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The Surgery System is indicated for cutting and coagulation of soft tissue during General, Plastic and Reconstructive (including but not limited to skin incisions and development of skin flaps), ENT, Gynecologic, Orthopaedic, Arthroscopic, Spinal and Neurological procedures.

The PlasmaBlade™ X 3.0S LIGHT is a single-use, monopolar RF device. It is designed to be used with the qualified Generator as part of the Surgery System. It can be operated with the integrated hand switch or a qualified Footswitch. The PlasmaBlade™ X 3.0S LIGHT consists of a single bendable blade and telescoping shaft that can be configured in both standard and extended length. The finger grip also incorporates a suction lumen for the evacuation of smoke and fluids. The device also has integrated LED-based illumination.

The provided document is a 510(k) premarket notification for a medical device called the PlasmaBlade X 3.0S LIGHT. This document outlines the regulatory approval process and includes summaries of non-clinical testing conducted to demonstrate substantial equivalence to previously cleared predicate devices.

It is important to note that this document describes a conventional electrosurgical device, NOT an AI/Algorithmic Decision Support (ADS) device. Therefore, the specific questions regarding acceptance criteria and studies that prove an AI device meets those criteria (e.g., sample size, expert consensus for ground truth, MRMC studies, standalone performance, training set details) are not applicable to the content provided.

The document demonstrates a different type of substantial equivalence claim, which is based on demonstrating that the new device has the same intended use, technological characteristics, and performs as safely and effectively as a legally marketed predicate device, with any differences not raising new questions of safety or effectiveness.

However, I can extract the closest analogous information from the provided text to answer the spirit of your request as it pertains to this type of medical device submission.

Here's an interpretation of the requested information based on the provided 510(k) summary for the PlasmaBlade X 3.0S LIGHT:

Acceptance Criteria and Device Performance (Analogous to a non-AI device submission):

For this conventional medical device, "acceptance criteria" are not defined as statistical metrics for an AI algorithm's performance (e.g., sensitivity, specificity, AUC). Instead, they relate to demonstrating that the new device's performance characteristics are equivalent to its predicate devices and meet established safety and performance standards for electrosurgical devices. The "study that proves the device meets the acceptance criteria" refers to the non-clinical (bench and animal) testing conducted.

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

2. Sample size used for the test set and the data provenance

• Test Set (Non-Clinical):
  • Bench Testing: The document states "Bench testing was completed" across various parameters (blade geometry, coating effect on performance, LED light specifications). Specific sample sizes (N of devices tested) are not provided in this summary.
  • Animal Testing: "Thermal damage testing was completed on a porcine model." It mentions "muscle tissue testing was completed in-vivo, while the skin and liver tissue testing was completed ex-vivo." The exact number of animals or tissue samples is not specified.
  • Data Provenance: The testing appears to be prospective as it was conducted specifically for this submission. The country of origin of the data is not explicitly stated, but it would have been conducted by Medtronic Navigation, an entity based in Louisville, Colorado, USA.

3. Number of experts used to establish the ground truth for the test set and the qualifications of those experts

• Not Applicable: For this type of device, "ground truth" as it relates to expert medical evaluation of images/data for AI performance is not relevant. The "ground truth" for performance is established through physical measurements (bench testing) and biological responses (animal tissue reactions) against established engineering and biological standards, and comparison to predicate device performance. This does not involve human expert consensus on medical findings.

4. Adjudication method for the test set

• Not Applicable: No adjudication method as understood for AI model evaluation (e.g., 2+1 radiologist review) was used. Testing results were presumably analyzed by qualified engineers and scientists.

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 device is an electrosurgical instrument, not an AI/ADS system. MRMC studies are not relevant for demonstrating the substantial equivalence of such a device. The device does not assist human "readers" in interpreting diagnostic information.

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

• Not Applicable: This is an electrosurgical instrument, not an AI algorithm. Its performance is evaluated through its physical and biological effects, independent of human interpretation of a digital output.

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

• Analogous "Ground Truth":
  • Bench Testing: Engineering specifications, physical measurements, and established performance parameters for electrosurgical cutting and coagulation.
  • Animal Testing: Histopathological analysis of tissue damage, visual assessment of surgical effects, and comparison to documented effects of predicate devices. This is a form of scientific and empirical "ground truth."
  • Standards Compliance: Ground truth is defined by the technical requirements and limits specified in recognized consensus standards (e.g., ISO 11135, ISO 10993 series, IEC 60601 series).

8. The sample size for the training set

• Not Applicable: This is not an AI/machine learning device. There is no "training set." The device's design is based on engineering principles and existing predicate device designs, not on data-driven algorithmic training.

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

• Not Applicable: As there is no training set, this question is not relevant. The "ground truth" for the device's design and validation is derived from established medical device standards, regulatory guidance, and scientific understanding of electrosurgical principles.

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