Optellum’s NTAPC

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

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

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

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

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.

SIIM 2022 Recap

The first in-person SIIM meeting since COVID hit is officially a wrap, delivering the latest in informatics and a family reunion vibe that might have surpassed any other imaging event. Here’s the top takeaways from the biggest imaging informatics conference of the year.

Crowds & Conversations – We understand there were 300 to 400 on-site attendees at SIIM 2022 (excluding exhibitors), with far more attendees in the educational sessions and afterparties than the exhibit hall booths. Still, it was clear that there’s no better place for informatics leaders and vendors to get together than SIIM.

Big Cloud – The shift to the cloud felt more inevitable than ever last week. The cloud was at the center of nearly every vendor and providers’ informatics roadmaps, while the AWS/GCP/Azure “healthcare cloud land grab” appears to be having an underrated influence on cloud adoption. That said, SIIM22’s cloud PACS conversations hadn’t changed much from previous years…

  • Everyone still agrees about the cloud’s security and administrative upsides
  • PACS vendors are still debating cloud native vs. cloud enabled (…and questioning whether providers know the difference or care as much as they do)
  • Nobody is willing to adopt cloud at the expense of PACS performance
  • And because of that, hybrid cloud remains the realistic starting point for many providers

Integrating AI – AI remained a major theme at SIIM, although most conversations focused on how to adopt and integrate AI (and then get ROI), rather than how AI can improve diagnosis. That probably explains why the exhibit hall featured far more AI distributors (AI marketplaces, PACS AI platforms, etc.) than AI developers, and it serves as a good reminder for AI vendors to continue improving their integration capabilities.

Productivity Hacks – Unsurprisingly, radiologist productivity was a common theme through the presentations and exhibit hall booths, ranging from the ultra-logical (fast PACS, administrative AI) to the ultra-ambitious (single-vendor unified imaging IT systems). 

Inconsistent Imaging – This might be old news to many of you, but I was amazed to learn how far many organizations are from achieving informatics best practice. I heard a lot about patched together workflows, outdated PACS versions, inconsistent site setups, antiquated imaging sharing, and narrowly-defined enterprise imaging. The silver lining to that is there’s plenty of room for improvement, but it also suggests that some imaging organizations will need a lot of work before they’re technologically prepared for the next-gen stuff we talked about all week.

The Takeaway

SIIM 2022 made it abundantly clear that there are seismic changes coming to imaging informatics, and even if those changes will probably take longer than some might hope, their impact might be greater than many of us expect. There’s also plenty of opportunities to improve radiology workflows in the short-term, and some of the smartest people in healthcare are ready to deliver these improvements.

Burdenless Incidental AI

A team of IBM Watson Health researchers developed an interesting image and text-based AI system that could significantly improve incidental lung nodule detection, without being “overly burdensome” for radiologists. That seems like a clinical and workflow win-win for any incidental AI system, and makes this study worth a deeper look.

Watson Health’s R&D-stage AI system automatically detects potential lung nodules in chest and abdominal CTs, and then analyzes the text in corresponding radiology reports to confirm whether they mention lung nodules. In clinical practice, the system would flag exams with potentially missed nodules for radiologist review.

The researchers used the AI system to analyze 32k CTs sourced from three health systems in the US and UK. They then had radiologists review the 415 studies that the AI system flagged for potentially missed pulmonary nodules, finding that it:

  • Caught 100 exams containing at least one missed nodule
  • Flagged 315 exams that didn’t feature nodules (false positives)
  • Achieved a 24% overall positive predictive value
  • Produced just a 1% false positive rate

The AI system’s combined ability to detect missed pulmonology nodules while “minimizing” radiologists’ re-reading labor was enough to make the authors optimistic about this type of AI. They specifically suggested that it could be a valuable addition to Quality Assurance programs, improving patient care while avoiding the healthcare and litigation costs that can come from missed findings.

The Takeaway

Watson Health’s new AI system adds to incidental AI’s growing momentum, joining a number of research and clinical-stage solutions that emerged in the last two years. However, this system’s ability to cross-reference radiology report text and apparent ability to minimize false positives are relatively unique. 

