|
AI Crosses the Chasm | Smartphone Screening
August 21, 2022
|
|
|
|
|
Together with
|
|
|
|
“By age 30 in radiology, you should have: 1) PTSD from the sound of the phone ringing on call; 2) had barium sprayed onto you during a fluoro case; 3)accidently said “period” at the end of a sentence at least once while talking to a normal person”
|
|
A tweet from radiologist Alex D. Bibbey, MD.
|
|
|
|
Despite plenty of challenges, Signify Research forecasts that the global imaging AI market will nearly quadruple by 2026, as AI “crosses the chasm” towards widespread adoption. Here’s how Signify sees that transition happening:
Market Growth – After generating global revenues of around $375M in 2020 and $400M and 2021, Signify expects the imaging AI market to maintain a massive 27.6% CAGR through 2026 when it reaches nearly $1.4B.
Product-Led Growth – This growth will be partially driven by the availability of new and more-effective AI products, following:
- An influx of new regulatory-approved solutions
- Continued improvements to current products (e.g. adding triage to detection tools)
- AI leaders expanding into new clinical segments
- AI’s evolution from point solutions to comprehensive solutions/workflows
- The continued adoption AI platforms/marketplaces
The Big Four – Imaging AI’s top four clinical segments (breast, cardiology, neurology, pulmonology) represented 87% of the AI market in 2021, and those segments will continue to dominate through 2026.
VC Support – After investing $3.47B in AI startups between 2015 and 2021, Signify expects that VCs will remain a market growth driver, while their funding continues to shift toward later stage rounds.
Remaining Barriers – AI still faces plenty of barriers, including limited reimbursements, insufficient economic/ROI evidence, stricter regulatory standards (especially in EU), and uncertain future prioritization from healthcare providers and imaging IT vendors.
The Takeaway
2022 has been a tumultuous year for AI, bringing a number of notable achievements (increased adoption, improving products, new reimbursements, more clinical evidence, big funding rounds) that sometimes seemed to be overshadowed by AI’s challenges (difficult funding climate, market consolidation, slower adoption than previously hoped).
However, Signify’s latest research suggests that 2022’s ups-and-downs might prove to be part of AI’s path towards mainstream adoption. And based on the steeper growth Signify forecasts for 2025-2026 (see chart above), the imaging AI market’s growth rate and overall value should become far greater after it finally “crosses the chasm.”
|
|
|
BSW and QEHB’s Symbia Pro.specta Upgrades
Learn why Texas’ Baylor Scott & White Medical Center and the UK’s Queen Elizabeth Hospital Birmingham decided to upgrade their SPECT-only cameras and first-generation SPECT/CTs to Siemens Healthineers’ Symbia Pro.specta SPECT/CT and how they’ve benefited since then.
|
|
- Smartphone Stroke Screening: New research shows that smartphone videos paired with video-based motion analysis (VMA) could be “excellent” for carotid artery stenosis screening (CAS; usually done w/ ultrasound). The researchers captured 30‐second smartphone videos of 202 patients’ necks (54% w/ CAS) and then used VMA to quantify skin motion changes, identifying CAS with a 0.914 AUC. This technique joins an emerging list of imaging-adjacent smartphone diagnostics, which also includes concussions/TBI (patient eye videos) and COVID (patient cough audio).
- United Imaging’s Big IPO: United Imaging Healthcare made its debut today on Shanghai’s STAR Market exchange as part of a massive $1.62B IPO, which equaled a 78x-multiple of UIH’s earnings (STAR’s average is 35x) and became STAR’s biggest IPO of 2022. United Imaging’s influx of public capital will be directed towards its R&D, manufacturing, and marketing operations, suggesting that the already-disruptive OEM is about to become even more aggressive.
- Evaluating AI Value Propositions: A new European Radiology paper provided a detailed and refreshingly visual breakdown of how imaging AI vendors position and legitimize their solutions. Analysis of 393 AI apps from 133 vendors found that most AI value propositions focus on quality-of-care (31%), efficiency (18%), or both quality and efficiency (28%). AI vendors’ efforts to legitimize these messages focused on their clinical / research partnerships (75%), regulatory approvals (72%), team expertise (56%), and clinical implementations (53%). However, very few vendors revealed the sources or size of their training datasets (8% & 10%).
