Paige Prostate

DEN200080

Paige.AI · granted 2021-09-21 · product code QPN · Pathology

Premarket evidence — what FDA accepted

Device typesamd
source quote (p.2)
“Paige Prostate is an in vitro diagnostic medical device software, derived from a deterministic deep learning system that has been developed with digitized WSIs of H&E stained prostate needle biopsy slides.”
Algorithmdeterministic deep learning system
source quote (p.2)
“Paige Prostate is an in vitro diagnostic medical device software, derived from a deterministic deep learning system that has been developed with digitized WSIs of H&E stained prostate needle biopsy slides.”
Adaptive (vs locked)No
source quote (p.7)
“Software v2.1.501 will remain locked for use with the authorized device and will not be continually trained and improved with each cohort analyzed in clinical practice, after marketing authorization.”
PCCPNo
source quote (p.7)
“Software v2.1.501 will remain locked for use with the authorized device and will not be continually trained and improved with each cohort analyzed in clinical practice, after marketing authorization.”
Cybersecurity addressedYes
source quote (p.7)
“Cybersecurity: The cybersecurity documentation is consistent with the recommendations for information that should be included in premarket submissions outlined in the FDA guidance document “Content of Premarket Submissions for Management of Cybersecurity in Medical Devices: Guidance for Industry and Food and Drug Administration Staff” (issued October 2, 2014).”

Validation studies (3)

Retrospective clinical

n=728 images · 217 site(s)

endpoints: Sensitivity; Specificity; True Positive; True Negative; False Positive; False Negative

Bench

n=71 images · 1 site(s)

endpoints: Positive Percent Agreement (PPA); Negative Percent Agreement (NPA); Agreement rate between repetitions

Reader study (MRMC)

n=527 images · 157 site(s)

endpoints: Sensitivity; Specificity

standards: CLSI document EP12-A2: User Protocol for Evaluation of Qualitative Test Performance; Approved Guideline – Second Edition, 2008

Reported performance (4 observations)

sensitivity0.945CI 91.4%; 96.6%
source quote (p.11)
“Sensitivity 94.5% 95% CI* 91.4%; 96.6%”
specificity0.94CI 91.3%; 95.9%
source quote (p.11)
“Specificity 94.0% 95% CI* 91.3%; 95.9%”
sensitivityas written: “Average improvement in sensitivity (assisted vs unassisted)”0.073CI 3.9%; 11.4%
source quote (p.23)
“For combined data, an average improvement in sensitivity was 7.3% with 95%CI: (3.9%; 11.4%) (statistically significant)”
specificityas written: “Average difference in specificity (assisted vs unassisted)”0.011CI -0.7%; 3.4%
source quote (p.23)
“An average difference in specificity was 1.1% with 95% CI: (-0.7%; 3.4% (not statistically significant)”

Each value carries its own analysis unit and task — never compare or pool across devices. Source: De Novo decision summary PDF.

Predicate network

Postmarket — what happened after clearance

Not yet tracked — the weekly postmarket refresh hasn't snapshotted this device.

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 De Novo 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/DEN200080