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The Voyant electrosurgical generator is indicated for use with Voyant devices in open and laparoscopic surgical procedures where the electrosurgical ligation of vessels or tissue bundles is desired.

The Applied Medical Voyant Electrosurgical Generator (ESG) is a tabletop radiofrequency (RF) power supply designed for use in electrosurgery. It operates outside the sterile field and is equipped with a receptacle for Voyant hand piece devices. The ESG enclosure is constructed of various metals and polymers and houses all electrical hardware and software components. The front panel features an LCD and buttons for the navigation, adjustment, and selection of ESG settings.

This document is a 510(k) Premarket Notification from the FDA regarding the Voyant Electrosurgical Generator. It primarily focuses on demonstrating substantial equivalence to a predicate device rather than presenting a novel AI/ML device that requires extensive acceptance criteria for predictive performance.

Therefore, many of the requested elements for an AI/ML device cannot be found in this document because they are not applicable to the type of device and study presented. The "device" in question is an electrosurgical generator, not an AI model.

Here's a breakdown of the information that can be extracted and what cannot:

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

The document does not specify quantitative acceptance criteria in the format typically seen for AI/ML performance metrics (e.g., accuracy, sensitivity, specificity thresholds). Instead, the "acceptance criteria" are implied by demonstrating "substantial equivalence" to a predicate device. The performance is assessed through various engineering and system tests comparing the subject device to its predicate.

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

This information is not provided in the document. The testing described (burst pressure, thermal spread, electrical safety, software testing) suggests engineering and bench testing, not a clinical "test set" of patient data as would be used for an AI/ML model. The provenance would be the lab where testing was conducted, but details are absent.

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)

This is not applicable/provided. Since this is not an AI/ML diagnostic device, there is no "ground truth" established by human experts in the sense of image interpretation or disease diagnosis. The "ground truth" for the engineering tests would be derived from the physical properties and measurements from the predicate device and the subject device.

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

This is not applicable/provided. No adjudication method for expert review of a test set is mentioned, as it's not relevant to this type of device submission.

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

There was no MRMC study done. This is not an AI-assisted diagnostic device. The study focused on the substantial equivalence of the electrosurgical generator itself, not on improving human reader performance. In fact, the document explicitly states: "No chronic survival or clinical studies were required to demonstrate the safety and efficacy of the subject device in support of this application for premarket clearance."

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

This is not applicable. This is not an algorithm, but an electrosurgical generator. Its performance is inherent to its electrical and mechanical design.

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

The "ground truth" (or basis for comparison) in this context is the performance of the legally marketed predicate device. For specific tests:

• Burst pressure and thermal spread: Measured physical properties directly compared to the predicate.
• Electrical safety and EMC: Compliance with international standards (IEC 60601 series).
• Software: Verification against software requirements.

8. The sample size for the training set

This is not applicable/provided. There is no "training set" as this is not an AI/ML device.

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

This is not applicable/provided. As above, there is no AI/ML training set.

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The Voyant electrosurgical generator is indicated for use with Voyant devices in open and laparoscopic surgical procedures where the electrosurgical ligation of vessels or tissue bundles is desired.

The Voyant 5mm Sealer-Divider device is a bipolar, electrosurgical device indicated for use with the Voyant electrosurgical generator in laparoscopic procedures where the ligation and division of vessels and tissue bundles is desired.

The device can seal and divide vessels up to and including 7mm in diameter and tissue bundles that can be captured in the jaws of the device.

The device has not been shown to be effective for tubal sterilization or tubal coagulation for sterilization procedures, and should not be used for these procedures.

The Applied Medical Voyant Electrosurgical Generator (ESG) is a tabletop radiofrequency (RF) power supply designed for use in electrosurgery. It operates outside the sterile field and is equipped with receptacles for Applied Medical bipolar devices. The ESG enclosure is constructed of various metals and polymers and houses all electrical hardware and software components. The front panel features an LCD and backlit buttons for the navigation, adjustment, and selection of ESG and device settings.

The Applied Medical Voyant 5mm Laparoscopic Tissue Sealer-Divider instrument is designed for use with the Voyant ESG. This device is an advanced bipolar instrument that uses RF energy, provided by the generator, to seal vessels up to and including 7mm in diameter. The device may also be used to seal tissue bundles that can be captured in the device jaws. The device features a mechanical, useractuated blade for the division of sealed tissue.

The provided text describes a 510(k) premarket notification for the Voyant® Electrosurgical Generator and Voyant® 5mm Sealer-Divider System. It outlines the device's indications for use, technological characteristics, and performance testing conducted to demonstrate substantial equivalence to predicate devices. However, the document does not contain a table of acceptance criteria and reported device performance in a way that directly answers the request. It states that testing was performed but does not list specific numerical criteria or outcomes in a clear tabular format for direct comparison.

Therefore, for the information requested in your prompt, I can only extract what is available and highlight what is missing.

Here's an attempt to answer based on the provided text, noting the limitations:

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

The document states its Indications for Use which implicitly sets a performance expectation: "The device can seal and divide vessels up to and including 7mm in diameter and tissue bundles that can be captured in the jaws of the device."

However, specific numerical acceptance criteria (e.g., minimum burst pressure, maximum thermal spread) and the precise reported performance values against these criteria are not explicitly presented in a table. The document generally concludes that the system is "substantially equivalent in performance to the predicate system with respect to vessel sealing performance and local tissue effects."

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: The document mentions "preclinical bench (ex vivo porcine vessels/tissue)" and "acute animal study (in vivo canine model)," and "a chronic study (in vivo canine model)." No specific sample sizes (e.g., number of porcine vessels, number of canine subjects) are provided.
• Data Provenance:
  • Country of Origin: Not specified.
  • Retrospective or Prospective: The preclinical and animal studies are prospective in nature, as they involve testing the device under controlled conditions.

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)

This section is not applicable as the studies are device performance studies on animal/ex vivo models, not studies requiring expert interpretation of diagnostic images or clinical outcomes in humans to establish ground truth. The "ground truth" for these types of studies would be objective measurements (e.g., burst pressure measurements, histological assessment of thermal damage) performed by trained technicians or researchers. The document does not specify the number or qualifications of individuals performing these measurements.

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

This section is not applicable for the type of device performance studies described. Adjudication methods are typically used in clinical or diagnostic studies to resolve discrepancies in expert interpretation of data (e.g., radiology reads).

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

This is not applicable. The document describes the performance of an electrosurgical device, not an AI-powered diagnostic or assistive tool for human readers. No MRMC study was conducted or mentioned.

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

This is not applicable. The device is an electrosurgical instrument used by a surgeon, not a standalone algorithm. Its performance inherently involves human-in-the-loop operation.

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

The "ground truth" for the performance studies appears to be based on:

• Objective physical measurements: Energy output, mechanical strength, durability, electrical safety, burst pressures of sealed vessels.
• Histopathological assessment: Evaluation of thermal damage in tissue, and chronic hemostasis in the animal studies.
• Comparative performance: Against a legally marketed predicate device to establish "substantial equivalence."

8. The sample size for the training set

This is not applicable. The document describes a physical medical device (electrosurgical generator and sealer-divider), not a machine learning or AI model that requires a "training set."

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

This is not applicable for the reasons stated in point 8.

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