Clarius Median Nerve AI

K250226

Clarius Mobile Health Corp. · cleared 2025-05-08 · product code QIH · Radiology

Premarket evidence — what FDA accepted

Device typesimd
source quote (p.5)
“Clarius Median Nerve 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 musculoskeletal ultrasound applications, specifically intended for segmentation and measurement of the cross-sectional area of the median nerve. Clarius Median Nerve AI is not a stand-alone software device.”
Algorithmmachine learning algorithm, deep learning image segmentation algorithm, Artificial Intelligence (AI)/Machine Learning (ML), Image segmentation for border detection, and median nerve view classification using a Deep Neural Network.
source quote (p.5)
“Clarius Median Nerve 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 musculoskeletal ultrasound applications, specifically intended for segmentation and measurement of the cross-sectional area of the median nerve. During the ultrasound imaging procedure, the anatomical site is selected through a preset software selection (i.e., Hand/Wrist) from the Clarius App in which Clarius Median Nerve AI will segment the median nerve in transverse view (with a segmentation mask placed on the ultrasound image) and engage to automatically place calipers on the segmentation mask to measure the median nerve's cross-sectional area. Ultrasound image processing software implementing artificial intelligence utilizing non-adaptive machine learning algorithms trained with clinical and/or artificial data intended for segmentation and measurements of ultrasound data. Artificial Intelligence (AI)/Machine Learning (ML). Image segmentation for border detection, and median nerve view classification using a Deep Neural Network.”
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 segmentation and measurements of ultrasound data.”
PCCPYes
source quote (p.2)
“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 (1)

Retrospective clinical

n=126 patients · 12 site(s)

endpoints: determine whether Clarius Median Nerve AI measurements are non-inferior to those obtained manually by human experts/qualified ultrasound users; determine the correlation between Clarius Median Nerve AI segmentation and those of human experts; accurately identify the median nerve in transverse view at the level of the wrist or mid forearm

standards: IEC 62304:2006 + A1:2015, ISO 14971:2019, NEMA PS 3.1 - 3.20 (2022d), IEC 62366-1:2015 + A1:2020, ISO 15223-1:2021

Reported performance (4 observations)

agreement_kappaas written: “Intraclass Correlation Coefficient (ICC)”0.81CI 95% CI: 0.74, 0.87
source quote (p.14)
“The Intraclass Correlation Coefficient (ICC) of the Clarius Median Nerve AI versus the Mean of Reviewers cross sectional area is 0.81 (95% CI: 0.74, 0.87).”
iouas written: “Jaccard Score (Reviewer 1 vs Clarius Median Nerve AI)”0.62CI 95%CI: 0.62, 0.68
source quote (p.14)
“0.62 [95%CI: 0.62, 0.68]”
iouas written: “Jaccard Score (Reviewer 2 vs Clarius Median Nerve AI)”0.71CI 95%CI: 0.69, 0.74
source quote (p.14)
“0.71 [95%CI: 0.69, 0.74]”
iouas written: “Jaccard Score (Reviewer 3 vs Clarius Median Nerve AI)”0.68CI 95%CI: 0.65, 0.71
source quote (p.14)
“0.68 [95%CI: 0.65, 0.71]”

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/K250226