Vista OS, Vista AI Scan, RTHawk

K251029

Vista AI, Inc. · cleared 2025-08-21 · product code LNH · Radiology

Premarket evidence — what FDA accepted

Device typesamd
source quote (p.6)
“RTHawk is a software platform designed from the ground up to provide efficient MRI data acquisition, data transfer, image reconstruction, and interactive scan control and display of static and dynamic MR imaging data. The AI algorithms within the Vista OS system are designed to assist MRI technologists which are always in control of the scan process. The software automates aspects of MRI setup and parameter selection to help reduce exam time, simplify the workflow, and increase reliability.”
Algorithmneural-network models
source quote (p.8)
“The system uses neural networks for image analysis, with no generative AI employed. These models have a multi-layered architecture that reduces data to the most relevant set for inline image analysis.”
Adaptive (vs locked)No
source quote (p.8)
“The system uses neural networks for image analysis, with no generative AI employed.”
PCCPFDA source did not state this
Cybersecurity addressedFDA source did not state this

Validation studies (6)

Retrospective clinical

n=120 images

endpoints: 80% agreement between neural-network assessment at its default sensitivity level and the cardiologist reader

standards: IEC 60601-2-33:2022-08 (Ed. 4.0), IEC 60601-1:2020 (Ed. 3.2), MS1-2008, MS3-2008, MS4-2010, MS8-2016, NEMA PS3.1 - 3.20 (2023e), ISO 14971:2019

Retrospective clinical

n=100 other

endpoints: 80% agreement between neural-network assessment at its default sensitivity level and the cardiologist reader

standards: IEC 60601-2-33:2022-08 (Ed. 4.0), IEC 60601-1:2020 (Ed. 3.2), MS1-2008, MS3-2008, MS4-2010, MS8-2016, NEMA PS3.1 - 3.20 (2023e), ISO 14971:2019

Retrospective clinical

n=209 images

endpoints: denoising should not detract from diagnostic accuracy in all cases; diagnostic quality of the denoised data be judged superior to its paired non-denoised series in more than 80% of test cases

standards: IEC 60601-2-33:2022-08 (Ed. 4.0), IEC 60601-1:2020 (Ed. 3.2), MS1-2008, MS3-2008, MS4-2010, MS8-2016, NEMA PS3.1 - 3.20 (2023e), ISO 14971:2019

Retrospective clinical

n=323 images

endpoints: mean error in plane angulation of less than 3 degrees with standard deviation less than 5 degrees; mean plane position error less than 5 mm with standard deviation less than 15 mm

standards: IEC 60601-2-33:2022-08 (Ed. 4.0), IEC 60601-1:2020 (Ed. 3.2), MS1-2008, MS3-2008, MS4-2010, MS8-2016, NEMA PS3.1 - 3.20 (2023e), ISO 14971:2019

Retrospective clinical

n=329 images

endpoints: mean 3D Intersection-over-Union (IoU) metrics of at least 0.65 for each volumetric scan prescription

standards: IEC 60601-2-33:2022-08 (Ed. 4.0), IEC 60601-1:2020 (Ed. 3.2), MS1-2008, MS3-2008, MS4-2010, MS8-2016, NEMA PS3.1 - 3.20 (2023e), ISO 14971:2019

Retrospective clinical

n=42 other

endpoints: average velocity error should be less than 10% individually for all vessels and views

standards: IEC 60601-2-33:2022-08 (Ed. 4.0), IEC 60601-1:2020 (Ed. 3.2), MS1-2008, MS3-2008, MS4-2010, MS8-2016, NEMA PS3.1 - 3.20 (2023e), ISO 14971:2019

Reported performance (2 observations)

agreement_kappaas written: “agreement between neural-network assessment and the cardiologist reader”80
source quote (p.10)
“The primary acceptance criterion for this test was 80% agreement between neural-network assessment at its default sensitivity level and the cardiologist reader.”
agreement_kappaas written: “agreement between neural-network assessment and the cardiologist reader”80
source quote (p.10)
“The primary acceptance criterion for this test was 80% agreement between neural-network assessment at its default sensitivity level and the cardiologist reader.”

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

100
recalls in product code, 24mo
510
MAUDE reports in code, 12mo
+5%
vs code's own 3-yr baseline
2
drift signals on this device
  • recall_reason_pattern

    Software/algorithm-related recall in product code LNH (Philips North America, initiated 2026-04-14): "The potential for stiffness value errors when a specific range of image reconstruction parameters is used in combination with Resoundant's algorithm, leading to the reconstruction " Recalling firm is another firm in the same product code.

    first seen 2026-07-08 · recall res_event_number:98779

  • recall_reason_pattern

    Software/algorithm-related recall in product code LNH (Philips North America, initiated 2025-12-03): "The potential for stiffness value errors when viewing exported MR Elastography (MRE) stiffness maps to viewer Picture Archiving and Communication System (PACS)." Recalling firm is another firm in the same product code.

    first seen 2026-07-08 · recall res_event_number:98111

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/K251029