Core lab reads with AI enablement, signed by human experts.
InVision's echocardiography core lab pairs peer-reviewed state-of-the-art AI first-pass measurement and interpretation with final adjudication sign-off by expert sonographers and cardiologists — compressing turnaround while tightening the reproducibility that clinical decisions and trial endpoints depend on.
Echo reads vary. Patients and trials are impacted by the variance.
Quantitative echocardiography is the workhorse of cardiac imaging, yet the same study read by two qualified humans — or by the same human twice — can yield materially different numbers. For a clinical decision that's noise; for a trial endpoint it's lost statistical power and slipped timelines. Our AI-enabled core lab exists to remove that variance.
Inter- and intra-reader drift
Manual LVEF and Doppler quantification carry real reader-to-reader and test–retest variability. In a randomized trial, expert sonographer reads required substantial cardiologist correction in 27.2% of studies while AI reads required adjustment in 16.8% of studies.
Turnaround that gates decisions
Comprehensive interpretation consumes an hour of combined technician and physician time per exam. Backlogs delay cardio-oncology surveillance, heart-failure titration, and trial interim analyses.
Cost and scale ceilings
Throughput is bounded by scarce expert time. Scaling a traditional core lab means linearly adding readers — and re-introducing the very variance a core lab was meant to eliminate.
AI does the first pass. Experts do the deciding.
Every study moves through the same five-stage pipeline. AI handles the repetitive, time-intensive measurement work at machine speed and consistency; credentialed humans own judgment, edge cases, and the signature. No read leaves the lab without a cardiologist's name on it.
Intake & quality control
AutomatedStudies arrive as standard DICOM from any scanner or PACS. The pipeline auto-de-identifies (HIPAA-safe), classifies every view, and flags inadequate image quality before measurement — the gatekeeping a trial run-in period provides, applied to every study.
AI first-pass measurement & draft read
EchoNet-Measurements + EchoPrimeSemantic-segmentation models auto-measure 18 standard B-mode and Doppler parameters; a multi-view vision–language foundation model produces a holistic, study-level draft interpretation across chambers, valves, and function. Deterministic by construction — the same pixels always yield the same numbers.
Sonographer adjudication
RDCS / RCSAn expert cardiac sonographer reviews every AI tracing and value against the images, corrects where clinical judgment differs, and confirms protocol adherence. The AI starts the work at a high baseline; the sonographer guarantees it.
Cardiologist finalization & sign-off
Level III readerA board-certified cardiologist over-reads the adjudicated study, finalizes the interpretation, and signs. In randomized testing, cardiologists made fewer substantial changes to AI-initiated reads and over-read them in less time than sonographer-initiated reads.
Delivery, locking & audit trail
FinalThe signed report is delivered as structured data, then locked and versioned with a complete edit history — designed for 21 CFR Part 11-aligned audit trails, blinded read workflows, and direct integration with sponsor EDC, your PACS, and Epic.
Faster to a finalized read — enabled by AI.
The slowest part of an echo read is the measurement and report generation, not the judgment. By moving measurement and first-draft interpretation upstream to AI, the human starts from a near-complete read and spends their time verifying rather than constructing. The randomized evidence is directional and clear: AI-initiated reads took cardiologists less time to finalize.
Illustrative of where time is spent in the workflow, not a published trial endpoint. Service-level turnaround is committed per engagement — typical clinical reads target 48–72 hours with premium processing; trial reads run to protocol, with AI pre-reads accelerating site-query resolution and interim analyses.
The same study reads the same way, every time.
Reproducibility is the core lab's product. A deterministic AI first-pass dramatically reduces test–retest variability at the measurement step by construction — identical input yields identical output — and anchors every human reader to the same starting point. Across 18 parameters, AI measurements matched expert sonographers with an R² of 0.97–0.99; in randomized testing, AI-initiated reads were also more concordant with prior independent cardiologist reads.
Conventional independent reads
Reader-to-reader and test–retest drift widen the measurement band — the variance a sponsor's power calculation must absorb.
AI first-pass + expert sign-off
A deterministic starting point plus expert sign-off collapses the band — fewer corrections, higher concordance, less noise to power through.
The reads rest on a decade of peer-reviewed research.
The core lab is not a wrapper around a closed model. It runs on the published, externally validated, open-source research that defined AI in echocardiography — including the first and only randomized trial of AI in cardiology. The headline numbers are reported exactly as the journals published them.
EchoPrime
A multi-view, view-informed, video-based vision–language foundation model that synthesizes every view in a comprehensive study into one holistic interpretation — the engine behind the draft read. Contrastive learning plus retrieval-augmented interpretation weight each video by anatomical relevance, the way a cardiologist does.
