Axial3D Insight

K250369

Axial Medical Printing Limited · cleared 2025-09-18 · product code QIH · Radiology

Premarket evidence — what FDA accepted

Device typesamd
source quote (p.4)
“Axial3D Insight is intended for use as a cloud-based service and image segmentation framework for the transfer of DICOM imaging information from a medical scanner to an output file. AxialML machine learning models are used to generate an initial segmentation of cases”
AlgorithmAxialML machine learning models
source quote (p.10)
“AxialML machine learning models are used to generate an initial segmentation of cases”
Adaptive (vs locked)No
source quote (p.11)
“The PCCP does not include provisions for implementation of adaptive algorithms that will continuously learn in the field.”
PCCPYes
source quote (p.2)
“FDA's substantial equivalence determination also included the review and clearance of your Predetermined Change Control Plan (PCCP). Axial3D Insight contains a Predetermined Change Control Plan (PCCP), which complies with Section 3308 of the Food and Drug Omnibus Reform Act (FDORA) of 2022, enacted on December 29, 2022.”
Cybersecurity addressedFDA source did not state this

Validation studies (6)

Reader study (MRMC)

n=12 cases

endpoints: assessment and feedback from the radiologists involved - all cases were scored within the acceptance criteria of 1 or 2a

standards: ACR RADPEER committee white paper with 2016 updates: revised scoring system, new classifications, self-review, and subspecialized reports." Journal of the American College of Radiology 14.8 (2017): 1080-1086.

Reader study (MRMC)

n=12 cases

endpoints: successful validation of the 3D models produced by Axial3D demonstrating the device outputs satisfied end user needs and indications for use

Standalone

n=38,870 images

Bench

sample size not stated

endpoints: Dice Coefficient; Pixel Accuracy; Area Under the Curve (AUC); Precision; Recall

Reader study (MRMC)

sample size not stated

endpoints: improved, equivalent or reduced performance based on the AxialML Model Design Input Specifications

Standalone

sample size not stated

endpoints: DICE; AUC; Precision; Accuracy; Recall

Reported performance (5 observations)

diceas written: “Dice Coefficient”stated without value
source quote (p.13)
“We utilize a suite of metrics including Dice Coefficient, Pixel Accuracy, Area Under the Curve (AUC), Precision, and Recall to provide a robust indicator of segmentation performance”
accuracyas written: “Pixel Accuracy”stated without value
source quote (p.13)
“We utilize a suite of metrics including Dice Coefficient, Pixel Accuracy, Area Under the Curve (AUC), Precision, and Recall to provide a robust indicator of segmentation performance”
ppvas written: “Precision”stated without value
source quote (p.13)
“We utilize a suite of metrics including Dice Coefficient, Pixel Accuracy, Area Under the Curve (AUC), Precision, and Recall to provide a robust indicator of segmentation performance”
sensitivityas written: “Recall”stated without value
source quote (p.13)
“We utilize a suite of metrics including Dice Coefficient, Pixel Accuracy, Area Under the Curve (AUC), Precision, and Recall to provide a robust indicator of segmentation performance”
accuracyas written: “Accuracy”stated without value
source quote (p.14)
“comparing the original model baseline vs modified model performance using a combination of quantitative metrics including DICE, AUC, Precision, Accuracy and Recall.”

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/K250369