Clarius Ejection Fraction AI
K253593Clarius Mobile Health Corp. · cleared 2026-03-02 · product code QIH · Radiology
Premarket evidence — what FDA accepted
source quote (p.6)
“Clarius Ejection Fraction AI is not a stand-alone software device.”
source quote (p.6)
“using a deep learning image segmentation algorithm.”
source quote (p.9)
“utilizing non-adaptive machine learning algorithms”
source quote (p.1)
“FDA's substantial equivalence determination also included the review and clearance of your Predetermined Change Control Plan (PCCP).”
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 (2)
Retrospective clinical
n=279 other
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
standards: IEC 62304:2006 + A1:2015, ISO 14971:2019, IEC 62366-1:2015 + A1:2020, ISO 15223-1:2021
Bench
sample size not stated
endpoints: 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)
source quote (p.15)
“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
Recall and MAUDE counts are product-code-level (reports aren't reliably attributable to one device). 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).