Clarius Prostate AI

K243853

Clarius Mobile Health Corp. · cleared 2025-04-16 · product code QIH · Radiology

Premarket evidence — what FDA accepted

Device typesamd
source quote (p.6)
“Clarius Prostate AI is a machine learning algorithm that is integrated into the Clarius App software as part of the comprehensive Clarius Ultrasound Scanner system for use in prostate ultrasound imaging applications.”
Algorithmdeep learning image segmentation algorithm; Deep Neural Network
source quote (p.6)
“Clarius Prostate AI is intended for use by trained healthcare practitioners for measurement of prostate volume on ultrasound data acquired by the Clarius Ultrasound Scanner system (i.e., curvilinear and endo-cavitary scanners) using a deep learning image segmentation algorithm.”
Adaptive (vs locked)No
source quote (p.9)
“Ultrasound image processing software application implementing artificial intelligence utilizing non-adaptive machine learning algorithms trained with clinical and/or artificial data intended for segmentation and measurements of ultrasound data.”
PCCPYes
source quote (p.1)
“FDA's substantial equivalence determination also included the review and clearance of your Predetermined Change Control Plan (PCCP).”
Cybersecurity addressedYes
source quote (p.13)
“Cybersecurity Analysis”

Validation studies (2)

Retrospective clinical

n=139 patients

endpoints: determine whether Clarius Prostate AI prostate volume measurements are non-inferior to those obtained manually by human experts/qualified ultrasound users (if the magnitude of the difference (the absolute percent error) between Clarius Prostate AI and mean reviewer (human expert) measurements is greater than the magnitude of the mean difference (mean absolute percent error) between the reviewers themselves); determine the correlation between Clarius Prostate AI segmentation and those of human experts, whether it can accurately identify transverse and sagittal prostate views

standards: IEC 62304:2006 + A1:2015 - Medical device software — Software life cycle processes, ISO 14971:2019 Medical devices — Application of risk management to medical devices, NEMA PS 3.1 - 3.20 (2022d) Digital Imaging and Communications in Medicine (DICOM) Set, IEC 62366-1:2019 + A1:2020 Medical devices — Part 1: Application of usability engineering to medical devices, ISO 15223-1:2021 Medical devices — Symbols to be used with medical device labels, labelling and information to be supplied

Standalone

sample size not stated

Reported performance (3 observations)

accuracyas written: “Accuracy in view prediction”0.95
source quote (p.14)
“Bland-Altman plots indicated strong agreement between Clarius Prostate AI and expert measurements, with the model showing high accuracy in view prediction (95%).”
agreement_kappaas written: “ICC scores for endo-cavitary probes (C3 HD3, EC7 HD3)”0.87
source quote (p.14)
“The ICC scores for different probe models (i.e., C3 HD3, EC7 HD3) were 0.87 for endo-cavitary probes and 0.67 for curvilinear probes, highlighting expected variations in performance.”
agreement_kappaas written: “ICC scores for curvilinear probes”0.67
source quote (p.14)
“The ICC scores for different probe models (i.e., C3 HD3, EC7 HD3) were 0.87 for endo-cavitary probes and 0.67 for curvilinear probes, highlighting expected variations in performance.”

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/K243853