AI-CVD

K252029

HeartLung Corporation · cleared 2025-12-19 · product code QIH · Radiology

Premarket evidence — what FDA accepted

Device typesamd
source quote (p.7)
“AI-CVD® is a multi-module deep learning-based software platform developed to automatically segment and quantify a broad range of cardiovascular, pulmonary, musculoskeletal, and metabolic biomarkers from standard chest or whole-body CT scans.”
Algorithmmulti-module deep learning-based software platform utilizing a self-configuring neural network (nnU-Net) with additional supervised learning and iterative model enhancement based on human expert review and corrections.
source quote (p.7)
“AI-CVD® is a multi-module deep learning-based software platform developed to automatically segment and quantify a broad range of cardiovascular, pulmonary, musculoskeletal, and metabolic biomarkers from standard chest or whole-body CT scans. AI-CVD® system builds upon the open-source TotalSegmentator as its foundational segmentation framework, incorporating additional supervised learning and model training layers specific to each module's clinical task. The original TotalSegmentator architecture utilizes a self-configuring neural network known as nnU-Net³, which was trained on 1,139 total body CT cases for general anatomical segmentation and 447 coronary CT angiography (CCTA) scans for high-resolution cardiac structure segmentation. Input data included both contrast-enhanced and non-contrast ECG-gated CT scans with 1.0 mm slice thickness, enabling robust cross-modality performance. Where available, matched contrast and non-contrast scans from the same subjects were registered and aligned to optimize anatomical consistency during training. Each module within AI-CVD® was further refined based on human expert knowledge for each particular measurement. Custom datasets were constructed for coronary artery calcium scoring, aortic and valvular calcifications, cardiac chamber volumetry, epicardial and visceral fat quantification, bone mineral density assessment, liver fat estimation, muscle mass and quality, and lung attenuation analysis. For each module, iterative model enhancement was applied: human reviewers evaluated model-generated segmentations and corrected any inaccuracies, and these corrections were looped back into the training process to improve performance and generalizability.”
Adaptive (vs locked)No
PCCPNo
Cybersecurity addressedNo

Validation studies (8)

Retrospective clinical

n=913 scans · 3 site(s)

endpoints: comparative safety and effectiveness between expert manual measurements and both the automated Agatston CAC scores and the AI-derived relative density-based calcium scores

Retrospective clinical

sample size not stated

endpoints: Bland–Altman agreement analyses demonstrating acceptable bias and reproducibility across imaging protocols

Retrospective clinical

sample size not stated

endpoints: agreement analyses demonstrating reproducible mitral valve calcium quantification across imaging protocols

Retrospective clinical

sample size not stated

endpoints: acceptable agreement and reproducibility across non-contrast and contrast-enhanced CT acquisitions

Retrospective clinical

sample size not stated

endpoints: low bias and comparable performance across gated and non-gated CT acquisitions; reliability of AI-CVD® Aorta and Main Pulmonary Artery Volume and Diameter measurements

Retrospective clinical

sample size not stated

endpoints: acceptable reproducibility

Retrospective clinical

sample size not stated

endpoints: reproducible lung density measurements across gated and non-gated CT acquisitions

Retrospective clinical

sample size not stated

endpoints: acceptable reproducibility across imaging protocols

Reported performance (0 observations)

FDA source did not state a quantitative performance metric — non-reporting is itself the signal.

Each value carries its own analysis unit and task — never compare or pool across devices. Source: 510(k) summary PDF.

Predicate network

Postmarket — what happened after clearance

0
recalls in product code, 24mo
3
MAUDE reports in code, 12mo
—
vs code's own 3-yr baseline
0
drift signals on this device

Recall and MAUDE counts are product-code-level (reports aren't reliably attributable to one device); a recall is shown as device-attributed only when the recall record itself lists this clearance number. Signals are descriptive observables with sources — never a judgment that the device is unsafe or drifting. Snapshot 2026-07-08.

Reimbursement — how devices like this got paid

Not yet tracked — no payment pathway indexed for this clearance (the reimbursement corpus is a growing seed set).

Applicable FDA guidance — what the submission is measured against

FDA guidance documents and guiding principles applicable to 510(k) AI/ML devices in the Radiology panel. A curated reference index, not legal or regulatory advice — each item states its own status, and a draft is never binding.

Applicability is derived from the device's FDA advisory panel and pathway — cross-cutting guidances apply to every AI/ML device; panel-specific ones are flagged. Titles, dates, and links verified against fda.gov as of July 2026.

Constat Precedent · public FDA/CMS data · descriptive decision-support, not regulatory or reimbursement advice. Share this page: constat.dev/precedent/device/K252029