Clarius Bladder AI

K232257

Clarius Mobile Health Corp. · cleared 2023-11-13 · product code QIH · Radiology

Premarket evidence — what FDA accepted

Device typesimd
source quote (p.5)
Clarius Bladder Al is a radiological (ultrasound) image processing software application which implements artificial intelligence (AI), utilizing non-adaptive machine learning algorithms, and is incorporated into the Clarius App software for use as part of the complete Clarius Ultrasound Scanner system product offering in bladder ultrasound imaging applications. Clarius Bladder Al is not a stand-alone software device.
AlgorithmArtificial intelligence (AI) image segmentation algorithm, deep neural network (DNN), non-adaptive machine learning algorithms.
source quote (p.5)
Clarius Bladder Al is a radiological (ultrasound) image processing software application which implements artificial intelligence (AI), utilizing non-adaptive machine learning algorithms, and is incorporated into the Clarius App software for use as part of the complete Clarius Ultrasound Scanner system product offering in bladder ultrasound imaging applications. Clarius Bladder Al operates by performing automatic measurements of bladder height, width, and length, and calculates bladder volume. The user has the option to manually adjust the measurements made by Clarius Bladder Al by moving the caliper crosshairs. Clarius Bladder Al does not perform any functions that could not be accomplished manually by a trained and qualified user. Clarius Bladder Al is intended for use in B-Mode only. Image segmentation for border detection, and bladder view classification using a Deep Neural Network.
Adaptive (vs locked)No
source quote (p.5)
Clarius Bladder Al is a radiological (ultrasound) image processing software application which implements artificial intelligence (AI), utilizing non-adaptive machine learning algorithms
PCCPNo
Cybersecurity addressedYes
source quote (p.11)
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)

Retrospective clinical

n=66 patients

endpoints: verify that the Clarius Bladder Al is non-inferior to manual measurement by the expert clinician

Prospective clinical

n=58 patients

endpoints: verify that the Clarius Bladder Al is non-inferior to manual measurement by the expert clinician

Prospective clinical

sample size not stated

endpoints: evaluate the design and clinical utility of Clarius Bladder AI; determine if the device performs as intended in a representative user environment, meets the product requirements, is clinically usable, and meets users' needs for use in semi-automated bladder volume measurements

Reported performance (4 observations)

agreement_kappaas written: “Intraclass correlation coefficients (ICC) for inter-rater reliability (Retrospective)stated without value
source quote (p.13)
The intraclass correlation coefficients (ICC) for inter-rater reliability were calculated between reviewer pairs and between the Clarius Bladder Al output and the mean reviewer measurement.
diceas written: “Average dice scores and Jaccard index (Retrospective)stated without value
source quote (p.13)
The average dice scores and Jaccard index between the Clarius Bladder Al model vs. each reviewer and between each reviewer pair were also calculated.
agreement_kappaas written: “Intraclass correlation coefficients (ICC) for inter-rater reliability (Prospective)stated without value
source quote (p.14)
The intraclass correlation coefficients (ICC) for inter-rater reliability were calculated between reviewer pairs and between the Clarius Bladder Al output and the mean reviewer measurement.
diceas written: “Average dice scores and Jaccard index (Prospective)stated without value
source quote (p.14)
The average dice scores and Jaccard index between the Clarius Bladder Al model vs. each reviewer and between each reviewer pair were also calculated.

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
1
drift signals on this device
  • re_clearance

    The FDA AI/ML device list shows a newer 510(k) K233955 (decision 2024-06-14) from Clarius Mobile Health Corp. for a matching device line ("Clarius OB AI") — a new clearance for the same line is a change event.

    first seen 2026-07-08 · k_number:K233955

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).

RIGOR™ Precedent · public FDA/CMS data · descriptive decision-support, not regulatory or reimbursement advice. Share this page: radar.healthai.com/precedent/device/K232257