
  • The Platform Pathway – The trend towards AI platforms heated up in H1, as new vendors launched or expanded their AI platforms, the major PACS players increased their AI integration efforts, and startups and radiology teams increasingly embraced AI platforms as a solution to their narrow AI challenges.

Optellum’s NTAPC

Posted on by Jake Fishman

Optellum joined the small group of imaging AI vendors who are on a path towards reimbursements, representing a major milestone for Optellum and another sign of progress for the business of imaging AI.

With Optellum’s “New Technology Ambulatory Payment Classification” (NTAPC), providers who use the Optellum Lung Cancer Prediction solution with Medicare patients can bill CMS $600-$700 for each use (CPT: 0721T).

Physicians would use Optellum LCP to analyze a Medicare patient’s CT scan, leveraging Optellum’s pulmonary nodule risk scores to support their decision whether to refer the patient to a pulmonologist. Then they would bill CMS for reimbursement.

However, like previous NTAPCs, this is just the first step in Optellum’s path towards full reimbursement coverage:

  • Regional Medicare Administrative Contractors will initially decide whether to reimburse on a case-by-case basis (and can decline reimbursements)
  • A similar process will happen with private plans
  • Reimbursements would only be nationally required once Optellum LCP is covered by each of the 12 MAC geographies and all commercial payors

Although not guaranteed, Optellum’s CMS-defined reimbursement rates/process represents a solid first step, especially considering that Perspectum and HeartFlow’s previous NTAPCs led to widespread coverage.

Optellum’s NTAPC also continues imaging AI’s overall progress towards reimbursements. Within the last two years, Viz.ai and Caption Health scored the first AI NTAPs (guaranteed add-on payments, but temporary) and startups like Nanox AI, Koios, and Perspectum landed AI’s first CPT III codes (reimbursements not guaranteed, but data collected for future reimbursement decisions). 

The Takeaway
Although reimbursements are still elusive for most AI vendors and not even guaranteed for most AI products that already have billing codes, it’s clear that we’re seeing more progress towards AI reimbursements. That’s good news for AI vendors, since it’s pretty much proven that reimbursements drive AI adoption and are necessary to show ROI for many AI products.

AI Experiences & Expectations

Posted on by Jake Fishman

The European Society of Radiology just published new insights into how imaging AI is being used across Europe and how the region’s radiologists view this emerging technology.

The Survey – The ESR reached out to 27,700 European radiologists in January 2022 with a survey regarding their experiences and perspectives on imaging AI, receiving responses from just 690 rads.

Early Adopters – 276 the 690 respondents (40%) had clinical experience using imaging AI, with the majority of these AI users:

  • Working at academic and regional hospitals (52% & 37% – only 11% at practices)
  • Leveraging AI for interpretation support, case prioritization, and post-processing (51.5%, 40%, 28.6%)

AI Experiences – The radiologists who do use AI revealed a mix of positive and negative experiences:

  • Most found diagnostic AI’s output reliable (75.7%)
  • Few experienced technical difficulties integrating AI into their workflow (17.8%)
  • The majority found AI prioritization tools to be “very helpful” or “moderately helpful” for reducing staff workload (23.4% & 62.2%)
  • However, far fewer reported that diagnostic AI tools reduced staff workload (22.7% Yes, 69.8% No)

Adoption Barriers – Most coverage of this study will likely focus on the fact that only 92 of the surveyed rads (13.3%) plan to acquire AI in the future, while 363 don’t intend to acquire AI (52.6%). The radiologists who don’t plan to adopt AI (including those who’ve never used AI) based their opinions on:

  • AI’s lack of added value (44.4%)
  • AI not performing as well as advertised (26.4%)
  • AI adding too much work (22.9%)
  • And “no reason” (6.3%)

US Context – These results are in the same ballpark as the ACR’s 2020 US-based survey (33.5% using AI, only 20% of non-users planned to adopt within 5 years), although 2020 feels like a long time ago.

The Takeaway

Even if this ESR survey might leave you asking more questions (What about AI’s impact on patient care? How often is AI actually being used? How do opinions differ between AI users and non-users?), more than anything it confirms what many of us already know… We’re still very early in AI’s evolution, and there’s still plenty of performance and perception barriers that AI has to overcome.

Longevity Imaging

Posted on by Jake Fishman

The emerging and controversial topic of longevity-focused imaging is back in the news, after AMRA Medical announced that Human Longevity will provide AMRA’s MRI-based Body Composition Profile Scans to its members. 

Human Longevity’s 100+ Precision Longevity Care program is built to help members live a “healthier and longer life” through risk detection and prevention. The program already included a range of exams (whole genome sequencing, blood biomarkers, whole body imaging, bone/muscle strength, nutrition/lifestyle), and now goes even deeper into imaging with AMRA’s MRI body composition analysis.

Just about every radiologist on Twitter rejected the idea of proactive imaging a few months ago when they came across a tweet from Human Longevity co-founder Dr. Peter Diamandis endorsing annual CTs and MRIs. As you might expect, the radiologists took issue with the exams’ radiation exposure and overdiagnosis risks, and had a laugh about annual CTs’ impact on patient genomes.

