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The BoVac Electrosurgery Suction Device removes smoke, odors, gases, particles, and fluids directly from the surgical site during electrosurgery procedures. The device is single-use and disposable.

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This document is a 510(k) clearance letter for the "BoVac Electrosurgery Suction Device." It indicates that the device has been found substantially equivalent to a predicate device, but it does not contain acceptance criteria or a study proving device performance against such criteria.

The 510(k) clearance process focuses on demonstrating substantial equivalence to a legally marketed predicate device, rather than proving performance against pre-defined acceptance criteria through a dedicated study. This type of document typically does not include the detailed study information requested in the prompt.

Therefore, I cannot provide the requested information based on the provided text.

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The intended use of the cutting and coagulation accessory is to ablate tissue and/or cauterize. The devices are indicated for use in joints such as, ankles, knees, hips, wrist, elbow, and shoulders.

The cutting and coagulation system consists of a system controller that is connected to a power source. Two cables extend from the system controller, one to a foot-pedal to allow the operator to control the device; the other cable leads to the wand portion, which functions in the surgical site. The wand is the accessory for which MIT has developed the reprocessing technology.

This submission (K012640) describes the reprocessing of electrosurgical wands. The document provided does not contain the detailed acceptance criteria or a study demonstrating that the reprocessed device meets such criteria in a format applicable to AI/ML device studies. This 510(k) is for a reprocessed electrosurgical wand, not an AI/ML device. Therefore, the questions regarding AI/ML device performance (like sample sizes for test/training sets, expert ground truth, adjudication methods, MRMC studies, or standalone performance) are not applicable to this submission.

However, based on the provided text, the "acceptance criteria" are implicitly met by demonstrating substantial equivalence to predicate devices and adherence to general controls. The primary "study" or justification for acceptance is the assertion that the reprocessed device maintains the same technological characteristics and intended use as the original, legally marketed predicate devices.

Here's an attempt to structure the information based on the prompt, acknowledging that much of it is not relevant to a traditional AI/ML device evaluation:

1. Table of acceptance criteria and the reported device performance

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

• Not Applicable (N/A). This K012640 submission is for a reprocessed medical device (an electrosurgical wand), not an AI/ML diagnostic or prognostic device requiring a test set of data for performance evaluation in the usual sense. The testing mentioned (e.g., dielectric test for sheathing) would have been performed on the reprocessed physical devices.

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)

• N/A. Ground truth in the context of expert consensus for AI/ML performance is not applicable to a submission for a reprocessed physical device. Device functionality and material equivalence are evaluated through engineering tests and comparison to specifications.

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

• N/A. Not applicable, as there isn't a "test set" requiring expert adjudication for 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

• N/A. This is not an AI/ML device, so MRMC studies are not relevant.

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

• N/A. This is not an AI/ML device.

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

• N/A in the context of AI/ML ground truth. For this device, the "ground truth" for demonstrating equivalence would be based on:
  • Predicate Device Specifications: The known design, material, and performance specifications of the original Mitek (K974022) or Arthrocare Wands (K962321).
  • Engineering Tests: Such as the dielectric test mentioned for the sheathing, demonstrating specific physical properties.
  • Functional Testing: Ensuring the reprocessed device performs its intended functions (ablation, cauterization) as expected.

8. The sample size for the training set

• N/A. Not an AI/ML device.

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

• N/A. Not an AI/ML device.

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The DeRoyal Grounding Pad Adapter is indicated for use whenever the ESU machine requires a grounding pad of the type provided by SurgiMate, or the connector on the SurgiMate pad does not match the intended jack of the ESU machine but its use would make the electrical connection compatible.

The DeRoyal Surgical Electrosurgical Grounding Pad Adapter functions in the same manner as predicate devices in that it sed to connect one type of electrosurgical grounding pad to a particular type of jack on the electrosurgical cautery ... achine being used.

Device Design/Materials Used/Physical Properties: The DeRoyal Grounding Pad Adapter is made of materials commonly used for their purpose. The concept of use is that the adapter converts one type of connection to another type of connection without any alteration of the circuit or any impact on the original circuit.

This document is a 510(k) summary for an electrosurgical grounding pad adapter. It describes a simple connector device and asserts its equivalence to predicate devices. The information provided is insufficient to answer most of your detailed questions regarding acceptance criteria and study design for performance. This type of device does not typically involve the kinds of advanced algorithms or human-in-the-loop studies implied by your questions.

Here's why and what can be extracted:

• Device Type: This is a physical adapter for connecting electrosurgical grounding pads. It's a passive component that facilitates a connection without altering the electrical circuit.
• Nature of the Submission: This is a 510(k) premarket notification, which focuses on demonstrating substantial equivalence to a legally marketed predicate device, rather than proving novel clinical effectiveness with complex studies.
• Missing Information: The document does not contain details about performance metrics, clinical studies, sample sizes, ground truth establishment, or expert reviews, as these are typically not required for this class and type of device. The "performance" for such a device is primarily about fit, electrical conductivity, and safety, often demonstrated through bench testing and adherence to standards rather than clinical trials with human readers.

However, I will attempt to address the parts of your request that can be inferred or directly stated from the provided text, and explicitly state when information is missing.

Acceptance Criteria and Study Information for DeRoyal Industries, Inc. Electrosurgical Grounding Pad Adapters

Given the nature of the device (a passive electrosurgical grounding pad adapter) and the context of a 510(k) premarket notification from 1996, the concept of "acceptance criteria" and "study" as typically applied to AI/software-driven medical devices (which your questions imply) is not applicable here. The "study" for such a device would primarily involve bench testing for electrical safety, mechanical fit, and material compatibility, demonstrating equivalence to predicate devices. The document provided does not detail specific acceptance criteria or performance study results beyond the general assertion of functional equivalence.

Here's what can be gathered, with limitations noted:

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

• Note: The "acceptance criteria" here are inferred to be basic functional and safety requirements for an adapter. The "reported device performance" is essentially a statement of equivalence and proper function, as detailed in the 510(k) summary. Specific quantitative metrics (e.g., resistance values, force to connect/disconnect) are not provided in this summary.

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

• Information Not Provided: The summary does not mention any formal "test set," sample size, or data provenance from a study comparing its performance in a clinical setting. For this type of device, testing would typically involve a small number of physical prototypes undergoing bench tests rather than a large data set.

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)

• Information Not Provided: This question is not applicable to a simple electrosurgical adapter. There is no "ground truth" in the sense of expert interpretation of data. Functional verification would be performed by engineers or technicians against design specifications.

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

• Information Not Provided: Not applicable.

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

• Answer: No. This is not an AI/software device. An MRMC study is completely irrelevant for an electrosurgical grounding pad adapter.

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

• Answer: Not applicable. This is a passive physical adapter, not an algorithm.

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

• Answer: Not applicable. The "ground truth" for this device would be its ability to correctly form an electrical and mechanical connection as per engineering specifications and relevant electrical safety standards. This is validated through bench testing, not clinical "ground truth" data.

8. The sample size for the training set

• Information Not Provided: Not applicable. This device does not involve a "training set" as it's not an AI/machine learning model.

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

• Information Not Provided: Not applicable.

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