Fetal EchoScan (v1.2)

K252294

Brightheart · cleared 2025-12-08 · product code POK · Radiology

Premarket evidence — what FDA accepted

Device typesamd
source quote (p.5)
“Fetal EchoScan is a cloud-based software-only device which uses neural networks to detect suspicious cardiac radiographic findings...”
Algorithmneural networks, Machine Learning Model
source quote (p.5)
“Fetal EchoScan is a cloud-based software-only device which uses neural networks to detect suspicious cardiac radiographic findings...”
Adaptive (vs locked)FDA source did not state this
PCCPFDA source did not state this
Cybersecurity addressedYes
source quote (p.8)
“Cybersecurity documentation and testing was conducted as recommended by FDA's Guidance for Industry and FDA Staff, “Content of Premarket Submissions for Management of Cybersecurity in Medical Devices”.”

Validation studies (2)

Standalone

n=877 patients · 11 site(s)

endpoints: detection of any suspicious radiographic finding; detection of each suspicious radiographic finding

standards: FDA Guidance for Industry and FDA Staff, “Guidance for the Content of Premarket Submissions for Software Contained in Medical Devices.”, IEC 62304:2016, FDA Guidance for Industry and FDA Staff, “Content of Premarket Submissions for Management of Cybersecurity in Medical Devices”

Reader study (MRMC)

n=200 cases

endpoints: identification of any suspicious radiographic finding; identification of each suspicious radiographic finding

Reported performance (7 observations)

sensitivity0.984CI 0.963; 0.993
source quote (p.9)
“0.984 (0.963; 0.993)”
specificity0.97CI 0.952; 0.981
source quote (p.9)
“0.970 (0.952; 0.981)”
aurocas written: “auc”0.974CI 0.957; 0.990
source quote (p.11)
“0.974 (0.957; 0.990)”
sensitivityas written: “Sensitivity (Best-Case)”0.99CI 0.972; 0.997
source quote (p.9)
“0.990 (0.972; 0.997)”
specificityas written: “Specificity (Worst-Case)”0.958CI 0.938; 0.971
source quote (p.9)
“0.958 (0.938; 0.971)”
sensitivityas written: “Mean sensitivity (aided)”0.935CI 0.892-0.978
source quote (p.11)
“The mean sensitivity for identification of any claimed suspicious finding was 0.935 (0.892-0.978) in the aided reading condition”
specificityas written: “Mean specificity (aided)”0.97CI 0.949-0.991
source quote (p.11)
“The mean specificity for identification of any claimed suspicious finding was 0.970 (0.949-0.991) in the aided reading condition”

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/K252294