Neosoma Brain Mets

K252922

Neosoma, Inc. · cleared 2025-12-17 · product code QIH · Radiology

Premarket evidence — what FDA accepted

Device typesamd
source quote (p.6)
“Neosoma Brain Mets is a Software as a Medical Device (SaMD) that is designed specifically for the semi-automatic segmentation of previously diagnosed brain metastases.”
Algorithmartificial intelligence algorithm (i.e., deep learning neural networks)
source quote (p.4)
“The Neosoma software uses an artificial intelligence algorithm (i.e., deep learning neural networks) to contour (segment) known or previously diagnosed brain tumors on MRI images for qualified and trained medical professionals.”
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

n=70 patients

endpoints: Sensitivity rate, to assess the true positive rate of lesion identification; False Positive Rate, to assess the number of false positive lesions per MRI; DSC (Dice Similarity Coefficient), to assess the degree of overlap between device output and the reference standard.; 95th percentile Hausdorff Distance (HD95) for true-positive lesions, to assess the maximum boundary distance between the device segmentation and the reference standard.; Mean Surface Distance (MSD) for true-positive lesions, to assess the average distance between device and reference standard surfaces.

standards: IEC 62304:2006/AC:2015, 2023 FDA Guidance document, “Content of Premarket Submissions for Device Software Functions"

Reported performance (2 observations)

sensitivity0.9CI 0.87 - 0.94
source quote (p.12)
“0.90 with 95% CI of 0.87 - 0.94”
diceas written: “DSC”0.86CI 0.83 - 0.89
source quote (p.13)
“0.86 with 95% CI of 0.83 - 0.89”

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
1
drift signals on this device
  • predicate_cohort_contagion

    K252922 shares predicate K203235 with K252304 (syngo.via RT Image Suite VC10), whose product code MUJ recorded 35 recall event(s) in the trailing 24 months.

    first seen 2026-07-08 · predicate:K203235 sibling:K252304

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 Radiology 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/K252922