Clarius Median Nerve AI
K250226Clarius Mobile Health Corp. · cleared 2025-05-08 · product code QIH · Radiology
Premarket evidence — what FDA accepted
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.”
source quote (p.6)
“measurement of the cross-sectional area (CSA) of the median nerve on ultrasound data acquired by the Clarius Ultrasound Scanner system (i.e., linear array scanners) using a deep learning image segmentation algorithm.”
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.”
source quote (p.15)
“Modifications to Clarius Median Nerve Al will be made in accordance with its Predetermined Change Control Plan (PCCP). The PCCP provides a description of the device's planned modifications, a modification protocol to test, verify, validate, and implement the modifications in a manner that ensures the continued safety and effectiveness of the device, mitigating risks associated with changes to the Median Nerve Al model to not adversely impact the device's performance, safety, or effectiveness associated with its indications for use, and an impact assessment of the planned modifications.”
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=182 images · 11 site(s)
endpoints: determine whether Clarius Median Nerve Al measurements are non-inferior to those obtained manually by human experts/qualified ultrasound users; determine the correlation between Clarius Median Nerve Al segmentation and those of human experts; whether it can accurately identify the median nerve in transverse view at the level of the wrist or mid forearm.
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 62366-1:2015 + 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, General Principles of Software Validation, Final Guidance for Industry and FDA Staff (issued January 11, 2002), Guidance for the Content of Premarket Submissions for Device Software Functions (issued June 14, 2023), Cybersecurity in Medical Devices: Quality System Considerations and Content of Premarket Submissions (issued September 27, 2023)
Prospective clinical
sample size not stated
endpoints: evaluate the design and clinical usage of Clarius Median Nerve 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 median nerve cross-sectional area.
Reported performance (4 observations)
source quote (p.14)
“The Intraclass Correlation Coefficient (ICC) of the Clarius Median Nerve Al versus the Mean of Reviewers cross sectional area is 0.81 (95% CI: 0.74, 0.87).”
source quote (p.14)
“0.62 [95%CI: 0.62, 0.68]”
source quote (p.14)
“0.71 [95%CI: 0.69, 0.74]”
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
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).