ClariSIGMAM

K203785

ClariPi Inc. · cleared 2021-09-10 · product code QIH · Radiology

Premarket evidence — what FDA accepted

Device typesamd
source quote (p.3)
“ClariSIGMAM is a software application intended for use with compatible full field digital mammography systems.”
AlgorithmClariSIGMAM calculates percent breast density defined as the ratio of fibroglandular tissue to total breast area estimates and provides breast density group information (BI-RADS A+B as fatty and BI-RADS C+D as dense).
source quote (p.6)
“ClariSIGMAM calculates percent breast density defined as the ratio of fibroglandular tissue to total breast area estimates and provides breast density group information (BI-RADS A+B as fatty and BI-RADS C+D as dense) to aid radiologists in the assessment of breast tissue composition.”
Adaptive (vs locked)FDA source did not state this
PCCPFDA source did not state this
Cybersecurity addressedFDA source did not state this

Validation studies (5)

Retrospective clinical

sample size not stated

endpoints: Comparison of ClariSIGMAM-generated breast density estimates with Gold Standard breast density estimates

standards: ISO 14971 Medical devices - Application of risk management for medical devices, NEMA-PS 3.1- PS 3.20 Digital Imaging and Communications in Medicine (DICOM), Guidance for the Content of Premarket Submissions for Software Contained in Medical Devices issued May 11, 2005., Design Considerations and Pre-market Submission Recommendations for Interoperable Medical Devices issued September 6, 2017.

Retrospective clinical

sample size not stated

endpoints: Reproducibility of breast density estimates with age

standards: ISO 14971 Medical devices - Application of risk management for medical devices, NEMA-PS 3.1- PS 3.20 Digital Imaging and Communications in Medicine (DICOM), Guidance for the Content of Premarket Submissions for Software Contained in Medical Devices issued May 11, 2005., Design Considerations and Pre-market Submission Recommendations for Interoperable Medical Devices issued September 6, 2017.

Retrospective clinical

sample size not stated

endpoints: Reproducibility of breast density estimates over time

standards: ISO 14971 Medical devices - Application of risk management for medical devices, NEMA-PS 3.1- PS 3.20 Digital Imaging and Communications in Medicine (DICOM), Guidance for the Content of Premarket Submissions for Software Contained in Medical Devices issued May 11, 2005., Design Considerations and Pre-market Submission Recommendations for Interoperable Medical Devices issued September 6, 2017.

Retrospective clinical

sample size not stated

endpoints: Comparison of breast density estimates for left and right breasts

standards: ISO 14971 Medical devices - Application of risk management for medical devices, NEMA-PS 3.1- PS 3.20 Digital Imaging and Communications in Medicine (DICOM), Guidance for the Content of Premarket Submissions for Software Contained in Medical Devices issued May 11, 2005., Design Considerations and Pre-market Submission Recommendations for Interoperable Medical Devices issued September 6, 2017.

Reader study (MRMC)

n=837 cases

endpoints: Comparison of breast density group information (BI-RADS A+B as fatty and BI-RADS C+D as dense) between experts' visual assessment and automated assessment with ClariSIGMAM

standards: ISO 14971 Medical devices - Application of risk management for medical devices, NEMA-PS 3.1- PS 3.20 Digital Imaging and Communications in Medicine (DICOM), Guidance for the Content of Premarket Submissions for Software Contained in Medical Devices issued May 11, 2005., Design Considerations and Pre-market Submission Recommendations for Interoperable Medical Devices issued September 6, 2017.

Reported performance (3 observations)

accuracyas written: “Accuracy (Fatty)”86.6
source quote (p.8)
“Accuracy 86.6%”
accuracyas written: “Accuracy (Dense)”87.3
source quote (p.8)
“Accuracy 87.3%”
agreement_kappaas written: “Kappa”0.734CI [0.688, 0.781]
source quote (p.8)
“n=837; Kappa 0.734 [0.688, 0.781]”

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
3
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/K203785