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The VirtuoSaph™ Plus Endoscopic Vessel Harvesting System is indicated for use in minimally invasive surgery allowing access for vessel harvesting, and is primarily indicated for patients undergoing endoscopic surgery for arterial bypass. lt is indicated for cutting tissue and controlling bleeding through coagulation, and for patients requiring blunt dissection of tissue including dissection of blood vessels and dissection of blood vessels of the extremities. Extremity procedures include tissue dissection and/or vessel harvesting along the saphenous vein for coronary artery bypass grafting and peripheral artery bypass grafing.

The system consists of a disposable trocar, dissector, and harvester. The trocar is inserted into the leg incision and stays in place with the clip securely placed on the skin allowing fast conversion between procedural steps. The dissector or harvester rod accesses the saphenous vein by entering the trocar through the port. A reusable endoscope (which is not in the scope of this submission) enters the body through dissector or harvester, and has optical components that send an image from inside of the body to a video monitor for the clinician to view. Dissector rod dissects the saphenous vein and surrounding branches. Harvester cauterizes and cuts the branches of the saphenous vein allowing for the harvesting of it.

The provided text does not contain detailed acceptance criteria and a study proving the device meets them as typically presented for AI/ML devices. Instead, it is a 510(k) summary for a medical device (VirtuoSaph™ Plus Endoscopic Vessel Harvesting System) seeking substantial equivalence to predicate devices, not an AI/ML-based device requiring performance metrics like sensitivity, specificity, etc.

Therefore, most of the requested information regarding acceptance criteria, study design for AI, sample sizes, ground truth establishment, expert qualifications, and MRMC studies is not available in the given document.

The document primarily focuses on demonstrating the device's substantial equivalence to existing predicate devices based on design, materials, and intended use. Performance is likely assessed through bench testing and possibly animal or cadaver studies, but not in a way that generates the quantitative metrics requested for AI/ML performance.

Based on the provided text, the following can be inferred and explicitly stated:

• Device Name: VirtuoSaph™ Plus Endoscopic Vessel Harvesting System
• Purpose of Submission: Traditional 510(k) for substantial equivalence to predicate devices, not for an AI/ML device.

1. Table of Acceptance Criteria and Reported Device Performance:

• Acceptance Criteria: Not explicitly stated as quantitative performance metrics (e.g., sensitivity, specificity) for an AI/ML device. The acceptance criteria for this type of device would generally revolve around demonstrating safety and effectiveness comparable to predicate devices. This would typically involve:
  • Functional performance: The device performs its intended functions (cutting tissue, controlling bleeding through coagulation, blunt dissection, vessel harvesting) as safely and effectively as predicates.
  • Biocompatibility: Materials are safe for human contact.
  • Sterility: The device can be sterilized and maintained sterile.
  • Mechanical Integrity: The components withstand forces during use.
  • Electrical Safety (for electrosurgical components): Meets relevant standards.
• Reported Device Performance: The document states: "The VirtuoSaph™ Plus Endoscopic Vessel Harvesting System is substantially equivalent to both predicate devices in terms of intended use, principles of operation, technology, design, materials, and performance. Any noted differences between the devices are minor and do not raise new issues of safety and effectiveness." This "performance" statement is high-level and does not provide specific quantitative metrics.

2. Sample Size for Test Set and Data Provenance:
* Not applicable/Not mentioned. The document describes a medical instrument, not an AI/ML system that uses a "test set" of data in the common sense. Performance testing would typically involve bench testing, perhaps animal models, or cadaver studies, but no details on sample sizes or data provenance for such studies are provided.

3. Number of Experts and Qualifications for Ground Truth:
* Not applicable/Not mentioned. "Ground truth" in the context of expert consensus on data is not relevant for this type of device submission.

4. Adjudication Method:
* Not applicable/Not mentioned.

5. MRMC Comparative Effectiveness Study:
* Not applicable/Not mentioned. This is not an AI-assisted device.

6. Standalone Performance Study:
* Not applicable/Not mentioned in the context of an AI algorithm. Device performance would be evaluated in a standalone manner (without human-in-the-loop "AI assistance") for its intended functions, but the specific studies and results are not detailed in this summary.

7. Type of Ground Truth Used:
* Not applicable/Not mentioned in the context of AI/ML. For this device, "ground truth" would relate to the successful and safe performance of its mechanical and electrical functions in a surgical setting, compared to established predicate devices. This would likely be assessed through engineering tests, pre-clinical evaluations, and clinical experience with similar devices, rather than labeled data.

8. Sample Size for Training Set:
* Not applicable/Not mentioned. This is not an AI/ML device requiring a training set.

9. How Ground Truth for Training Set Was Established:
* Not applicable/Not mentioned.

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