AutoContour Model RADAC V2

K220598

Radformation, Inc. · cleared 2022-08-24 · product code QKB · Radiology

Premarket evidence — what FDA accepted

Device typesamd
source quote (p.5)
“As with AutoContour RADAC, the AutoContour RADAC V2 device is software that uses DICOM-compliant image data (CT or MR) as input to (1) automatically contour various structures of interest for radiation therapy treatment planning using machine learning based contouring.”
AlgorithmMachine learning based contouring using deep-learning based structure models with CNN architecture.
source quote (p.5)
“The deep-learning based structure models are trained using imaging datasets consisting of anatomical organs of the head and neck, thorax, abdomen and pelvis for adult male and female patients. (a) the same CNN architecture was used to train these new CT models”
Adaptive (vs locked)FDA source did not state this
PCCPFDA source did not state this
Cybersecurity addressedNo

Validation studies (2)

Retrospective clinical

n=140 images

endpoints: Mean Dice Similarity Coefficient (DSC); Sensitivity; Specificity

standards: NRG/RTOG guidelines

Retrospective clinical

n=16 images · 2 site(s)

endpoints: Mean Dice Similarity Coefficient (DSC); Sensitivity; Specificity

standards: NRG/RTOG guidelines

Reported performance (4 observations)

diceas written: “Mean DSC (CT Large structures)”0.94CI +/-0.03
source quote (p.12)
“For CT Large, Medium, and Small structures, AutoContour's results had a mean DSC of 0.94+/-0.03, 0.82+/-0.09, and 0.61+/-0.14 respectively”
diceas written: “Mean DSC (CT Medium structures)”0.82CI +/-0.09
source quote (p.12)
“For CT Large, Medium, and Small structures, AutoContour's results had a mean DSC of 0.94+/-0.03, 0.82+/-0.09, and 0.61+/-0.14 respectively”
diceas written: “Mean DSC (CT Small structures)”0.61CI +/-0.14
source quote (p.12)
“For CT Large, Medium, and Small structures, AutoContour's results had a mean DSC of 0.94+/-0.03, 0.82+/-0.09, and 0.61+/-0.14 respectively”
diceas written: “Mean DSC (MR All structures)”0.67CI +/-0.08
source quote (p.16)
“For MR Structure models a mean DSC of 0.67+/-0.08 was found across all structure models.”

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
3
drift signals on this device
  • re_clearance

    The FDA AI/ML device list shows a newer 510(k) K260509 (decision 2026-03-19) from Radformation, Inc. for a matching device line ("AutoContour (RADAC V5)") — a new clearance for the same line is a change event.

    first seen 2026-07-08 · k_number:K260509

  • re_clearance

    The FDA AI/ML device list shows a newer 510(k) K242729 (decision 2024-12-09) from Radformation, Inc. for a matching device line ("AutoContour (Model RADAC V4)") — a new clearance for the same line is a change event.

    first seen 2026-07-08 · k_number:K242729

  • re_clearance

    The FDA AI/ML device list shows a newer 510(k) K230685 (decision 2023-04-14) from Radformation, Inc. for a matching device line ("AutoContour Model RADAC V3") — a new clearance for the same line is a change event.

    first seen 2026-07-08 · k_number:K230685

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/K220598