We hear a lot about AI’s potential to expand ultrasound to far more users and clinical settings, and a new study out of Singapore suggests that ultrasound’s AI-driven expansion might go far beyond what many of us had in mind.
The PANES-HF trial set up a home-based echo heart failure screening program that equipped a team of complete novices (no experience with echo, or in healthcare) with EchoNous’s AI-guided handheld ultrasound system and Us2.ai’s AI-automated echo analysis and reporting solution.
After just two weeks of training, the novices performed at-home echocardiography exams on 100 patients with suspected heart failure, completing the studies in an average of 11.5 minutes per patient.
When compared to the same 100 patients’ NT-proBNP blood test results and reference standard echo exams (expert sonographers, cart-based echo systems, and cardiologist interpretations), the novice echo AI pathway…
- Yielded interpretable results in 96 patients
- Improved risk prediction accuracy versus NT-proBNP by 30%
- Detected abnormal LVEF <50% scans with an 0.880 AUC (vs. NT-proBNP’s 0.651-0.690 AUCs)
- Achieved good agreement with expert clinicians for LVEF<50% detection (k=0.742)
These findings were strong enough for the authors to suggest that emerging ultrasound and AI technologies will enable healthcare organizations to create completely new heart failure pathways. That might start with task-shifting from cardiologists to primary care, but could extend to novice-performed exams and home-based care.
Considering the rising prevalence of heart failure, the recent advances in HF treatments, and the continued sonographer shortage, there’s clearly a need for more accessible and efficient echo pathways — and this study is arguably the strongest evidence that AI might be at the center of those new pathways.
A Cedars-Sinai-led team developed an echocardiography AI model that was able to accurately assess coronary artery calcium buildup, potentially revealing a safer, more economical, and more accessible approach to CAC scoring.
The researchers used 1,635 Cedars-Sinai patients’ transthoracic echocardiogram (TTE) videos paired with their CT-based Agatston CAC scores to train an AI model to predict patients’ CAC scores based on their PLAX view TTE videos.
When tested against Cedars-Sinai TTEs that weren’t used for AI training, the TTE CAC AI model detected…
- Zero CAC patients with “high discriminatory abilities” (AUC: 0.81)
- Intermediate patients “modestly well” (≥200 scores; AUC: 0.75)
- High CAC patients “modestly well” (≥400 scores; AUC: 0.74)
When validated against 92 TTEs from an external Stanford dataset, the AI model similarly predicted which patients had zero and high CAC scores (AUCs: 0.75 & 0.85).
More importantly, the TTE AI CAC scores accurately predicted the patients’ future risks. TTE CAC scores predicted one-year mortality similarly to CT CAC scores, and they even improved overall prediction of low-risk patients by downgrading patients who had high CT CAC scores and zero TTE CAC scores.
CT-based CAC scoring is widely accepted, but it isn’t accessible to many patients, and concerns about its safety and value (cost, radiation, incidentals) have kept the USPSTF from formally recommending it for coronary artery disease surveillance. We’d need a lot more research and AI development efforts, but if TTE CAC AI solutions like this prove to be reliable, it could make CAC scoring far more accessible and potentially even more accepted.
Exo took a big step towards making its handheld ultrasounds easier to use and adopt, acquiring AI startup Medo AI. Although unexpected, this is a logical and potentially significant acquisition that deserves a deeper look…
Exo plans to integrate Medo’s Sweep AI technology into its ultrasound platform, forecasting that this hardware-software combination will streamline Exo POCUS adoption among clinicians who lack ultrasound training/experience.
- Medo’s automated image acquisition and interpretation software has clearance for two exams (thyroid nodule assessments, developmental hip dysplasia screening), and it has more AI modules in development.
Exo didn’t disclose acquisition costs, but Medo AI is relatively modest in size (23 employees on LinkedIn, no public info on VC rounds) and it’s unclear if it had any other bidders.
- Either way, Exo can probably afford it following its $220M Series C in July 2021 (total funding now >$320m), especially considering that Medo’s use case directly supports Exo’s core strategy of expanding POCUS to more clinicians.
Some might point out how this acquisition continues 2022’s AI shakeup, which brought three other AI acquisitions (Aidence & Quantib by RadNet; Nines by Sirona) and at least two strategic pivots (MaxQ AI & Kheiron).
- That said, this is the first AI acquisition by a hardware vendor and it doesn’t represent the type of segment consolidation that everyone keeps forecasting.
Exo’s Medo acquisition does introduce a potential shift in the way handheld ultrasound vendors might approach expanding their AI software stack, after historically focusing on a mix of partnerships and in-house development.
Handheld ultrasound is perhaps the only medical imaging product segment that includes an even mix of the industry’s largest OEMs and extremely well-funded startups, setting the stage for fierce competition.
That competition is even stronger when you consider that the handheld ultrasound segment’s primary market (point-of-care clinicians) is still early in its adoption curve, which places a big target on any products that could make handheld ultrasounds easier to use and adopt (like Medo AI).