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The CONMED HelixAR ABC System is intended to deliver argon gas as well as high frequency electrical current for the cutting and/or coagulation of tissue.

The HelixAR ABC System is composed of an electrosurgical generator with argon beam coagulation (ABC) and a mobile pedestal. The generator is designed with a graphic user interface (GUI) display which allows the user to select monopolar, bipolar, and ABC modes of operation, choose power and argon flow settings, adjust user settings options, and create, edit, or delete user programs. The HelixAR mobile pedestal (cart) houses two D-size argon gas cylinders and provides connection ports for the use of wired footswitches. The mobile pedestal is designed with an argon tank capacity monitor that provides the user with feedback on the argon gas cylinder pressure.

This document is a 510(k) premarket notification from the FDA, specifically concerning the CONMED HelixAR ABC System, an electrosurgical device for cutting and coagulating tissue. As an electrosurgical device, it's not an AI/ML-driven diagnostic tool, which is typically where the detailed acceptance criteria and study designs (like MRMC studies) you've requested are relevant.

Therefore, many of the specific questions you've asked regarding AI/ML models (e.g., sample size for test/training sets, ground truth establishment, expert adjudication, MRMC studies) are not applicable to this type of medical device submission.

However, I can extract information related to the device's performance and the general "acceptance criteria" presented in the context of device safety and effectiveness.

Here's a breakdown based on the provided document:

Device: CONMED HelixAR ABC System (Electrosurgical Cutting and Coagulation Device and Accessories)
Type of Device: Electrosurgical Generator with Argon Beam Coagulation

1. Acceptance Criteria and Reported Device Performance (as interpreted for an electrosurgical device):

For an electrosurgical device, "acceptance criteria" revolve around demonstrating that the device is safe and effective for its intended use, and performs comparably to a predicate device. This is primarily established through compliance with recognized standards and bench testing.

Study Proving Device Meets Acceptance Criteria:

The document describes the studies and activities undertaken to demonstrate substantial equivalence and meet the acceptance criteria. These are primarily benchtop testing, software verification and validation, and risk management activities.

• 2. Sample size used for the test set and the data provenance: This is not an AI/ML device dealing with data sets of patient images/records. "Test sets" in this context refer to specific tests performed on the physical device and its software. The document does not specify the "sample size" in terms of number of patients or cases, as it's not a clinical study in that sense. The provenance would be internal laboratory testing by the manufacturer (ConMed Corporation). It's benchtop and software testing, not patient data.

• 3. Number of experts used to establish the ground truth for the test set and the qualifications of those experts: Not applicable. Ground truth for an electrosurgical device's performance is established through engineering and safety standards compliance, and verified through direct measurements or functional testing, not expert interpretation of patient data.

• 4. Adjudication method (e.g. 2+1, 3+1, none) for the test set: Not applicable. This refers to clinical expert review for diagnostic AI, not electrosurgical device testing.

• 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 is a question for AI/ML diagnostic devices, not an electrosurgical device.

• 6. If a standalone (i.e. algorithm only without human-in-the-loop performance) was done: Not applicable. This device is an electrosurgical tool, not an algorithm that performs a task autonomously. It is designed for human-in-the-loop operation during surgery.

• 7. The type of ground truth used (expert consensus, pathology, outcomes data, etc.): Ground truth for this device's performance is based on established engineering and safety standards (e.g., IEC 60601-1, IEC 60601-2-2, IEC 60601-1-2) and the device's design specifications. Its function (cutting/coagulation) is directly observable and measurable through bench testing, not derived from complex clinical outcomes or expert consensus on interpretations.

• 8. The sample size for the training set: Not applicable. This device does not use machine learning, so there is no "training set."

• 9. How the ground truth for the training set was established: Not applicable, as there is no training set for a machine learning model. For the device itself, "ground truth" (i.e., correct operation and safety parameters) is established by adherence to recognized national and international electrical safety and performance standards for electrosurgical equipment.

