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The Azurion series (within the limits of the Operation Room table) are intended for use to perform · Image guidance in diagnostic, interventional and minimally invasive surgery procedures for the following clinical application areas: vascular, non-vascular, cardiovascular and neuro procedures. · Cardiac imaging applications including diagnostics, interventional and minimally invasive surgery procedures. Additionally: - · The Azurion series can be used in a hybrid Operation Room · The Azurion series contain a number of features to support a flexible and patient centric procedural workflow.

The Azurion R2.1 is classified as an interventional fluoroscopic X-ray system. The primary performance characteristics of the Azurion R2.1 interventional fluoroscopic X-ray system include: Real-time image visualization of patient anatomy during procedures Imaging techniques and tools to assist interventional procedures Post processing functions after interventional procedures Storage of reference/control images for patient records Compatibility to images of other modalities via DICOM Built in radiation safety controls This array of functions offers the physician the imaging information needed to perform minimally invasive interventional procedures. The Azurion R2.1 is available in comparable models and configurations as the currently marketed and predicate device Azurion R1.2. Configurations are composed of detector type (monoplane and biplane), floor or ceiling mounted geometry, standard or OR table type and available image processing. The FlexArm option is available for the 7M20 configuration in Azurion R2.1 The monoplane (single C-arm) and biplane (dual arm) X-ray system configurations are differentiated by the following features: 12 inch Flat Detector (FD12) 15 inch Flat Detector (FD15) 20 inch Flat Detector (FD20) Additionally, identical to the predicate device, Azurion R2.1 can be used in a hybrid operating room when supplied with a compatible operating room table, and can be optionally equipped with the ClarityIQ image processing algorithms.

The provided text describes the Azurion R2.1, an image-intensified fluoroscopic x-ray system, and states that it did not require clinical study data to demonstrate substantial equivalence to its predicate device, Azurion R1.2. Therefore, this document does not contain the detailed information typically found in a study proving the device meets acceptance criteria through clinical performance data.

Instead, the submission relies on demonstrating substantial equivalence based on:

• Indications for Use
• Technological Characteristics
• Non-clinical performance testing
• Safety and Effectiveness

The "study that proves the device meets the acceptance criteria" in this context refers to the non-clinical verification and validation activities summarized.

Here's an analysis of the provided text in relation to your request, noting where information is explicitly stated and where it is absent because no clinical study was conducted:

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

The document mentions that "Results demonstrated that all executed verification tests were passed" and "Results demonstrated that all executed validation protocols were passed" in the non-clinical performance and validation sections. However, it does not provide a specific table of acceptance criteria with corresponding device performance metrics. The general acceptance criteria are compliance with specified standards, user needs, and safety requirements.

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

• Test Set Sample Size: Not applicable in the context of clinical images/data for AI performance evaluation, as this submission did not involve a clinical study or AI performance evaluation on a test set of patient images. The "test set" here refers to the system itself undergoing verification and validation.
• Data Provenance: Not applicable, as no patient data was used for a clinical performance study. The tests were non-clinical verification and validation of the device system.

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)

• Number of Experts: Usability validation was performed with "both interventional radiologists / cardiologists (physicians) and nurse/technicians." The exact number is not specified.
• Qualifications of Experts: Interventional radiologists/cardiologists (physicians) and nurse/technicians. Specific years of experience are not mentioned. Their role was to validate usability and workflow in a simulated environment, not to establish ground truth for image data.

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

• Adjudication Method: Not applicable. This was not a study involving human reader interpretation of images requiring adjudication for ground truth. The "validation protocols" were executed by users, and the passing criteria would have been pre-defined operational outcomes rather than subjective interpretations needing adjudication.

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

• MRMC Study: No. The document explicitly states: "The Azurion R2.1 did not require clinical study data since substantial equivalence to the currently marketed predicate device Azurion R1.2 was demonstrated..." This device is an X-ray system, not specifically an AI algorithm for image analysis that would typically undergo an MRMC study to show human reader improvement.

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

• Standalone Performance: Not explicitly stated as a separate "algorithm only" study. The device itself is an integrated system. The "ClarityIQ image processing algorithms" are mentioned as an optional feature, but their standalone performance is not detailed in this summary, nor is it the primary focus of this 510(k) submission which deems the entire device substantially equivalent.

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

• Type of Ground Truth: Not applicable in the traditional sense of a clinical image-based study. For the verification and validation tests mentioned, the "ground truth" was compliance with established engineering specifications, risk mitigation strategies, international consensus standards, FDA guidance documents, and user needs as validated through simulated use.

8. The sample size for the training set

• Training Set Sample Size: Not applicable. This document describes a medical device (an X-ray system), not an AI algorithm that would have a "training set" of patient data in the machine learning sense.

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

• Ground Truth for Training Set: Not applicable, as there was no AI training set as described for an AI algorithm.

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