Clarius Ejection Fraction AI

K253593

Clarius Mobile Health Corp. · cleared 2026-03-02 · product code QIH · Radiology

Premarket evidence — what FDA accepted

Device typesimd
source quote (p.5)
“Clarius Ejection Fraction AI is a machine learning algorithm that is integrated into the Clarius App software as part of the complete Clarius Ultrasound Scanner system for use in cardiac ultrasound applications, specifically intended for use by trained healthcare practitioners for semi-automatic real-time measurement of the left ventricular (LV) ejection fraction (EF) on ultrasound image data acquired by the Clarius Ultrasound Scanner system (i.e., phased array and curvilinear scanners) using a deep learning image segmentation algorithm. (...) Clarius Ejection Fraction AI is not a stand-alone software device.”
Algorithmmachine learning algorithm using deep learning image segmentation
source quote (p.5)
“Clarius Ejection Fraction AI is a machine learning algorithm that is integrated into the Clarius App software as part of the complete Clarius Ultrasound Scanner system for use in cardiac ultrasound applications, specifically intended for use by trained healthcare practitioners for semi-automatic real-time measurement of the left ventricular (LV) ejection fraction (EF) on ultrasound image data acquired by the Clarius Ultrasound Scanner system (i.e., phased array and curvilinear scanners) using a deep learning image segmentation algorithm.”
Adaptive (vs locked)No
source quote (p.9)
“Ultrasound image processing software implementing artificial intelligence utilizing non-adaptive machine learning algorithms trained with clinical and/or artificial data intended for measurements of cardiac 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.11)
“Cybersecurity and vulnerability analyses were conducted, and it has been determined that Clarius conforms to the cybersecurity requirements by implementing a process of preventing unauthorized access, modifications, misuse or denial of use, or the unauthorized use of information that is stored, accessed or transferred from a medical device to an external recipient.”

Validation studies (3)

Bench

sample size not stated

standards: IEC 62304:2006 + A1:2015, ISO 14971:2019, IEC 62366-1:2015 + A1:2020, ISO 15223-1:2021

Retrospective clinical

n=279 cases · 30 site(s)

endpoints: determine whether Clarius Ejection Fraction AI measurements are non-inferior to those obtained manually by human experts/qualified ultrasound users by determining if the magnitude of the mean absolute difference between Clarius Ejection Fraction AI and mean reviewer measurements is greater than the magnitude of the mean absolute difference among reviewers themselves; determine the correlation between Clarius Ejection Fraction AI predictions and those of human experts among the different Clarius scanner models (i.e., C3 HD3, PA HD3)

Standalone

sample size not stated

endpoints: evaluate the design and clinical usage of Clarius Ejection Fraction AI, as it is integrated into the Clarius App software, to determine if it performs as intended in a representative user environment, meets the product requirements, is clinically usable, and meets users' needs for use in semi-automated measurements of the left ventricular ejection fraction

Reported performance (1 observation)

agreement_kappaas written: “ICC (AI EF vs. Mean Reviewers)”0.78CI [0.71 0.83]
source quote (p.15)
“Table 3: ICC values of Reviewers and Clarius Ejection Fraction AI (...) AI EF vs. Mean Reviewers 0.78 [0.71 0.83]”

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/K253593