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The CONMED HelixAR ABC System is intended to deliver argon gas as well as high frequency electrical current for the cutting and/ or coagulation of tissue.

The HelixAR ABC System is composed of an electrosurgical generator with argon beam coagulation (ABC) and a mobile pedestal. The generator is designed with a graphic user interface (GUI) display which allows the user to select monopolar, bipolar, and ABC modes of operation, choose power and argon flow settings, adjust user settings options, and create, edit, or delete user programs. The HelixAR mobile pedestal (cart) houses two D-size argon gas cylinders and provides connection ports for the use of wired footswitches. The mobile pedestal is designed with an argon tank capacity monitor that provides the user with feedback on the argon gas cylinder pressure.

This document describes the CONMED HelixAR ABC System, an electrosurgical generator with argon beam coagulation (ABC). The information provided is primarily for a 510(k) premarket notification, focusing on demonstrating substantial equivalence to predicate devices, rather than a detailed clinical study demonstrating specific performance metrics against pre-defined acceptance criteria for a new clinical claim. Therefore, much of the requested information regarding detailed acceptance criteria and a study proving those specific criteria cannot be fully extracted.

However, based on the provided text, here's an attempt to answer the questions within the context of substantial equivalence:

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

The document does not present explicit, quantifiable acceptance criteria with corresponding device performance values for specific clinical outcomes (e.g., coagulation time, depth of penetration, adverse event rates). Instead, the "acceptance" is focused on demonstrating that the device is substantially equivalent to predicate devices. The performance testing section mentions compliance with several standards.

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

The document mentions "Benchtop and ex-vivo comparison testing as well as in-vivo simulated use testing." However, it does not specify sample sizes for these tests (e.g., number of ex-vivo tissue samples, number of in-vivo simulated use observations) nor does it provide details on the data provenance (country of origin, retrospective/prospective). The focus is on the equivalence rather than a clinical trial with a defined test set sample size.

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 information is not provided in the document. Given that the testing appears to be primarily benchtop, ex-vivo, and simulated in-vivo, it's unlikely that external medical experts were used to establish a "ground truth" in the way they would be for image-based diagnostic devices or clinical outcomes. The "ground truth" for electrosurgical performance would typically be based on quantifiable physical measurements and established scientific principles rather than expert consensus on subjective observations.

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

This information is not applicable/provided. Adjudication methods like 2+1 or 3+1 are typically used in clinical trials, especially for endpoints requiring subjective assessment, to resolve discrepancies among multiple reviewers. Since no such clinical test set or expert review process is described, an adjudication method is not mentioned.

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 information is not applicable/provided. The HelixAR ABC System is an electrosurgical device, not a diagnostic imaging device with an AI component for interpretation. Therefore, an MRMC study or assessment of human reader improvement with AI assistance is irrelevant to this device.

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

This information is not applicable/provided. The HelixAR ABC System is a physical electrosurgical device that requires human operation. It does not have a standalone algorithm for performance assessment in the way AI/ML software devices do.

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

For the performance testing mentioned (benchtop, ex-vivo, in-vivo simulated use), the "ground truth" would likely be based on:

• Physical measurements: For parameters like power output, argon flow rates, temperature, current, etc.
• Observed tissue effects: For ex-vivo and in-vivo tests, comparison to known and accepted electrosurgical tissue effects validated by established scientific methods.
• Compliance with standards: Verification against the requirements of AAMI/ANSI ES60601-1, IEC 60601-2-2, and IEC 60601-1-2.

The document does not explicitly state the type of ground truth used, but it would not be expert consensus, pathology, or outcomes data in the context of a clinical trial.

8. The sample size for the training set

This information is not applicable/provided. The HelixAR ABC System is a hardware medical device; it is not an AI/ML device that requires a training set in the computational sense. The "training" for such a device would involve engineering design, prototyping, and iterative testing, not a dataset for algorithm training.

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

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

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