Even if most incidental AI tools aren’t ready for everyday clinical use, and their potential to increase re-read labor might be alarming to some rads, these solutions’ ability to catch earlier stage diseases and minimize the impact of diagnostic “misses” could earn the attention of a wide range of healthcare stakeholders going forward.

MRI Accessibility Advantage

Memorial MRI and Diagnostic’s COO Todd Greene starred in a recent Aunt Minnie webinar, detailing the role MRI accessibility plays in the Texas imaging group’s strategy, and sharing some very relevant takeaways for imaging providers and vendors.

Founded in 2001, Memorial MRI and Diagnostic (MMD) operates 16 imaging centers across Texas, including eight in greater Houston and eight Dallas-area locations added through its 2021 acquisition of Prime Diagnostic Imaging. 

  • MMD’s strategy focuses on integrating its imaging centers within their local communities, making patient access and referring physician relationships particularly important.

In addition to proximity to patients, MMD’s MRI accessibility strategy historically focused on maintaining a fleet of open bore 1.5T MRI scanners to accommodate larger and claustrophobic patients. 

  • This is especially important given that many of MMD’s patients are “Texas sized” or don’t realize they’re claustrophobic until the scan begins. 

That strategy started to change when MMD installed United Imaging’s 3T uMR OMEGA ultra-wide-bore (75 cm), allowing it to scan larger and claustrophobia-prone patients (plus all other patients) without open MRIs’ scan speed and image quality tradeoffs. 

  • The uMR OMEGA was MMD’s first 3T MRI at any of MMD’s imaging centers, although Greene expects its patient and referrer-friendly advantages to drive a continued shift towards wide-bore 3T MRI systems.

Greene also detailed Memorial MRI’s alliance with United Imaging (the webinar’s sponsor), specifically highlighting the scalability of UIH’s “Software for Life” (scanners automatically updated with future software) and “All-In” (scanners include all possible features/packages) policies.

As the webinar wrapped up, Greene warned imaging centers not to blindly rely on what has worked in the past, predicting that “ease of access is what is going to shape the future of healthcare.” 

The Takeaway

We get plenty of insights from the medical center side of radiology, but it’s still rare to hear from imaging center chains. That makes MDD’s insights particularly useful for the many regional imaging providers who’d like to improve MRI accessibility (without open MRI’s tradeoffs) and for MRI OEMs looking to drive 3T MRI adoption in an imaging provider segment that historically favored 1.5T systems.

Autonomous & Ultrafast Breast MRI

A new study out of the University of Groningen highlighted the scanning and diagnostic efficiency advantages that might come from combining ultrafast breast MRI with autonomous AI. That might make some readers uncomfortable, but the fact that autonomous AI is one of 2022’s most controversial topics makes this study worth some extra attention.

The researchers used 837 “TWIST” ultrafast breast MRI exams from 488 patients (118 abnormal breasts, 34 w/ malignant lesions) to train and validate a deep learning model to detect and automatically exclude normal exams from radiologist workloads. They then tested it against 178 exams from 149 patients from the same institution (55 abnormal, 30 w/ malignant lesions), achieving a 0.81 AUC.

When evaluated at a conservative 0.25 detection error threshold, the DL model:

  • Achieved 98% sensitivity and negative predictive values
  • Misclassified one abnormal exam as normal (out of 55)
  • Correctly classified all exams with malignant lesions
  • Would have reduced radiologists’ exam workload by 6.2% (-15.7% at breast level)

When evaluated at a 0.37 detection error threshold, the model:

  • Achieved 95% sensitivity and a 97% negative predictive value (still high)
  • Misclassified three abnormal exams (3 of 55), including one malignant lesion
  • Would have reduced radiologists’ exam workload by 15.7% (-30.6% at breast level)

These radiologist workflow improvements would complement the TWIST ultrafast MRI sequence’s far shorter magnet time than current protocols (2 vs. 20 minutes), while the DL model could further reduce scan times by automatically ending exams once they are flagged as normal. 

The Takeaway

Even if the world might not be ready for this type of autonomous AI workflow, this study is a good example of how abbreviated MRI protocols and AI could be able to improve both imaging team and radiologist efficiency. It’s also the latest in a series of studies exploring how AI could exclude normal scans from radiologist workflows, suggesting that the development and design of this type of autonomous AI will continue to mature.

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