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The Electrosurgical Generator is used to deliver RF energy via an assortment of surgical devices to cut and coagulate different kinds of tissue.

The ES-100 and ES-300 Electrosurgical Generator are advanced high-frequency surgical devices designed to provide versatility and safety in various surgical procedures. Both models offer a range of operating modes, including monopolar electrosurgical excision modes, monopolar electrocoagulation modes, and bipolar modes, catering to the diverse needs of surgeons. The maximum output power of the ES-100 is 100 W, while the ES-300 offers increased output power of up to 300 W, providing surgeons with enhanced capabilities for cutting and coagulation in various surgical procedures. Both models are equipped with manual and foot switch controls, allowing for seamless operation during surgeries. They feature volume control for adjusting the device's audio output, memory functions for storing recent settings, and built-in return electrode monitor systems for real-time safety monitoring.

This document is a 510(k) summary for an Electrosurgical Generator, not a study evaluating an AI/ML device. Therefore, the requested information regarding acceptance criteria, study design, and performance metrics for an AI/ML powered device cannot be extracted from this document.

The document describes the regulatory submission for electrosurgical generators (ES-100 and ES-300 models) and compares them to a predicate device (Bovie IDS-310 High Frequency Electrosurgical Generator). The core of the submission is to demonstrate "substantial equivalence" to the predicate device, not to prove performance against specific acceptance criteria for an AI/ML algorithm.

Here's how each of your requested points relates to the provided document:

1. A table of acceptance criteria and the reported device performance: This document does not specify "acceptance criteria" in the context of an AI/ML study. Instead, it presents a comparison table of technical characteristics between the subject device and the predicate device (pages 7-8). The "performance" mentioned refers to electrical safety, electromagnetic compatibility, output performance, and thermal damage characteristics of the electrosurgical unit itself, not an AI/ML algorithm's diagnostic performance.

2. Sample sized used for the test set and the data provenance (e.g. country of origin of the data, retrospective or prospective): Not applicable. This document is about hardware medical devices and their electrical/thermal performance, not an AI/ML algorithm that processes data.

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): Not applicable. "Ground truth" in the context of expert consensus for AI/ML validation is not a component of this submission.

4. Adjudication method (e.g. 2+1, 3+1, none) for the test set: 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: Not applicable.

6. If a standalone (i.e. algorithm only without human-in-the-loop performance) was done: Not applicable.

7. The type of ground truth used (expert concensus, pathology, outcomes data, etc): Not applicable. The "ground truth" for electrosurgical generators would be physical measurements of electrical output and thermal effects, verified through accredited testing, not expert consensus on medical images or patient outcomes data.

8. The sample size for the training set: Not applicable. This is for an electrosurgical hardware device, not an AI/ML model.

9. How the ground truth for the training set was established: Not applicable.

In summary, the provided document is a 510(k) premarket notification for electrosurgical generators, demonstrating substantial equivalence to a predicate device through non-clinical testing (electrical safety, EMC, bench testing for output performance, and preclinical thermal damage assessment). It does not contain any information related to AI/ML device validation.

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The Apyx Helium Plasma Generators (owned by Apyx Medical) are indicated for delivery of radiofrequency energy and/ or helium plasma to cut, coagulate and ablate soft tissue during open and laparoscopic surgical procedures. The helium plasma portion of the generator can be used only with dedicated Renuvion/ J-Plasma handpieces.

The Apyx Helium Plasma Generator is an electrosurqical device that delivers radiofrequency (RF) energy to cut and coaqulate soft tissue. It can also deliver Helium plasma energy for cutting, coagulation, and ablation of soft tissue. Like the predicate, the Apyx Helium Plasma Generator provides standard electrosurgical energy and Helium Plasma energy, and there are no changes to the intended use, performance specifications, electrosurgical modes, output power waveforms or maximum power settings. Essentially, the Apyx Helium Plasma Generator is a combination of a standard electrosurgical generator and a Helium Plasma generator.

