LARALAB

K242500

LARALAB GmbH · cleared 2025-04-16 · product code QIH · Radiology

Premarket evidence — what FDA accepted

Device typesamd
source quote (p.6)
“LARALAB is a stand-alone software developed to enable cardiologists, radiologists, heart surgeons and healthcare professionals (“Users”) to import, view and process Medical Images.”
Algorithmdeterministic Deep Learning Algorithms
source quote (p.6)
“In particular, the software generates pre-calculated automatic segmentations and measurements based on deterministic Deep Learning Algorithms.”
Adaptive (vs locked)No
source quote (p.6)
“Similar, the subject device implements artificial intelligence including nonadaptive”
PCCPFDA source did not state this
Cybersecurity addressedYes
source quote (p.11)
“An external cybersecurity assessment, including penetration testing, was successfully completed to evaluate the system's security posture. No medium or high-risk vulnerabilities were identified, and a strong overall security posture with no critical issues was confirmed. The testing was conducted in accordance with the FDA guidance document Cybersecurity in Medical Devices: Quality System Considerations and Content of Premarket Submissions.”

Validation studies (1)

Retrospective clinical

n=60 patients

endpoints: Dice score; Mean Surface Distance (MSD); 95th percentile Hausdorff distance (95% HD); mean bias; 95% limits of agreement; intraclass correlation coefficient (ICC)

Reported performance (2 observations)

diceas written: “Dice score”stated without valueCI 0.89 to 0.98
source quote (p.11)
“The Dice score analysis demonstrated that LARALAB achieved high accuracy in segmenting primary cardiovascular structures, with Dice scores ranging from 0.89 to 0.98 for major structures such as the LA, LV, RV, and RA.”
agreement_kappaas written: “ICC”0.75
source quote (p.11)
“The ICC values were above 0.75 for all measurements, indicating excellent agreement between the clinical experts' manual measurements.”

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/K242500