Axial3D Insight
K250369Axial Medical Printing Limited · cleared 2025-09-18 · product code QIH · Radiology
Premarket evidence — what FDA accepted
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.”
source quote (p.10)
“AxialML machine learning models are used to generate an initial segmentation of cases, however the output of these models is not used in isolation to produce the final 3D patient specific model.”
source quote (p.11)
“The PCCP does not include provisions for implementation of adaptive algorithms that will continuously learn in the field.”
source quote (p.11)
“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. Modifications to the AxialML models of Axial3D Insight will be made in accordance with its Predetermined Change Control Plan (PCCP). The PCCP provides a description of the device's planned modifications, a modification protocol to test, verify, validate, and implement the modifications in a manner that ensures the continued safety and effectiveness of the device.”
Validation studies (3)
Reader study (MRMC)
n=12 cases
endpoints: scoring within acceptance criteria
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 3D models demonstrating device outputs satisfied end user needs and indications for use
Standalone
n=38,870 images
endpoints: Dice Coefficient; Pixel Accuracy; Area Under the Curve (AUC); Precision; Recall
Reported performance (4 observations)
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, comparing the candidate modified AxialML model against the model version from the original submission.”
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, comparing the candidate modified AxialML model against the model version from the original submission.”
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, comparing the candidate modified AxialML model against the model version from the original submission.”
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, comparing the candidate modified AxialML model against the model version from the original submission.”
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
Recall and MAUDE counts are product-code-level (reports aren't reliably attributable to one device). 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).