Here's a breakdown of the acceptance criteria and the study that proves the device meets them, based on the provided text:

1. Table of Acceptance Criteria and Reported Device Performance

• ANSI/AAMI/IEC ES60601-1:2005/(R)2012 and A1:2012 (Medical Electrical Equipment - Part 1: General requirements for basic safety and essential performance)
• AAMI/ANSI/IEC 60601-1-2:2014 (4th Edition) (Medical electrical equipment - Part 1-2: General requirements for basic safety and essential performance - Collateral Standard: Electromagnetic disturbances - Requirements and tests)
• AAMI/ANSI/IEC-60601-2-2:2017 (Medical electrical equipment - Part 2-2: Particular requirements of basic safety and essential performance of high-frequency surgical equipment) |
  | Electromagnetic Compatibility (EMC) | Complies with:
• AAMI/ANSI/IEC 60601-1-2:2014 (4th Edition) (Medical electrical equipment - Part 1-2: General requirements for basic safety and essential performance - Collateral Standard: Electromagnetic disturbances - Requirements and tests)
• IEC60601-1-2 (electromagnetic compatibility) |
  | Electrical Performance | The electrical functionality of the generator was verified to meet performance specification requirements. |
  | Software Functionality | The system and Field Programmable Gate Array (FPGA) perform as intended and according to the product specifications. |
  | Mechanical Design | The mechanical design meets the product and performance requirements. |

Summary of the Study:

The provided document describes a 510(k) premarket notification for the Apyx Helium Plasma Generator. This notification seeks to demonstrate substantial equivalence to a previously cleared predicate device (K170188, Bovie Ultimate® Gen 2 Electrosurgical Generator). The study involved bench and software verification and validation testing to confirm the modified device's safety and effectiveness.

2. Sample Size Used for the Test Set and Data Provenance

The document does not specify a numerical sample size for the test set. The tests describe "Electrical Verification," "Software Verification & Validation," and "Mechanical Verification," which are typically performed on a limited number of manufactured units or system components, rather than a large clinical "test set" in the sense of patient data.

The data provenance is retrospective, as the study primarily compares the modified device to a previously cleared predicate and leverages existing regulatory standards. The testing itself would have been conducted in a controlled laboratory or engineering environment, rather than using patient data from a specific country of origin.

3. Number of Experts Used to Establish the Ground Truth for the Test Set and Qualifications of Those Experts

This information is not provided in the document. The type of testing described (electrical, software, mechanical verification) typically relies on engineering and regulatory standards rather than expert clinical consensus to establish "ground truth" for the device's functional and safety characteristics.

4. Adjudication Method for the Test Set

This information is not applicable/not provided for the type of testing described. Adjudication methods like 2+1 or 3+1 are typically used in clinical studies where expert consensus is needed to establish ground truth for diagnostic decisions. Here, the "truth" is determined by adherence to established engineering and safety standards.

5. Multi-Reader Multi-Case (MRMC) Comparative Effectiveness Study

No, an MRMC comparative effectiveness study was not done. The study described is not a clinical trial evaluating human reader performance with or without AI assistance. It's a regulatory submission demonstrating the substantial equivalence of an electrosurgical device through engineering and software verification.

6. Standalone Performance (Algorithm Only Without Human-in-the-Loop Performance)

This concept is not directly applicable to this device. The Apyx Helium Plasma Generator is a physical medical device that delivers energy for surgical procedures. It does not operate as an "algorithm only" or an AI system that provides diagnostic output without human intervention in the loop. Its performance is evaluated based on its technical specifications and safety conformity, regardless of human interaction methods.

7. Type of Ground Truth Used

The "ground truth" used in this submission is implicitly based on established international and national standards for medical electrical equipment (e.g., IEC 60601 series). Compliance with these standards, along with the device's documented performance against its own product specifications, forms the basis of its "truth" for safety and effectiveness in the context of substantial equivalence. There is no mention of pathology, expert consensus (in a clinical sense), or outcomes data being used to establish ground truth for the device itself in this specific regulatory context.

8. Sample Size for the Training Set

This information is not applicable/not provided. The device in question is an electrosurgical generator, which is a hardware device with embedded software. It is not an artificial intelligence (AI) or machine learning (ML) algorithm that requires a "training set" of data in the conventional sense. The software verification and validation are against predefined specifications, not learned from a dataset.

9. How the Ground Truth for the Training Set Was Established

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

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