EchoGo Pro

K201555

Ultromics Ltd · cleared 2020-12-18 · product code POK · Radiology

Premarket evidence — what FDA accepted

Device typesamd
source quote (p.7)
“Software as a medical device (SaMD)”
Algorithmartificial intelligence (machine learning) algorithm; fixed classification model
source quote (p.6)
“Geometric parameters are calculated from the approved contours and are fed into a fixed classification model that has been previously trained on datasets with known outcomes. Both devices provide computer analytics based on morphological and enhancement characteristics that the predicate device refers to as imaging features that are then synthesized by an artificial intelligence (machine learning) algorithm into a categorical output.”
Adaptive (vs locked)No
source quote (p.6)
“Geometric parameters are calculated from the approved contours and are fed into a fixed classification model that has been previously trained on datasets with known outcomes.”
PCCPNo
Cybersecurity addressedNo

Validation studies (1)

Reader study (MRMC)

sample size not stated

endpoints: The difference between the diagnostic performance of readers when interpreting ultrasound studies with and without the assistance of EchoGo Pro v1.0.2 is equivalent or better than that of the predicate device.; The difference between inter-operator agreement when interpreting ultrasound studies with and without the assistance of EchoGo Pro v1.0.2. is equivalent or better than that of the predicate device.

Reported performance (3 observations)

sensitivity0.844CI 95% CI 0.739, 0.950
source quote (p.9)
“The system achieved a native system performance of 0.927 AUROC, with specificity of 0.927 (95% CI 0.878, 0.976) and a sensitivity of 0.844 (95% CI 0.739, 0.950), greater than the native system performance reported for the predicate device (K190442) of 0.882.”
specificity0.927CI 95% CI 0.878, 0.976
source quote (p.9)
“The system achieved a native system performance of 0.927 AUROC, with specificity of 0.927 (95% CI 0.878, 0.976) and a sensitivity of 0.844 (95% CI 0.739, 0.950), greater than the native system performance reported for the predicate device (K190442) of 0.882.”
aurocas written: “auc”0.927
source quote (p.9)
“The system achieved a native system performance of 0.927 AUROC, with specificity of 0.927 (95% CI 0.878, 0.976) and a sensitivity of 0.844 (95% CI 0.739, 0.950), greater than the native system performance reported for the predicate device (K190442) of 0.882.”

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/K201555