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This device is a digital radiography/fluoroscopy system used in a diagnostic and interventional angiography configuration. The system is indicate for use in diagnostic and angiographic procedures for blood vessels in the heart, brain, abdomen and lower extremities.

The Alphenix, INFX-8000V/B, /S V9.1, is an X-ray system that is capable of radiographic and fluoroscopic studies and is used in an interventional setting. The system consists of a C-arm/Omega-arm which is equipped with an X-ray tube, beam limiter and X-ray receptor, X-ray controller, computers with system and processing software, and a patient radiographic table.

The provided text describes a 510(k) premarket notification for a medical device, which focuses on demonstrating substantial equivalence to a predicate device rather than presenting a de novo study with specific clinical acceptance criteria. Therefore, the document does not contain the detailed information requested for a study proving device acceptance criteria in the typical sense of a clinical trial for a new product.

However, based on the information provided, here's what can be extracted and inferred regarding the device's assessment:

1. Table of Acceptance Criteria and Reported Device Performance

The submission is for modifications to a previously cleared device, focusing on maintaining or improving performance compared to the predicate. The "acceptance criteria" are primarily related to safety, effectiveness, and substantial equivalence to the predicate.

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

The document explicitly states: "Clinical images were deemed not necessary for the aforementioned improvements via design control and risk management activities."

Therefore, a "test set" in the sense of clinical patient data with specific sample sizes for this submission is not applicable. The testing primarily involved bench testing and phantom testing. The provenance of such phantom test data is not specified (e.g., country of origin), but it would typically be internal to the manufacturer. The data is retrospective in the sense that it's generated for the submission based on the modified device's performance.

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

Since clinical images were not used and the testing focused on technical performance via bench and phantom tests, there were no external experts establishing ground truth in the context of clinical diagnoses or interpretations for a "test set" of patient data. The "ground truth" for these engineering and phantom tests would be established by the device's technical specifications and engineering requirements, verified by the manufacturer's qualified personnel.

4. Adjudication Method for the Test Set

Not applicable, as a clinical test set requiring adjudication was not 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: "Clinical images were deemed not necessary for the aforementioned improvements."

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

The device is an interventional fluoroscopic X-ray system, not primarily an AI algorithm. While software improvements are noted, the "standalone" performance here refers to the system as a whole, including its imaging capabilities. The testing mentioned ("bench testing" and "phantom testing") represents the standalone performance of the modified system.

7. The Type of Ground Truth Used

For the technical and phantom testing performed, the "ground truth" was based on:

• Engineering specifications and design requirements: For safety, performance parameters, and compliance with standards.
• Physical properties of phantoms: For evaluating image metrics (e.g., AlphaCT IQ improvement, 2D IQ Improvement).
• Predicate device's established performance: As the basis for comparison to demonstrate "equal to or better" performance.

8. The Sample Size for the Training Set

Not applicable. The device is not an AI algorithm that undergoes "training" on a dataset in the machine learning sense to develop its primary function. The software changes are described as functional improvements, usability enhancements, and operating system updates, not as a deep learning model trained on a specific dataset.

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

Not applicable, as there was no explicit "training set" in the context of machine learning.

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