DV. Target

K202928

Deepvoxel INC · cleared 2021-04-02 · product code QKB · Radiology

Premarket evidence — what FDA accepted

Device typesamd

source quote (p.3)

“DV.Target is a software application that enables the routing of DICOM-compliant data (CT Images) to automatic image processing workflows, using machine learning-based algorithms to automatically delineate organs-at-risk (OARs). Contours generated by DV.Target may be used as an input to clinical workflows for treatment planning in radiation therapy.”

Algorithmmachine learning-based algorithms; deep learning processing

source quote (p.3)

“DV.Target is a software application that enables the routing of DICOM-compliant data (CT Images) to automatic image processing workflows, using machine learning-based algorithms to automatically delineate organs-at-risk (OARs). Contours generated by DV.Target may be used as an input to clinical workflows for treatment planning in radiation therapy. DV.Target should be installed on a specialized server supporting deep learning processing.”

Adaptive (vs locked)FDA source did not state this

PCCPFDA source did not state this

Cybersecurity addressedFDA source did not state this

Validation studies (1)

Retrospective clinical

sample size not stated · 1 site(s)

endpoints: auto-contouring accuracy

Reported performance (1 observation)

diceas written: “Dice-Sørensen coefficients (DICE score)”stated without value

source quote (p.10)

“The Dice-Sørensen coefficients (DICE score) were calculated and used to evaluate contouring accuracies by comparing device-generated contours with ground truth contours. The DICE scores of the proposed device are generally higher than those of the predicate/reference device (from the Box and Whisker plots). The confidence interval of performance differences between the proposed and the predicate/reference devices are within the non-inferiority margin for all compared OARs.”

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).