Advanced Algorithms for Treatment Management Applications (AATMA)

K212218

Elekta Solutions AB · cleared 2021-10-25 · product code QKB · Radiology

Premarket evidence — what FDA accepted

Device typesamd
source quote (p.3)
“AATMA™ is a medical image processing library intended to produce derived data sets for use as input into radiation therapy treatment planning systems or other intermediate pre-treatment-planning applications. AATMA™ does not provide a user interface and is designed to be accessed through its application programming interface (API) by other devices.”
Algorithmmachine-learning convolutional neural networks
source quote (p.4)
“The auto-segmentation algorithm in AATMA™ is based on machine-learning convolutional neural networks and includes pre-trained models that will be used to automatically segment image sets.”
Adaptive (vs locked)No
source quote (p.4)
“The auto-segmentation algorithm in AATMA™ is based on machine-learning convolutional neural networks and includes pre-trained models that will be used to automatically segment image sets. The available models have been pre-trained on specific datasets that exhibit similar characteristics (e.g., body site and imaging modality).”
PCCPNo
Cybersecurity addressedNo

Validation studies (2)

Retrospective clinical

n=13 patients

endpoints: average DICE coefficient over all structures

standards: CFR 21 Part 820, DICOM standard

Retrospective clinical

n=20 patients

endpoints: average DICE coefficient over all structures

standards: CFR 21 Part 820, DICOM standard

Reported performance (2 observations)

diceas written: “average DICE coefficient (Head & Neck model)”0.84
source quote (p.6)
“the average DICE coefficient over all structures was determined to be 0.84 which met the defined acceptance criteria.”
diceas written: “average DICE coefficient (Male Pelvis model)”0.93
source quote (p.6)
“the average DICE coefficient over all structures was determined to be 0.93 which met the defined acceptance criteria.”

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/K212218