Lunit INSIGHT DBT (V1.2)

K253796

Lunit, Inc. · cleared 2026-03-26 · product code QDQ · Radiology

Premarket evidence — what FDA accepted

Device typesamd
source quote (p.6)
“Lunit INSIGHT DBT is a computer-assisted detection and diagnosis (CADe/x) Software as a Medical Device that provides information about the presence, location and characteristics of lesions suspicious for breast cancer to assist interpreting physicians in making diagnostic decisions when reading digital breast tomosynthesis (DBT) images.”
Algorithmartificial intelligence/machine learning-based software algorithm trained via deep learning
source quote (p.6)
“The software automatically analyzes digital breast tomosynthesis slices via artificial intelligence technology that has been trained via deep learning.”
Adaptive (vs locked)FDA source did not state this
PCCPNo
Cybersecurity addressedNo

Validation studies (2)

Retrospective clinical

n=3,277 cases

endpoints: Lower bound of 95% CI of device's ROC AUC in standalone performance was greater than 0.903 and p-value was less than the significance level of 5% (0.05); JAFROC AUC; Sensitivity; Specificity; Lesion type agreement analysis

Reader study (MRMC)

n=258 cases

Reported performance (8 observations)

sensitivity0.9111CI 89.66, 92.57
source quote (p.9)
“Sensitivity at the default operating point (0.1) was 91.11% (95% CI: 89.66, 92.57)”
specificity0.7762CI 75.70, 79.54
source quote (p.9)
“specificity was 77.62% (95% CI: 75.70, 79.54), respectively.”
aurocas written: “auc”0.9388CI 0.9304, 0.9472
source quote (p.9)
“ROC AUC in the standalone performance analysis was 0.9388 (95% CI: 0.9304, 0.9472) with statistical significance (p < 0.05).”
aurocas written: “JAFROC AUC”0.9206CI 0.9117, 0.9295
source quote (p.9)
“For the secondary endpoints, the result of the JAFROC AUC was 0.9206 (95% CI: 0.9117, 0.9295).”
sensitivityas written: “Sensitivity at supplementary '0.3' operating point”0.8838CI 86.74, 90.02
source quote (p.9)
“Sensitivity at the supplementary '0.3' operating point was 88.38% (95% CI: 86.74, 90.02)”
specificityas written: “Specificity at supplementary '0.3' operating point”0.8368CI 81.98, 85.38
source quote (p.9)
“specificity was 83.68% (95% CI: 81.98, 85.38), respectively.”
sensitivityas written: “Sensitivity at supplementary '0.6' point”0.8148CI 79.49, 83.47
source quote (p.9)
“Sensitivity at the supplementary ‘0.6' point was 81.48% (95% CI: 79.49, 83.47)”
specificityas written: “Specificity at supplementary '0.6' point”0.9344CI 92.30, 94.58
source quote (p.9)
“specificity was 93.44% (95% CI: 92.30, 94.58), respectively.”

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 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/K253796