Premarket evidence — what FDA accepted

Device typesamd

source quote (p.5)

“syngo.via MI Workflows (including Scenium and syngo MBF applications) is a multi-modality post-processing software only medical device intended to aid in the management of diseases, including those associated with oncology, cardiology, neurology, and organ function.”

Algorithmregion of interest detection based on anatomical landmarks, followed by a Deep Image-to-Image Network performing the actual segmentation step.

source quote (p.8)

“The organ segmentation consists of a region of interest detection based on anatomical landmarks, followed by a Deep Image-to-Image Network performing the actual segmentation step.”

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

sample size not stated

endpoints: DICE coefficient; average symmetric surface distance (ASSD)

standards: Digital Imaging and Communications in Medicine (DICOM) Set; PS 3.1 - 3.20 2022d, Medical Device Software -Software Life Cycle Processes; 62304:2006 (1st Edition)/A1:2016, Medical devices - Application of risk management to medical devices; 14971 Third Edition 2019-12, Medical devices - Part 1: Application of usability engineering to medical devices 62366-1:2015+AMD1:2020, Medical devices - Symbols to be used with medical device labels, labelling, and information to be supplied - Part 1: General requirements ISO 15223-1 Fourth edition 2021-07

Standalone

n=700 images

endpoints: correlations and agreement between SUVRs calculated by the pipeline and MR-based method

standards: Digital Imaging and Communications in Medicine (DICOM) Set; PS 3.1 - 3.20 2022d, Medical Device Software -Software Life Cycle Processes; 62304:2006 (1st Edition)/A1:2016, Medical devices - Application of risk management to medical devices; 14971 Third Edition 2019-12, Medical devices - Part 1: Application of usability engineering to medical devices 62366-1:2015+AMD1:2020, Medical devices - Symbols to be used with medical device labels, labelling, and information to be supplied - Part 1: General requirements ISO 15223-1 Fourth edition 2021-07

Reported performance (1 observation)

diceas written: “DICE coefficient”stated without value

source quote (p.8)

“The segmentation quality was assessed by comparing a manually annotated ground truth with the algorithm result using the overlap measure DICE coefficient and the average symmetric surface distance (ASSD: average surface distance between algorithm result and manual ground truth annotation). All organs met criteria for either the average DICE coefficient or the ASSD.”

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

3

MAUDE reports in code, 12mo

—

vs code's own 3-yr baseline

2

drift signals on this device

re_clearance
The FDA AI/ML device list shows a newer 510(k) K251528 (decision 2025-07-03) from Siemens Medical Solutions USA, Inc. for a matching device line ("syngo.via MI Workflows; Scenium; syngo MBF") — a new clearance for the same line is a change event.
first seen 2026-07-08 · k_number:K251528
re_clearance
The FDA AI/ML device list shows a newer 510(k) K242275 (decision 2024-08-30) from Siemens Medical Solutions USA, Inc. for a matching device line ("syngo.via MI Workflows; Scenium; syngo MBF") — a new clearance for the same line is a change event.
first seen 2026-07-08 · k_number:K242275

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).