EchoNet-Measurements
Deep-learning semantic-segmentation models that automate the measurement work itself — 9 B-mode and 9 Doppler parameters, from LV internal diameter and septal thickness to TR Vmax and septal e′. Validated against expert sonographer annotations at two academic centers, with accuracy on par with the humans it accelerates.
The first randomized trial of AI in cardiology
A blinded, randomized, non-inferiority trial of AI versus sonographer initial assessment of LVEF, with final interpretation by a blinded cardiologist. From 3,769 studies screened, 3,495 analyzable transthoracic echocardiograms were independently re-read by 25 sonographers (mean 14.1 years' experience) and 10 cardiologists (mean 12.7 years). The AI-initiated arm was both non-inferior and superior on the primary endpoint — and cardiologists, unable to reliably tell which arm they were reading, changed AI reads less often and finalized them faster.
Selected publications
References
- He B, Kwan AC, Cho JH, et al. Blinded, randomized trial of sonographer versus AI cardiac function assessment. Nature. 2023;616:520–524. ClinicalTrials.gov NCT05140642. PubMed 37020027.
- Vukadinovic M, Chiu I-M, Tang X, et al. Comprehensive echocardiogram evaluation with view-primed vision–language AI. Nature. 2025. PubMed 41219498.
- Sahashi Y, Ieki H, Yuan V, et al. Artificial intelligence automation of echocardiographic measurements. J Am Coll Cardiol. 2025;86(13):964–978. PubMed 40914895.
- Christensen M, Vukadinovic M, Yuan N, Ouyang D. Vision–language foundation model for echocardiogram interpretation. Nat Med. 2024;30:1481–1488. PubMed 38689062.
- Ouyang D, He B, Ghorbani A, et al. Video-based AI for beat-to-beat assessment of cardiac function. Nature. 2020;580:252–256. PubMed 32269341.
A core lab built for endpoints.
In a cardiovascular trial, echo endpoints are only as good as their measurement variance. Lower variance is not a nicety — it directly raises the signal-to-noise ratio for detecting a treatment effect, and because required sample size scales with the square of the endpoint's standard deviation, tightening the read can lower enrollment or raise power. A deterministic AI first-pass, adjudicated by blinded experts, is a direct lever on that variance.
The measures trials live on
LVEF and GLS for cardiotoxicity and heart-failure trials; diastolic parameters; valve severity; chamber dimensions; RV function; estimated pulmonary pressures — quantified consistently across every site and every visit.
Variance you can bank
A deterministic first-pass removes test–retest noise at the measurement step and anchors blinded adjudicators to one baseline, shrinking endpoint variance — fewer patients for the same power, or more power for the same N.
Reads that don't gate the DSMB
AI pre-reads compress the measurement bottleneck, accelerating site-query resolution, interim analyses, and database lock — without trading away the blinded, expert-finalized rigor a regulator expects.
Blinded, locked, traceable
Blinded read workflows, locked and versioned outputs, and a complete edit trail behind every signed value — designed for 21 CFR Part 11-aligned audit trails and direct delivery to sponsor EDC.
Read it the same in year three
The same version-locked model and protocol re-read a baseline study identically two years later — eliminating drift across long enrollment windows and between interim and final analyses.
Evidence a reviewer recognizes
The underlying models are published in Nature, JACC, and Nature Medicine and open-sourced — so the methods behind your endpoints are inspectable, citable, and externally validated rather than a black box.
AI assists. Experts decide.
The model accelerates the read; it never owns it. Accountability stays with the credentialed humans whose names sign the report.
AI never signs a read
Every measurement is verified by an expert cardiac sonographer and every interpretation is finalized by a board-certified cardiologist. The AI output is a draft and a starting point — never the deliverable.
Credentialed readers, defined competency
Reads are performed by RDCS/RCS-credentialed sonographers and Level III-equivalent cardiologist readers, with documented training and ongoing concordance monitoring.
The human can always overrule
When clinical judgment differs from the AI, the human value wins and the divergence is logged. The edit trail captures exactly what the model proposed and what the expert changed.
Quality control on the loop
Sampled over-reads, inter-reader concordance tracking, and model-drift monitoring keep both the AI and the humans inside agreed performance bounds over time.
“We aspire to democratize expert knowledge to more clinicians, and ensure that excellent care is available to every patient — in every clinical setting.”
Bring your studies to a faster, more reproducible core lab.
Schedule a 30-minute briefing with our clinical and operations teams. We'll walk through read protocols, turnaround commitments, the evidence base, and how blinded, expert-finalized AI reads fit your service line or trial — tailored to your endpoints.
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