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)”

Algorithmmachine learning-based decision support system; artificial intelligence (machine learning) algorithm; fixed classification model; automated contour detection

source quote (p.5)

“EchoGo Pro v1.0.2 is a machine learning-based decision support system, indicated as an adjunct to diagnostic stress echocardiography for patients undergoing assessment for coronary artery disease (CAD).”

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.”

PCCPFDA source did not state this

Cybersecurity addressedFDA source did not state this

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)”

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)”

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)”

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

recalls in product code, 24mo

MAUDE reports in code, 12mo

—

vs code's own 3-yr baseline

drift signals on this device

Recall and MAUDE counts are product-code-level (reports aren't reliably attributable to one device). 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).