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The Veradius 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 babies, within the limits of the device. The device is to be used in health care facilities both inside and outside the operating room, sterile as well as non-sterile environment in a variety of procedures.

Applications:

• Orthopedic Neuro Abdominal Vascular Thoracic Cardiac

The Veradius device is a Mobile C-arm X-ray System designed for medical applications during diagnostic, interventional and surgical procedures.

The device consists mainly of two 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, mains control unit, an user interface for image/patient handling and optionally an integrated workstation).

All movements of the C-arm stand are manual except the height movement. The Mobile viewing station can be used standalone for reviewing and archiving purposes,

Here's an analysis of the provided text regarding the Veradius device, focusing on acceptance criteria and supporting studies:

It's important to note that the provided 510(k) summary is for a new version of a mobile C-arm X-ray system, where the primary change is the Image Detection Subsystem (IDS), specifically replacing an Image Intensifier with a Flat Detector. The summary emphasizes substantial equivalence to a predicate device (Pulsera K061685). Therefore, the "acceptance criteria" and "device performance" are primarily focused on demonstrating that the new component (IDS) does not compromise image quality or safety and maintains the same intended use as the predicate.

1. Table of Acceptance Criteria and Reported Device Performance

Given the nature of this 510(k) summary (substantial equivalence for a component change), the acceptance criteria are not explicitly stated in quantitative metrics like sensitivity/specificity for a diagnostic AI. Instead, they relate to overall image quality and functional equivalence to the predicate device.

Study Details from the Provided Text:

This document is a premarket notification (510(k)), not a detailed study report. As such, it provides summary statements rather than in-depth methodological details of the studies.

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

• Sample Size for Test Set: Not explicitly stated. The text mentions "comparison between images pairs taken during non-clinical performance tests." This implies a set of images was used, but the quantity is not provided.
• Data Provenance: The study is described as "non-clinical performance tests." This suggests the data was likely generated in a controlled, engineering-focused environment within Philips Medical Systems Nederland B.V. (The Netherlands, where the manufacturer is located). Given it's a "non-clinical" test, it likely involved phantoms or standardized test objects, not patient data in the clinical sense of "retrospective or prospective."

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

• Number of Experts: Not mentioned.
• Qualifications of Experts: Not mentioned.

4. Adjudication Method for the Test Set

• Adjudication Method: Not mentioned. This type of detail is usually found in a full study report, which is not this document. Since the evaluation was likely "non-clinical performance tests" focused on image quality comparison, it may not have required formal adjudication of clinical diagnoses.

5. If a Multi-Reader Multi-Case (MRMC) Comparative Effectiveness Study Was Done

• MRMC Study: No, an MRMC study was not explicitly mentioned or described. The performance evaluation focuses on "Image Quality is equal or even better" based on the "comparison between images pairs taken during non-clinical performance tests." This typically implies objective measurements or subjective comparison by engineers/specialists, not a formal MRMC study involving human readers diagnosing clinical cases with and without AI assistance (as this is an X-ray system, not an AI diagnostic tool in the modern sense).

6. If a Standalone Performance (Algorithm Only) Was Done

• Standalone Performance: The description of "non-clinical performance tests" to evaluate "Image Quality" of the new "Image Detection Subsystem (IDS)" is essentially a standalone performance evaluation of the system's image acquisition and processing capabilities. The IDS itself is an algorithm-driven component (converting X-rays to digital images and applying calibration). However, it's not "algorithm only" in the sense of a pure AI diagnostic software; it's a core hardware/software component of the imaging chain.

7. The Type of Ground Truth Used (Expert Consensus, Pathology, Outcomes Data, etc.)

• Type of Ground Truth: Not explicitly stated. For "non-clinical performance tests" assessing "Image Quality," the ground truth likely involved:
  • Objective image quality metrics: Such as spatial resolution, contrast-to-noise ratio, modulation transfer function (MTF), dose efficiency, etc., measured using phantoms.
  • Reference images: Comparison against images produced by the predicate device under identical conditions, or against established standards for image quality.

8. The Sample Size for the Training Set

• Sample Size for Training Set: Not applicable in the context of advanced AI models that require large training datasets. The "Veradius" is a C-arm X-ray system with a new detector, not an AI diagnostic algorithm that learns from vast image datasets to identify pathologies. The "IDS" performs image conversion and calibration, which are rule-based or empirically derived processes, not deep learning-based training.

9. How the Ground Truth for the Training Set Was Established

• Ground Truth for Training Set: Not applicable for the same reasons as #8. The "training" of such a system would involve engineering and calibration procedures during design and manufacturing, not data-driven machine learning.

Summary Takeaway:

This 510(k) emphasizes substantial equivalence for a physical device (mobile C-arm X-ray system) with a component change (detector type). The "studies" mentioned are "non-clinical performance tests" designed to show that the new detector either maintains or improves image quality and that the overall system remains as safe and effective as its predicate. It is not an AI diagnostic device, so many of the requested details concerning AI-specific study methodologies (MRMC, large training sets, expert consensus for ground truth) are not present in this type of submission.

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