Us2.ai’s Carolyn Lam MBBS, PhD after a recent study showed that echo AI can enable task shifting to new users and settings.

Imaging Wire Sponsors

annalise.ai  •  Arterys  •  Bayer  •  Blackford  •  Canon Medical  •  CARPL.ai  •  Change Healthcare  •  Enlitic  •  GE HealthCare  •  Hyperfine  •  Intelerad  •  Merge by Merative  •  Novarad  •  Nuance  •  Riverain Technologies  •  Siemens Healthineers  •  Subtle Medical  •  United Imaging  •  Us2.ai

We talk a lot about AI’s potential to expand ultrasound access, and the latest Imaging Wire Show reveals that ultrasound’s AI-driven expansion might go far beyond what many of us had in mind. Check out our discussion with Duke Health’s Madhav Swaminathan, MBBS, MD and Us2.ai’s Carolyn Lam MBBS, PhD and James Hare, to see how AI is democratizing echo exams.

Artificial Intelligence

RadNet’s Path to AI Profit

There’s plenty of bold forecasts about imaging AI’s long term potential, but short term projections of when AI startups will reach profitability are rarely disclosed and almost never bold. That’s why RadNet’s quarterly investor calls are proving to be such a valuable bellwether for the business of AI, and its latest briefing was no exception.

RadNet entered the AI arena with its 2020 acquisition of DeepHealth (~$20M) and solidified its AI presence in early 2022 by acquiring Aidence and Quantib (~$85M), but its AI business generated just $4.4M in revenue and booked a $24.9M in pre-tax loss in 2022. 

Those numbers are likely typical for similar-sized AI companies. However, RadNet’s path towards AI revenue growth and breakeven operations might outpace most of its peers.

• Looking into 2023, RadNet forecasts that its AI revenue will quadruple to between $16M and $18M, while its Adjusted EBITDA loss falls to between -$9M and -$11M.

• By 2024, RadNet expects its AI division to generate at least $25M to $30M in revenue, allowing it to achieve AI profitability for the first time.

So how exactly is RadNet going to achieve 6x AI revenue growth and reach profitability within just two years? Patients are going to pay for it. 

RadNet expects its new direct-to-patient Enhanced Breast Cancer Detection (EBCD) service to generate between $11M and $13M in 2023 revenue, representing up to 72% of RadNet’s overall AI revenue and driving much of its AI profitability improvements. And EBCD’s nationwide rollout won’t be complete until Q3.

RadNet’s 2024 AI revenue and profit improvements will again rely on “substantial” EBCD growth, with some help from its Aidence and Quantib operations. Those improvements would offset delayed AI efficiency benefits that RadNet has “yet to really realize” due in part to slow radiologist adoption.

Takeaway

The fact that RadNet expects to become one of imaging’s largest and most profitable AI companies within the next two years might not be surprising. However, RadNet’s reliance on patient payments to drive that growth is astounding, and it’s something to keep an eye on as AI vendors and radiology groups work on their own AI monetization strategies.

Bedside MRI’s Patient Acuity Impact

With rising patient acuity rates creating “unsustainable financial challenges,” health systems are looking for innovative ways to increase critical care throughput. A growing number of health systems are achieving this goal with the Hyperfine Swoop point-of-care MRI, which can eliminate risks associated with intrahospital transport and keeps more critical care team members in the ICU.

Purpose-Built for the Cloud

Imaging’s cloud evolution didn’t happen all at once. This Change Healthcare animation details the history of digital imaging architectures, and how cloud-native imaging improves stability and scalability, ease of management, patient data security, and operating costs.

The Wire

• Qure.ai to Distribute Therapixel: Qure.ai expanded its portfolio and business strategy, launching a global mammography AI distribution agreement with Therapixel. The alliance solidifies Qure.ai’s already comprehensive AI portfolio, adding Therapixel’s MammoScreen solution (2D & 3D) to its long list of homegrown AI tools. Qure.ai joins the small list of AI vendors who are partnering to achieve complete AI platforms (most-notably Aidoc), and Qure.ai’s mention of a “pipeline for future partnerships” suggests that more alliances are coming.

• Echo-Based 4D Flow Alternative: Purdue University researchers developed an echo-based method for measuring right ventricular intracardiac flow, which could serve as a more accessible alternative to CMRI-based 4D Flow. The team’s vendor-independent Doppler Velocity Reconstruction (DoVeR) technique measured RV intracardiac flow in 20 pediatric Repaired Tetralogy of Fallot patients (RTOF, who typically develop RV dysfunction), finding that DoVeR could indeed detect increased velocity and diastolic flow energy loss.