However, proactive longevity imaging services might be emerging faster than many of us realize, helped by a growing field of startups and major healthcare-wide trends towards consumerization and personalization…

  • BrainKey combines brain MRI and genetics analysis to help individuals understand their current brain health and the factors influencing their future brain longevity.
  • Ezra provides concerned/curious patients with full-body MRI cancer screenings, followed by easy-to-understand reports and physician consultations.
  • Q Bio analyzes full-body MRIs and other data (medical records, blood, saliva, vitals, urine) to create a patient physiological “digital twin” that’s used to proactively assess and manage patient health.

Human Longevity might end up leading this trend, as it’s further along in its R&D and commercialization processes, and it just announced plans to go public via a $1B SPAC deal. 

The Takeaway

Even if you’re not ready to embrace longevity imaging or don’t find SPAC listings as impressive as several years ago (or several months ago), it’s pretty clear that imaging will play a central role within proactive healthcare assessments and management. That could mean a lot more imaging exams and interpretations, a new source of incidental findings, and potentially greater longevity among patients who can afford these services.

Chest CT AI Efficiency

Posted on by Jake Fishman

A new AJR study out of the Medical University of South Carolina showed that Siemens Healthineers’ AI-RAD Companion Chest CT solution significantly reduced radiologists’ interpretation times. Considering that radiologist efficiency is often sacrificed in order to achieve AI’s accuracy and prioritization benefits, this study is worth a deeper look.

MUSC integrated Siemens’ AI-RAD Companion Chest CT into their PACS workflow, providing its radiologists with automated image analysis, quantification, visualization, and results for several key chest CT exams.

Three cardiothoracic radiologists were randomly assigned chest CT exams from 390 patients (195 w/ AI support), finding that the average AI-supported interpretations were significantly faster. . .

  • For the combined readers – 328 vs. 421 seconds 
  • For each individual radiologist – 289 vs. 344; 449 vs. 649; 281 vs. 348 seconds
  • For contrast-enhanced scans – 20% faster
  • For non-contrast scans – 24.2% faster
  • For negative scans – 26.4% faster
  • For positive scans without significant new findings – 25.7% faster
  • For positive scans with significant new findings – 20.4% faster

Overall, the solution allowed a 22.1% average reduction in radiologist interpretation times, or an hour per typical workday.

The authors didn’t explore the solution’s impact on radiologist accuracy, noting that AI accuracy has already been covered in plenty of previous studies. In fact, members of this same MUSC research team previously showed that AI-RAD Companion Chest CT identified abnormalities more accurately than many of its radiologists.

The Takeaway

Out of the hundreds of AI studies we see each year, very few have tried to measure efficiency gains and even fewer have shown that AI actually reduces radiologist interpretation times.
Given the massive exam volumes that radiologists are facing and the crucial role efficiency plays in AI ROI calculations, these results are particularly encouraging, and suggest that AI can indeed improve both accuracy and efficiency.

Siemens’ Big SPECT/CT Launch

Posted on by Jake Fishman

Siemens Healthineers kicked off SNMMI 2022 with the launch of its Symbia Pro.specta SPECT/CT, marking one of the biggest SPECT/CT rollouts we’ve seen in years.

The FDA and CE-cleared Symbia Pro.specta succeeds Siemens’ longstanding Symbia Intevo SPECT/CT (first launched in 2013) and is built to encourage nuclear medicine departments to finally replace their SPECT-only cameras and first-generation SPECT/CTs. That’s a big goal given SPECT/CT’s history of slow clinical adoption, and the Symbia Pro.specta will rely on a range of new and improved features to try to make it happen:

  • Integrated SPECT/CT – The Symbia Pro.specta boasts a fully integrated SPECT/CT, including an integrated user interface, while allowing providers to also use the system for SPECT or CT-only imaging.
  • myExam Companion – The Symbia Pro.specta adopts Siemens’ high-priority myExam Companion solution, which combines a new UI and automated guidance tools to make SPECT/CT operation far less manual, user dependent, and inconsistent (before and after image acquisition).
  • Diagnostic-Quality CT – Siemens’ new SPECT/CT is now available with 32 or 64-slice CTs (vs. Symbia Intevo’s 32-slice max) and a 70cm bore, while also offering standard Tin Filter and SAFIRE iterative CT reconstruction for low-dose CT imaging.
  • Advanced SPECT – The Symbia Pro.specta ships with standard automatic patient motion correction during SPECT exams (and optional cardiac exam motion correction), while its advanced quantification and energy level versatility allow it to support treatment response evaluations and theranostics usage.
  • Accessibility & Flexibility – Siemens leaned-in on the Symbia Pro.specta’s accessibility strengths, noting that it is sleek enough to fit into most existing SPECT rooms, and can support a range of clinical uses (cardiology, neurology, oncology, orthopedics) and patient types (pediatric, obese, and physically challenged).

The Takeaway

SPECT/CT’s slow path towards becoming a mainstream modality arguably has more to do with its adoption barriers and providers’ acceptance of the status quo than any doubts about its clinical benefits. Even though not all adoption barriers are hardware-dependent, the Symbia Pro.specta lowers enough of them to give nuclear imaging departments a good reason to consider moving up to a modern SPECT/CT.

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