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The Veradius Unity device is intended to be used and operated by: adequately trained, qualified, and authorized health care professionals such as physicians, surgeons, cardiologists, and radiographers, who have full understanding of the safety information and emergency procedures as well as the capabilities and functions of the device. The device is used for radiological guidance and visualization during diagnostic, interventional, and surgical procedures on all patients except neonates (birth to one month), within the limits of the device is to be used in health care facilities both inside and outside the operating room, in sterile environments, in a variety of procedures.

The proposed Veradius Unity is a counterbalanced C-arm with a thin flat detector x-ray system. The system consists of two main component parts: the C-arm stand (comprising X-ray generator and X-ray tube, Flat Detector and the X-ray control user interface) and the mobile viewing station (comprising the image processor, monitors, user interface for image/patient handling and optionally an integrated workstation)

Here's the breakdown of acceptance criteria and the study that proves the device meets them, based on the provided text:

Device Name: Veradius Unity

1. Table of Acceptance Criteria and Reported Device Performance

2. Sample Size Used for the Test Set and Data Provenance

The document does not specify a sample size for a test set in the context of imaging data or patient studies. The "testing" referred to is primarily non-clinical performance and verification/validation testing of the device's components and new features. The provenance of any underlying data used for these tests is also not mentioned (e.g., country of origin, retrospective/prospective). It explicitly states: "The subject of this premarket submission Veradius Unity did not require clinical studies to support substantial equivalence because the Veradius Unity utilizes identical components throughout the imaging chain and the system's equivalent performance in light of the modifications could be supported through verification and validation testing alone."

3. Number of Experts Used to Establish Ground Truth for the Test Set and Qualifications

Not applicable. As no clinical studies were performed, there was no separate "ground truth" for a test set established by medical experts in the typical sense of evaluating diagnostic accuracy. The testing focused on device functionality and engineering performance.

4. Adjudication method for the test set

Not applicable, as no clinical studies with a test set requiring adjudication were conducted.

5. If a Multi-Reader Multi-Case (MRMC) comparative effectiveness study was done

No, an MRMC comparative effectiveness study was not done. The submission explicitly states that "clinical studies to support substantial equivalence" were not required.

6. If a standalone (i.e. algorithm only without human-in-the-loop performance) was done

Not applicable, as this device is an imaging system (hardware and software), not an AI algorithm designed to operate entirely without human input for image interpretation or diagnosis. The "Outline tool" and "ClearGuide" are features embedded within the system to assist human users, not standalone diagnostic algorithms.

7. The type of ground truth used

For the non-clinical performance tests, the "ground truth" was established by engineering specifications, international standards (IEC, ISO), and FDA guidance documents. The device's performance was validated against these predefined technical requirements and functional standards. There was no "pathology" or "outcomes data" ground truth collection mentioned.

8. The sample size for the training set

Not applicable. This document describes a medical imaging device (C-arm X-ray system), not an AI algorithm that would typically require a "training set" of data for machine learning. The "improvements" are hardware and software features, not AI model updates.

9. How the ground truth for the training set was established

Not applicable, as there was no training set for an AI algorithm.

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