DEEPVESSEL FFR

K213657

KeyaMed NA Inc. · cleared 2022-04-01 · product code PJA · Cardiovascular

Premarket evidence — what FDA accepted

Device typesamd
source quote (p.3)
“DEEPVESSEL FFR is a coronary physiological simulation software for the clinical quantitative and qualitative analysis of previously acquired Computed Tomography DICOM data for clinically stable symptomatic patients with coronary artery disease.”
Algorithmdeep learning neural networks, multi-layer perceptron network (MLP) and a bidirectional multi-layer recursive neural network (BRNN)
source quote (p.5)
“It estimates FFR values from static coronary CTA images with extracted coronary tree structures using deep learning neural networks. In the second step, the deep learning framework consists of a multi-layer perceptron network (MLP) and a bidirectional multi-layer recursive neural network (BRNN), which utilize the segmentation results and the CTA image, to estimate semi-continuous FFR values along the coronary artery centerlines.”
Adaptive (vs locked)FDA source did not state this
PCCPFDA source did not state this
Cybersecurity addressedFDA source did not state this

Validation studies (4)

Bench

sample size not stated

Bench

sample size not stated

Bench

n=0 scans

Retrospective clinical

n=244 patients · 8 site(s)

endpoints: per-vessel sensitivity and specificity of DVFFR to detect ischemic condition comparing with invasive FFR measurement

Reported performance (10 observations)

sensitivity86.9CI 80.6%-92.7%
source quote (p.7)
“At the vessel level, the observed sensitivity of DVFFR was 86.9% with a two-sided lower bound 95% Cl of 80.6%, and the observed specificity of DVFFR was 86.7% with a two-sided lower bound 95% CI of 82.0%.”
specificity86.7CI 82.0%-91.1%
source quote (p.7)
“At the vessel level, the observed sensitivity of DVFFR was 86.9% with a two-sided lower bound 95% Cl of 80.6%, and the observed specificity of DVFFR was 86.7% with a two-sided lower bound 95% CI of 82.0%.”
accuracyas written: “Diagnostic Accuracy (per-vessel)”86.8CI 83.0%–90.4%
source quote (p.7)
“The observed diagnostic accuracy, PPV (positive predictive value) and NPV (negative predictive value) of DVFFR were 86.8% (95% CI: 83.0%–90.4%), 79.4% (95% CI: 71.8%-86.2%) and 91.9% (95% CI: 87.7%–95.6%), respectively.”
ppvas written: “PPV (per-vessel)”79.4CI 71.8%-86.2%
source quote (p.7)
“The observed diagnostic accuracy, PPV (positive predictive value) and NPV (negative predictive value) of DVFFR were 86.8% (95% CI: 83.0%–90.4%), 79.4% (95% CI: 71.8%-86.2%) and 91.9% (95% CI: 87.7%–95.6%), respectively.”
npvas written: “NPV (per-vessel)”91.9CI 87.7%–95.6%
source quote (p.7)
“The observed diagnostic accuracy, PPV (positive predictive value) and NPV (negative predictive value) of DVFFR were 86.8% (95% CI: 83.0%–90.4%), 79.4% (95% CI: 71.8%-86.2%) and 91.9% (95% CI: 87.7%–95.6%), respectively.”
sensitivityas written: “Sensitivity (patient-level)”87.4CI 79.4%-93.1%
source quote (p.8)
“At the patient level, the observed sensitivity, specificity, accuracy, PPV, and NPV were 87.4% (95% CI: 79.4%-93.1%), 83.7% (95% CI: 76.5%–89.4%), 85.2% (95% CI: 80.2%–89.4%), 79.6% (95% CI: 71.0%-86.6%), and 90.1% (95% CI: 83.6%–94.6%), respectively, as shown in Table 3.”
specificityas written: “Specificity (patient-level)”83.7CI 76.5%-89.4%
source quote (p.8)
“At the patient level, the observed sensitivity, specificity, accuracy, PPV, and NPV were 87.4% (95% CI: 79.4%-93.1%), 83.7% (95% CI: 76.5%–89.4%), 85.2% (95% CI: 80.2%–89.4%), 79.6% (95% CI: 71.0%-86.6%), and 90.1% (95% CI: 83.6%–94.6%), respectively, as shown in Table 3.”
accuracyas written: “Accuracy (patient-level)”85.2CI 80.2%–89.4%
source quote (p.8)
“At the patient level, the observed sensitivity, specificity, accuracy, PPV, and NPV were 87.4% (95% CI: 79.4%-93.1%), 83.7% (95% CI: 76.5%–89.4%), 85.2% (95% CI: 80.2%–89.4%), 79.6% (95% CI: 71.0%-86.6%), and 90.1% (95% CI: 83.6%–94.6%), respectively, as shown in Table 3.”
ppvas written: “PPV (patient-level)”79.6CI 71.0%-86.6%
source quote (p.8)
“At the patient level, the observed sensitivity, specificity, accuracy, PPV, and NPV were 87.4% (95% CI: 79.4%-93.1%), 83.7% (95% CI: 76.5%–89.4%), 85.2% (95% CI: 80.2%–89.4%), 79.6% (95% CI: 71.0%-86.6%), and 90.1% (95% CI: 83.6%–94.6%), respectively, as shown in Table 3.”
npvas written: “NPV (patient-level)”90.1CI 83.6%–94.6%
source quote (p.8)
“At the patient level, the observed sensitivity, specificity, accuracy, PPV, and NPV were 87.4% (95% CI: 79.4%-93.1%), 83.7% (95% CI: 76.5%–89.4%), 85.2% (95% CI: 80.2%–89.4%), 79.6% (95% CI: 71.0%-86.6%), and 90.1% (95% CI: 83.6%–94.6%), respectively, as shown in Table 3.”

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
14
MAUDE reports in code, 12mo
+35%
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 Cardiovascular 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/K213657