RayStation 12A

K222312

RaySearch Laboratories AB (publ) · cleared 2023-03-29 · product code MUJ · Radiology

Premarket evidence — what FDA accepted

Device typesamd
source quote (p.3)
“RayStation is a software system for radiation therapy and medical oncology.”
AlgorithmPeer reviewed algorithms for plan parameter optimization and dose calculation (Monte Carlo, point kernel superposition, pencil beam) and deep learning for automatic segmentation.
source quote (p.4)
“A scientific basis for the device is the implementation of peer reviewed algorithms of plan parameter optimization and photon and particle dose calculation. For electron beams RayStation calculates dose by the Monte Carlo technique. For photon beams RayStation calculates dose by the point kernel superposition method (a.k.a. Collapsed Cone) or a Monte Carlo algorithm for radiation transport. For proton beams RayStation uses either the pencil beam algorithm with the Fermi-Eyges formalism, or a Monte Carlo algorithm for radiation transport. With deep learning segmentation, the user can use trained deep learning models for automatic segmentation of new patient images. (The model training is performed offline on clinical CT and structure data.)”
Adaptive (vs locked)No
source quote (p.11)
“With deep learning segmentation, the user can use trained deep learning models for automatic segmentation of new patient images. (The model training is performed offline on clinical CT and structure data.)”
PCCPNo
Cybersecurity addressedYes
source quote (p.6)
“Both systems are compliant with the requirements listed in the FDA guideline 1825 “Content of Premarket Submissions for Management of Cybersecurity in Medical Devices".”

Validation studies (4)

Bench

sample size not stated

endpoints: The SOBP distal fall-off of the central axis depth dose curve; 95% and 98% of the computed depth dose values with Gamma pass rates

Bench

sample size not stated

endpoints: For a 3D-CRT plan, the merged beams' MU shall agree with original beams' MU.; Merged beams' segments shall keep original shapes.; MU and segment weights after split are subdivided correctly and that split beams are managed correctly in terms of ordering and ROI handling.

Bench

sample size not stated

standards: TG43

Bench

sample size not stated

endpoints: Two different gamma criteria for comparison with another TPS or measurement are evaluated for each test case, with specified requirements on level of agreement.; The fraction of the calculated dose data points for comparison with previous RayStation dose that fail has been evaluated, and the fraction of the calculated dose data points for comparison to BEAMnrc/egs++ that fail has been evaluated.

Reported performance (0 observations)

FDA source did not state a quantitative performance metric — non-reporting is itself the signal.

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

35
recalls in product code, 24mo
17
MAUDE reports in code, 12mo
-50%
vs code's own 3-yr baseline
2
drift signals on this device
  • recall_reason_pattern

    Software/algorithm-related recall in product code MUJ (Philips Medical Systems (Cleveland) Inc, initiated 2025-08-05): "Due to a software issue, there is a potential image error of the Region of Interest for expansion/contraction for HFP (Head First Prone), FFS (Feet First Supine) and FFP (Feet Firs" Recalling firm is another firm in the same product code.

    first seen 2026-07-08 · recall res_event_number:97049

  • recall_reason_pattern

    Software/algorithm-related recall in product code MUJ (Philips Medical Systems (Cleveland) Inc, initiated 2025-07-17): "Due to software issue, Radiation Therapy Planning system may provide incorrect dataset calculations when performing the "Stopping Power Ratio" (SPR) ," Recalling firm is another firm in the same product code.

    first seen 2026-07-08 · recall res_event_number:97309

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/K222312