Galen™ Second Read™

K241232

Ibex Medical Analytics Ltd. · cleared 2025-01-24 · product code QPN · Pathology

Premarket evidence — what FDA accepted

Device typesamd
source quote (p.1)
“Regulation Name: Software algorithm device to assist users in digital pathology”
Algorithmdeterministic deep convolutional neural network
source quote (p.6)
“The Galen Second Read is an in vitro diagnostic medical device software, derived from a deterministic deep convolutional neural network that has been developed with digitized WSIs of H&E-stained prostate core needle biopsy (PCNB) slides originating from formalin-fixed paraffin-embedded (FFPE) tissue sections, that were initially diagnosed as benign by the pathologist.”
Adaptive (vs locked)No
source quote (p.6)
“The Galen Second Read is an in vitro diagnostic medical device software, derived from a deterministic deep convolutional neural network that has been developed with digitized WSIs of H&E-stained prostate core needle biopsy (PCNB) slides originating from formalin-fixed paraffin-embedded (FFPE) tissue sections, that were initially diagnosed as benign by the pathologist.”
PCCPFDA source did not state this
Cybersecurity addressedFDA source did not state this

Validation studies (3)

Bench

sample size not stated

endpoints: precision (repeatability and reproducibility) in identifying WSI of PCNBs suspicious of cancer; localization accuracy; localization precision of detecting the pixels that belong to cancer foci

Retrospective clinical

n=347 cases · 3 site(s)

endpoints: performance of the Galen Second Read in identifying prostatic adenocarcinoma cases (subjects) missed by Standard of Care (SoC)

Reader study (MRMC)

n=772 cases · 4 site(s)

endpoints: difference in performances of a pathologist supported by the Galen Second Read vs GT result and a pathologist with SoC vs GT result; Sensitivity and specificity for each pathologist

Reported performance (2 observations)

sensitivity93.9CI (92.2%; 95.8%)
source quote (p.14)
“With Galen 93.9% (1115/1187) (92.2%; 95.8%)”
specificity87.9CI (85.8%; 90.4%)
source quote (p.14)
“With Galen 87.9% (991/1127) (85.8%; 90.4%)”

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
0
MAUDE reports in code, 12mo
—
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 Pathology 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/K241232