- Pro Medicus’ Plans: On the heels of Pro Medicus/Visage Imaging’s “most successful” fiscal year ever (revenue +38%, net profit +44%), CEO Sam Hupert shared new insights into the company’s future strategy. Hupert highlighted Pro Medicus’ ample remaining growth in the US (still has ~5% share, can expand beyond academic centers), while suggesting that an economic downturn could make acquisitions a larger part of its strategy (target adjacent imaging startups… like AI, not PACS competitors). Meanwhile, Pro Medicus’ R&D efforts will continue to prioritize expanding to “other ologies,” including adding cardiology to its existing imaging platform.
- Demographics and Mammography Perceptions: A Duke study highlighted the major influence demographics have on how patients perceive their mammography screening reports. Out of 178 women (71% White), Black patients were less likely to be satisfied with report quality (p=0.043), but more likely to trust their report’s findings if their radiologist was also Black (p=0.037). Meanwhile, participants without any college education were less likely to be satisfied with their report quality (p=0.020) or feel that their radiologist cares about his/her patients (p=0.037).
- MediMatrix Acquired: Mobile imaging software company MediMatrix (formerly WebInterstate) was acquired by ASG, a PE-backed company that’s assembling a portfolio of SaaS companies in targeted verticals (including healthcare). MediMatrix develops software used in mobile imaging operations (technologist dispatch, order management, image sharing, billing), which seems to have gained new momentum with the current home care trend. ASG plans to expand MediMatrix’s business organically and through future healthcare acquisitions.
- Brain Tumor MRI AI: A new JAMA study detailed an MRI-based AI model that improved classification and diagnosis of 18 types of brain tumors, both when used independently or by neuroradiologists. The researchers trained and tested the AI model with MRIs from 37.8k and 1.3k patients, finding that it outperformed nine neurorads (accuracy: 73.3% vs. 60.9%; sensitivity 88.9% vs. 53.4%; specificity 96.3% vs. 97.9%). Perhaps more importantly, neurorads who used AI were more accurate than neurorads without AI (75.5% vs. 63.5%).
- Imaging Heart Disease in Women: A review in JACC highlighted the need to consider women’s unique cardiac imaging features and disease physiology. The authors emphasize that women have a unique ischemic heart disease phenotype (less calcified lesions, more nonobstructive plaques, higher prevalence of microvascular disease) and that imaging tests tend to be less accurate with women (summarized wonderfully in this image).
- COVID Time-to-Death AI: Japanese researchers developed a chest X-ray and clinical data-based AI model that accurately predicted COVID patients’ likelihood of dying within days of their admission. The researchers combined a DeepSurv model (based on the Cox hazards model) and a deep learning CNN, and trained and tested it using data from 1.35k COVID inpatients. The multi-modal AI system predicted time-to-death more accurately than versions of the same AI model that used only CXR images, only clinical data, or only the Cox proportional hazards model (c-index 0.82 vs. 0.77 & 0.70 & 0.71).
- Scientific Misconduct: Authors of an Academic Radiology study identified 192 retracted medical imaging papers that were published between 1984 and 2021, noting a steady rise in retractions since 2000. The retracted articles were most often published by teams based in China, the US, Japan and South Korea (31.3%, 12.5%, 7.3% and 6.3%), and were most commonly pulled due to duplication of other papers, plagiarism, and data concerns (7.1%, 6.8% and 5.4%) – with “scientific misconduct” identified in 55.7% of the retracted articles.
- Considering Radiation Risks: A new study in the European Journal of Radiology found that most referring physicians consider radiation risk when ordering CT scans, and they’re open to regulations that would control the number of CTs patients receive annually. In an email survey of 505 referring physicians from 24 countries, 58% understood that current regulations do not limit patients’ annual CT volumes, but 69% are open to regulations controlling CT volumes (36% “maybe,” 33% “yes”).
|
|
How Aster DM Healthcare Leveraged CARPL
See how healthcare leader Aster DM Healthcare leveraged the CARPL platform to connect its doctors, data scientists, and imaging workflows, and support its AI projects and development infrastructure.
|
|
- Ready to improve your mammography workflows? Arterys is the first and only cloud-native Breast AI provider, and its solution dramatically reduces 3D Mammography reading times, while supporting breast cancer detection, density measurements, and personalized risk assessments.
- 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.
- With radiation dose management now largely considered best practice, this Bayer white paper details the top five benefits of adopting contrast dose management.
- Ready to address burnout on your team? This Novarad report details the main burnout drivers within imaging teams, and the steps radiology leaders can take to prevent burnout.
- How much time are you spending on interruptions? Based on this Enlitic report, it could be quite a lot, and data governance can eliminate many of them.
|
|
|
|
|