• RapidAI Adds RV/LV: RapidAI announced the FDA clearance of its new Rapid RV/LV ratio algorithm, adding pulmonary embolism risk stratification to its existing Rapid PE solution. RapidAI’s new RV/LV ratio algorithm analyzes CTPAs to assess right heart strain and help physicians prioritize PE cases, while equipping PE response teams with more actionable information. RapidAI is very on-trend with this launch, following similar RV/LV ratio additions to Viz.ai and Aidoc’s PE triage tools.

• Portable X-Ray AI Experiences: The Stop TB Partnership shared key insights from clinicians who have been using the latest ultra-portable X-ray scanners and TB AI tools on tuberculosis’ frontlines. The 26 global interviewees were generally “receptive” to the combined technologies, citing a handful of adoption drivers (portability, product integration, training/support) and a list of barriers (internet dependence, low batteries, suitability for larger patients, interoperability conflicts, challenges integrating/using AI). Considering the growing focus on AI-integrated portable imaging, this feedback seems applicable well beyond X-ray and TB.

• contextflow Adds Incidental PE: contextflow added incidental pulmonary embolism (IPE) detection to its ADVANCE Chest CT solution. contextflow’s IPE addition comes just days after Avicenna.AI launched its own IPE product, and both join longtime IPE detection company Aidoc. The new IPE detection functionality also adds to ADVANCE Chest CT’s support for lung cancer, ILD, COPD, thus bolstering contextflow’s position in the growing group of “comprehensive AI” startups focusing on detecting multiple abnormalities with one product.

• Us2.ai’s Platform Expansion: Us2.ai continued its AI platform expansion, making its AI-based automated echo reporting and measurement solution available on the Nuance Precision Imaging Network and TeraRecon’s Eureka Clinical AI platform. The new AI platform partnerships further expand Us2.ai’s channel following similar alliances with Blackford, Aidoc, and Viz.ai.

• Americans’ Healthcare AI Discomfort: Sixty percent of U.S. adults in a massive new Pew Research survey (n=11k) would feel uncomfortable if their healthcare providers relied on AI for their treatment, while 33% expect AI to lead to worse outcomes and 57% believe it will hurt patient-provider relationships. On the bright side, the respondents were more likely to believe AI would reduce provider mistakes than increase their mistakes (40% vs. 27%) and AI is more likely to improve care inequalities than exacerbate them (51% vs. 15%).

• Guadalupe Regional Ditches the Disk: PocketHealth added Guadalupe Regional Medical Center (GRMC) to the growing list of hospitals using its image sharing platform. The launch puts the San Antonio, Texas area medical center on a path towards ending CD-based image sharing, giving its patients the ability to access and share their medical imaging from any device, and allowing GRMC’s clinicians to view and import all imaging using their existing infrastructure. GRMC’s PocketHealth launch comes just a week after Southern Illinois Healthcare began offering the platform.

• TeraRecon’s European Expansion: TeraRecon announced the expansion of its Eureka Clinical AI platform to the EU, while promoting its initial list of EU-approved AI partners (Cercare Medical, Combinostics, Coreline Soft, Imaging Biometrics, Infervision, Radiobotics, & Riverain). TeraRecon might be best known for visualization, but it’s placed a major emphasis on the Eureka Clinical AI platform over the last year, launching a number of partnerships and now expanding from the US to Europe.

• Fetal Ultrasound AI Generalizability: A new Scientific Reports study suggests that fetal ultrasound deep learning models trained in high-resource regions can generalize to low-resource settings by adding limited local exams to the training data. The researchers trained a fetal ultrasound DL model with data from 1.8k Spanish patients, and evaluated it against 1k Danish patients, before using transfer learning to fine-tune the model with “a few” ultrasound images from Africa. The fine-tuned European model was able to “reach the same performance” when tested against ultrasound exams from five African countries. 

• Intelerad’s New CTO: Intelerad named 30-year imaging and technology veteran Robert Petrocelli as the company’s new Chief Technology Officer. Petrocelli will lead nearly 300 members of Intelerad’s global R&D, IT, and engineering teams, with a focus on scaling Intelerad’s solutions in the public cloud era and expanding its product lines. Intelerad has been actively bolstering its leadership team, previously appointing new leaders across its product, delivery, and professional services functions.

• NASA’s Butterfly Progress: NASA continues to test Butterfly Network’s handheld ultrasound to see if it can be recommended for use during space missions, after first launching the device into space in 2021. NASA is still reviewing results from Butterfly’s iQ initial space mission when it performed scans that were transmitted to Earth for review, and a new Butterfly handheld is expected to be aboard the Polaris Dawn mission later in March.

