Genius AI Detection

K201019

Hologic, Inc. · cleared 2020-11-18 · product code QDQ · Radiology

Premarket evidence — what FDA accepted

Device typesamd
source quote (p.4)
“Genius AI Detection is a software device intended to identify potential abnormalities in breast tomosynthesis images.”
Algorithmdeep learning networks, machine learning
source quote (p.4)
“Genius AI Detection analyzes each standard mammographic view in a digital breast tomosynthesis examination using deep learning networks. For each detected lesion, Genius AI Detection produces CAD results that include the location of the lesion, an outline of the lesion and a confidence score for that lesion. Genius AI Detection also produces a case score for the entire tomosynthesis exam. Image processing device utilizing machine learning to aid in the detection, localization, and characterization of soft tissue densities (masses, architectural distortions and asymmetries) and calcifications in the 1-mm 3D DBT slices.”
Adaptive (vs locked)FDA source did not state this
PCCPFDA source did not state this
Cybersecurity addressedYes
source quote (p.5)
“"Content of Premarket Submissions for Management of Cybersecurity in Medical Devices,” issued on October 2, 2014”

Validation studies (2)

Reader study (MRMC)

n=390 cases

endpoints: reader accuracy as assessed by AUC; improved assisted-read detection from the sensitivity analysis

Standalone

n=764 cases

endpoints: comparable detection performance as observed by fROC analysis; number and type of cancers detected; stratified fROC analysis using lesion type as well as breast density

Reported performance (6 observations)

sensitivity0.759
source quote (p.10)
“The average observed reader sensitivity for cancer cases was 75.9% with CAD and 66.8% without CAD.”
aurocas written: “auc”0.825CI 0.783, 0.867
source quote (p.9)
“The average observed AUC was 0.825 (95% CI: 0.783, 0.867) with CAD and 0.794 (95% CI: 0.748, 0.840) without CAD.”
aurocas written: “Difference in observed AUC”0.031CI 0.012, 0.051
source quote (p.9)
“The difference in observed AUC was +0.031 (95% CI: 0.012, 0.051).”
sensitivityas written: “Difference in observed reader sensitivity for cancer cases”0.09CI 6.0%, 12.1%
source quote (p.10)
“The difference in observed sensitivity was +9.0% (99% CI: 6.0%, 12.1%).”
sensitivityas written: “Average observed recall rate for non-cancer cases with CAD”0.258
source quote (p.10)
“The average observed recall rate for non-cancer cases was 25.8% with CAD and 23.4% without CAD.”
sensitivityas written: “Difference in negative recall rate”0.024CI 0.7%, 4.2%
source quote (p.10)
“The observed difference in negative recall rate was +2.4% (99% CI: 0.7%, 4.2%).”

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
3
drift signals on this device
  • re_clearance

    The FDA AI/ML device list shows a newer 510(k) K243341 (decision 2025-07-31) from Hologic, Inc. for a matching device line ("Genius AI Detection 2.0") — a new clearance for the same line is a change event.

    first seen 2026-07-08 · k_number:K243341

  • re_clearance

    The FDA AI/ML device list shows a newer 510(k) K230096 (decision 2023-05-23) from Hologic, Inc. for a matching device line ("Genius AI Detection 2.0 with CC-MLO Correlation") — a new clearance for the same line is a change event.

    first seen 2026-07-08 · k_number:K230096

  • re_clearance

    The FDA AI/ML device list shows a newer 510(k) K221449 (decision 2022-10-06) from Hologic, Inc. for a matching device line ("Genius AI Detection 2.0") — a new clearance for the same line is a change event.

    first seen 2026-07-08 · k_number:K221449

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/K201019