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The Garion is intended to be used and operated by: adequately trained, qualified and authorized health care professionals 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, sterile as well as non-sterile environment in a variety of procedures.

GARION is mobile fluoroscopy system is designed to provdie fluoroscopic and spot-film images of thepatient during diagnostic, surgical procedures. This device is a digital X-ray radiographic equipment that consists of high voltage generator, X-ray control unit, X-ray tube unit, Collimator, image processing unit. The image processing unit consists of medical image detector, power supply, medical image collecting unit and relevant software.

The provided text describes a 510(k) premarket notification for a medical device called "Garion", an image-intensified fluoroscopic x-ray system. The document focuses on demonstrating substantial equivalence to a legally marketed predicate device, "Zenition 70".

Here's an analysis of the acceptance criteria and study information based on the provided text:

1. Table of Acceptance Criteria and Reported Device Performance:

The document focuses on demonstrating substantial equivalence to a predicate device rather than defining specific performance acceptance criteria for the Garion device against a set of quantitative metrics. The "performance" is implicitly assessed through a comparison of technical characteristics to the predicate.

The acceptance criterion, in this context, is that the Garion device's technological characteristics are substantially equivalent to the predicate device, implying similar safety and effectiveness. The reported performance is the direct comparison of these characteristics.

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

The document explicitly states: "9. Summary of clinical testing: Not required for a finding of substantial equivalene." This indicates that no clinical test set was used to evaluate the device's performance against specific acceptance criteria. The evaluation relies on comparing the technical specifications of the new device to the predicate device and adherence to relevant IEC standards.

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

Since no clinical test set was used, there were no experts establishing ground truth for a test set.

4. Adjudication Method for the Test Set:

Not applicable, as no clinical test set was used.

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

No, an MRMC comparative effectiveness study was not done. The document states that clinical testing was not required for substantial equivalence.

6. If a Standalone (i.e., algorithm only without human-in-the-loop performance) Was Done:

The "Garion" is an X-ray system, not an AI algorithm. Therefore, the concept of "standalone (algorithm only)" performance is not applicable in the same way as it would be for a pure software device. The evaluation is of the hardware and integrated software for image acquisition and display.

7. The Type of Ground Truth Used:

The "ground truth" for the substantial equivalence claim is effectively the established safety and effectiveness of the legally marketed predicate device (Zenition 70), supported by the device's compliance with applicable performance standards (IEC and FDA Radiation Safety Performance Standard). The comparison of technical specifications aims to show that the Garion device is sufficiently similar to the predicate to assume similar safety and effectiveness.

8. The Sample Size for the Training Set:

The concept of a "training set" is typically associated with machine learning or AI algorithms. Since this submission is for an X-ray system and does not mention any AI components requiring supervised learning, there is no training set in the conventional sense.

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

Not applicable, as there is no mention of a training set or AI algorithms requiring ground truth establishment.

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The proposed Zenition 70 is intended to be used and operated by: adequately trained, qualified and authorized health care professionals 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. 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 proposed Zenition 70 is a mobile, diagnostic X-ray imaging and viewing system. It is designed for medical use in healthcare facilities where X-ray imaging is needed. The system comprises two main components: The C-arm stand and a mobile view station.

The provided text is a 510(k) summary for the Philips Zenition 70, a fluoroscopic X-ray system. The submission focuses on demonstrating substantial equivalence to a predicate device (previous Zenition 70 model K183040) and a reference device (Digiscan FDX K200218) by adding a new flat panel detector (FD17) and some software/UI updates.

The document states that no clinical studies were required as substantial equivalence was demonstrated through non-clinical performance testing and similarity in indications for use and technological characteristics. Therefore, the device's acceptance criteria are primarily met through compliance with recognized standards and verification of technical performance, rather than a clinical study involving human readers or a set of clinical cases with ground truth.

Here's an analysis of the provided information based on your request:

1. A table of acceptance criteria and the reported device performance

The document doesn't present a table of specific clinical acceptance criteria and corresponding performance metrics from a study involving clinical cases. Instead, the acceptance criteria are largely defined by adherence to a comprehensive list of international and FDA-recognized consensus standards and FDA guidance documents. The "reported device performance" is the statement that the device complies with these standards.

2. Sample size used for the test set and the data provenance (e.g. country of origin of the data, retrospective or prospective)

The document explicitly states: "The proposed Zenition 70 did not require clinical study since substantial equivalence to the currently marketed and predicate device Zenition 70 was demonstrated with the following attributes: Indications for use; Technological characteristics; Non-clinical performance testing; and Safety and effectiveness."
Therefore, there was no test set of clinical cases that a sample size would apply to. The testing involved non-clinical performance evaluations against standards.

3. Number of experts used to establish the ground truth for the test set and the qualifications of those experts (e.g. radiologist with 10 years of experience)

Not applicable, as no clinical test set requiring expert ground truth was performed.

4. Adjudication method (e.g. 2+1, 3+1, none) for the test set

Not applicable, as no clinical test set requiring adjudication was performed.

5. If a multi reader multi case (MRMC) comparative effectiveness study was done, If so, what was the effect size of how much human readers improve with AI vs without AI assistance

Not applicable. This is a submission for an X-ray imaging system, not an AI-powered diagnostic device, and no MRMC study was conducted.

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

Not applicable, as this is an imaging system, not a standalone algorithm. Its performance is evaluated through technical standards.

7. The type of ground truth used (expert consensus, pathology, outcomes data, etc.)

The "ground truth" for this submission comes from the established and recognized international and FDA standards for medical electrical equipment and X-ray imaging devices. The performance of the new FD17 detector was compared to the predicate detectors, where "image quality performance...is compared and found to be equal." This comparison is technical/physical, not clinical.

8. The sample size for the training set

Not applicable, as this is an X-ray system. The "training" for such a device involves engineering, design, and manufacturing processes compliant with quality systems, not machine learning training on a dataset.

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

Not applicable, as there is no "training set" in the context of machine learning for this device.

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