Avenda Health AI Prostate Cancer Planning Software

K221624

Avenda Health, Inc. · cleared 2022-11-22 · product code POK · Radiology

Premarket evidence — what FDA accepted

Device typesamd

source quote (p.3)

“The Avenda Health AI Prostate Cancer Planning Software is an artificial intelligence (AI)-based decision support software, indicated as an adjunct to the review of magnetic resonance (MR) prostate images and biopsy findings in the prostate oncological workflow.”

AlgorithmMachine learning-based algorithm based on patient information and image morphological, intensity, and geometric characteristics.

source quote (p.13)

“Machine learning-based algorithm based on patient information and image morphological, intensity, and geometric characteristics.”

Adaptive (vs locked)FDA source did not state this

PCCPFDA source did not state this

Cybersecurity addressedFDA source did not state this

Validation studies (2)

Standalone

n=50 patients

endpoints: accuracy in contouring GGG ">=2 lesions; accurately segment the prostate organ

Reader study (MRMC)

n=50 cases · 10 site(s)

endpoints: improve reader performance for prostate cancer lesion contouring; superior sensitivity; superior specificity; balanced accuracy; clinical quality; complete csPCa encapsulation rate

Reported performance (3 observations)

sensitivity97.4

source quote (p.7)

“The study results demonstrated that lesion contours produced using the Proposed Device had superior sensitivity (mean 97.4% vs 38.2%, p < 0.0001) compared to SOC contours”

specificity72.1

source quote (p.7)

“and superior specificity compared to hemi-gland contours (mean 72.1% vs 53.4%, p < 0.0001).”

accuracyas written: “Balanced accuracy”84.7

source quote (p.7)

“Furthermore, lesion contours produced using the Proposed Device were superior to both SOC and hemi-gland contours using measures of balanced accuracy (mean 84.7% vs 67.2% & 75.9% respectively, p < 0.0001)”

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).