United Imaging Innovation

Check out this Imaging Wire Show with United Imaging’s Jeffrey Bundy and Mike Coulter, who detail their unique approach to medical imaging innovations. If you’re trying to figure out a simpler and more scalable way to run your imaging organization, this interview is a great way to start.

ClearRead CT’s Impact at Einstein Medical

This Riverain Technologies case study details how Einstein Medical Center adopted ClearRead CT enterprise-wide (all 13 CT scanners) and how the solution allowed Einstein radiologists to identify small nodules faster and more reliably.

RP and I-MED’s Perspectives on CARPL.ai

Watch industry leaders and trendsetters in radiology, Dr. Krishna Nallamshetty, CMO at Radiology Partners, and Dr. Ron Shnier, CMO at I-MED Radiology Network, share their perspectives on the CARPL platform, from clinical trials to clinical deployment at RSNA 2022.

The Resource Wire

• Arterys and Tempus have an ambitious vision for their combined diagnostic portfolios, starting with the integration of their radiology and pathology AI platforms. That’s a big deal for both physicians and researchers, and it’s detailed in this post from Tempus COO, Ryan Fukushima.

• Despite what we’ve been taught, acquiring high SNR MRIs doesn’t always mean longer scan times. Take Canon’s AiCE Deep Learning Reconstruction challenge and see if you can tell which of these brain MRI studies were performed in less scan time with the help of AiCE DLR.

• Proper patient data anonymization and deidentification is a must, but it can be challenging to do while still retaining clinical relevance. See why Enlitic proposes an AI-based approach to deidentify and anonymize healthcare data (both pixel data and metadata), and how it would be valuable to your organization.

• Is your in-office MRI service prepared for the future? See how three macro trends will impact your in-office orthopedic MRI service, and the MRI capabilities you’ll need in the future in this Siemens Healthineers report.

• Most of the time when people think of medical needles, they fret about the jab. But needles are often used for more serious and exacting procedures, including biopsies, ablations, osteosynthesis, and drainage. See how 3D Tech from IMACTIS and GE HealthCare ‘Augments the Radiologist’ for faster and more accurate needle placement.

• annalise.ai’s Annalise CXR solution detects up to 124 findings in a single chest X-ray. See how it detects such a wide range of abnormalities using these demo studies…  or upload your own CXR images.

• Intelerad just launched its Intelerad Cloud suite of imaging solutions, marking the culmination of over four years of cloud investments and acquisitions. The new Intelerad Cloud allows imaging organizations to adopt a variety of hybrid, public, or private cloud solutions based on their specific needs (including: PACS, VNA, image exchange storage, long-term archiving, disaster recovery, patient portal).

• Enterprise imaging is mainly adopted in the largest hospitals, but that doesn’t have to be the case. Check out this Imaging Wire Show featuring Novarad product leader Dave GrandPre, where we discuss what’s caused this divide and why smaller hospitals should adopt enterprise imaging.

• See how East Texas Medical Center reduced its abdominal CT contrast volumes by 30% after adopting Bayer Radiology’s contrast dose management software.

• Working out your AI business case? Check out this helpful Blackford Analysis post outlining the three steps to help you justify AI adoption. 

• Listen to a panel of medical imaging experts – including Merative CMO Dr. David Gruen, Wake Radiology CIO Matt Dewey, and Dr. Ajay Choudri, Chairman of Radiology at Capital Health Advanced Imaging – share their perspectives on the value of AI in improving patient outcomes, streamlining workflows and reducing physician burnout. Merative and TeraRecon, in partnership with AI Med, will host this panel discussion in a webinar on March 9th. Sign up here.

• What if AI could produce echo measurements that are comparable to expert physicians, but with less variability? That’s exactly what this Nature study revealed about Us2.ai’s solution, finding that its measurements had fewer and smaller differences compared to three human experts than when the experts were compared with each other.

• Clinical applications for healthcare AI are rapidly expanding, but many barriers are still preventing widespread adoption. This Nuance post explores a critical set of questions: what happens after an AI model goes into production, and how to know if it continues to perform as expected?

• When one of Precision Imaging Centers’ 3T MRIs wasn’t meeting their requirements, they implemented Subtle Medical’s SubtleMR solution, rather than purchasing a new scanner or an expensive upgrade. See how SubtleMR enhanced Precision’s patient throughput and comfort, without compromising image quality in this case study.