Contour ProtégéAI+

K253270

Mim Software, Inc. · cleared 2026-03-27 · product code QKB · Radiology

Premarket evidence — what FDA accepted

Device typesamd
source quote (p.4)
“Trained medical professionals use Contour ProtégéAI as a tool to assist in the automated processing of digital medical images of modalities CT and MR, as supported by ACR/NEMA DICOM 3.0.”
Algorithmmachine-learning algorithms, including 3D U-Net neural network models, with some models using a modular, multi-stage architecture employing three distinct neural networks
source quote (p.4)
“Creation of contours using machine-learning algorithms for applications including, but not limited to, quantitative analysis, aiding adaptive therapy, aiding image registration, transferring contours to radiation therapy treatment planning systems, and archiving contours for patient follow-up and management.Two different configurations of neural network architectures are used for training the 4.3.0 MR Brain model (A) and the 5.0.0 CT Male Pelvis model (B): (A) A single 3D U-Net is trained to produce all of the contours for a given model with the entire input image as an input. (B) Multiple 3D U-Net models are trained, with each tailored to a specific anatomical structure or groups of structures (complexes) within a model bundle. Rather than relying on a single network, this modular, multi-stage architecture employs three distinct neural networks to progressively localize and segment the target regions.”
Adaptive (vs locked)No
PCCPYes
source quote (p.2)
“FDA's substantial equivalence determination also included the review and clearance of your Predetermined Change Control Plan (PCCP).”
Cybersecurity addressedFDA source did not state this

Validation studies (1)

Retrospective clinical

n=189 images · 7 site(s)

endpoints: Contour creation time savings; Clinical quality contour generation; Dice score; MDA score; Cumulative Added Path Length (APL); User beta testing evaluation on a five-point scale

standards: ISO 13485, AAPM recommendations

Reported performance (1 observation)

diceas written: “Dice Coefficient (Brain)”0.97CI ± 0.01
source quote (p.14)
“0.97 ± 0.01”

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
0
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/K253270