inHEART Models

K231683

inHEART, SAS · cleared 2024-02-29 · product code QIH · Radiology

Premarket evidence — what FDA accepted

Device typesamd
source quote (p.5)
“inHEART MODELS AI: a medical image processing software for automatic 3D modelling, used to pre-process medical images (acquired only by CT devices). This software module uses a machine-learning based approach with the following characteristics:”
AlgorithmMachine learning algorithm (UNet) is trained applying data augmentation (including patch mirroring) and regularization (including Instance Normalization, Dropout, Data Augmentation, Weight Initialization, Leaky ReLU Activation) methods. Loss functions (soft dice loss and binary cross entropy) and optimization methods (stochastic gradient descent) are used during learning over a fixed number of 1000 epochs, each consisting of 250 iterations with a batch size of 2.
source quote (p.5)
“Training process: Machine learning algorithm (UNet) is trained applying data augmentation (including patch mirroring) and regularization (including Instance Normalization, Dropout, Data Augmentation, Weight Initialization, Leaky ReLU Activation) methods. Loss functions (soft dice loss and binary cross entropy) and optimization methods (stochastic gradient descent) are used during learning over a fixed number of 1000 epochs, each consisting of 250 iterations with a batch size of 2.”
Adaptive (vs locked)FDA source did not state this
PCCPFDA source did not state this
Cybersecurity addressedYes
source quote (p.6)
“IEC 81001-5-1 Edition 1.0 2021-12, Health software and health IT systems safety, effectiveness and security – Part 5-1 Security – Activities in the product life cycle”

Validation studies (1)

Retrospective clinical

n=100 cases

endpoints: Dice coefficient; Average Symmetric Surface Distance (ASSD); volumetric analysis

standards: ISO, 14971 Third Edition 2019-12, Medical devices – Application of Risk Management to medical devices, ISO, 15223-1 Third Edition 2016-11-01, Medical devices – Symbols to be used with information to be supplied by the manufacturer – Part 1: General requirements, IEC, 62304 Edition 1.1 2015-06 CONSOLIDATED VERSION: Medical device software – Software life cycle processes, IEC, 62366-1 Edition 1.1 2020-06 CONSOLIDATED VERSION: Medical devices - Part 1 : application of usability engineering to medical devices, IEC, /TR 80002-1 Edition 1.0 2009-09, Medical device software – Part 1 : Guidance of the application of ISO 14971 to medical devices, IEC, 82304-1 Edition 1.0 2016-10, Health software – Part 1 : General requirements for product safety, IEC 81001-5-1 Edition 1.0 2021-12, Health software and health IT systems safety, effectiveness and security – Part 5-1 Security – Activities in the product life cycle

Reported performance (1 observation)

diceas written: “Average Dice score”0.94
source quote (p.9)
“Average Dice score is 0.94, average ASSD is 1.17mm, average volume variations is 9,18mL and 7%.”

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/K231683