EdgeFlow UH10

K231677

Edgecare Inc. · cleared 2024-03-06 · product code IYO · Radiology

Premarket evidence — what FDA accepted

Device typesamd
source quote (p.5)
“The deep learning model employed in EdgeFlow UH10 comprises three components: feature extraction, binary classification, and semantic segmentation networks.”
Algorithmdeep learning model comprising three components: feature extraction, binary classification, and semantic segmentation networks
source quote (p.5)
“The deep learning model employed in EdgeFlow UH10 comprises three components: feature extraction, binary classification, and semantic segmentation networks.”
Adaptive (vs locked)FDA source did not state this
PCCPFDA source did not state this
Cybersecurity addressedYes
source quote (p.11)
“Cybersecurity Test”

Validation studies (2)

Retrospective clinical

n=3,711 images · 1 site(s)

endpoints: F1 score; PR AUC

Retrospective clinical

n=1,528 images · 1 site(s)

endpoints: Dice Score

Reported performance (2 observations)

f1as written: “F1 score”0.979CI 95% CI 0.974–0.984
source quote (p.13)
“The test criteria of classification accuracy and segmentation accuracy were both satisfied with an F1 score of 0.979 (95% CI 0.974–0.984)”
diceas written: “Dice score”0.896CI 95% CI 0.890-0.901
source quote (p.13)
“and a Dice score of 0.896 (95% CI 0.890-0.901).”

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

8
recalls in product code, 24mo
344
MAUDE reports in code, 12mo
-40%
vs code's own 3-yr baseline
1
drift signals on this device
  • recall_reason_pattern

    Software/algorithm-related recall in product code IYO (Civco Medical Instruments Co. Inc., initiated 2026-03-02): "There was an error in inspection and programming of the eTRAX needle sensor for Aurora trackers. The result is a potential for the needle tip position to be incorrectly identified " Recalling firm is another firm in the same product code.

    first seen 2026-07-08 · recall res_event_number:98513

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/K231677