Diagnocat

K252934

DGNCT, LLC · cleared 2026-01-15 · product code MYN · Radiology

Premarket evidence — what FDA accepted

Device typesamd
source quote (p.5)
“Diagnocat Software is a computer-assisted detection (CADe) software-only device intended to concurrently aid in the detection of periapical radiolucency areas.”
Algorithmdeep learning algorithms and artificial intelligence (AI)
source quote (p.5)
“leveraging deep learning algorithms and artificial intelligence (AI).”
Adaptive (vs locked)FDA source did not state this
PCCPNo
Cybersecurity addressedYes
source quote (p.8)
“Software verification and validation testing, and cybersecurity testing per FDA guidance, "Cybersecurity in Medical Devices: Quality System Considerations and Content of Premarket Submissions", were conducted to ensure that the software meets its specifications and performs as intended.”

Validation studies (3)

Retrospective clinical

n=100 images

endpoints: Teeth Segmentation; Periapical Radiolucency Segmentation

Retrospective clinical

n=285 images

endpoints: detection of periapical radiolucency

Reader study (MRMC)

sample size not stated

endpoints: improvements in diagnostic ability when using the device; clinician performance in detecting PARL

Reported performance (7 observations)

sensitivity0.854
source quote (p.9)
“Sensitivity 0.854”
specificity0.991
source quote (p.9)
“Specificity 0.991”
aurocas written: “auc”0.9213
source quote (p.9)
“Aided 0.9213”
diceas written: “Teeth Segmentation (Cohort 1) Mean DSC”0.955
source quote (p.8)
“Teeth Segmentation Cohort 1 0.955”
diceas written: “Teeth Segmentation (Cohort 2) Mean DSC”0.947
source quote (p.8)
“Teeth Segmentation Cohort 2 0.947”
diceas written: “Periapical Radiolucency Segmentation (Cohort 2) Mean DSC”0.804
source quote (p.8)
“Periapical Radiolucency Segmentation Cohort 2 0.804”
aurocas written: “AUC Difference (Aided vs Unaided)”0.027
source quote (p.9)
“AUC Difference +0.027”

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
-100%
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 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/K252934