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ESG-410:
The electrosurgical generator, in conjunction with electrosurgical accessories and ancillary equipment, is intended for cutting and coagulation of tissue in the following medical fields:

• Open surgery
• Laparoscopic surgery
• Endoscopic surgery
  Only for use by a qualified physician in an adequate medical environment.

PK Cutting Forceps (CF-PK0533):
The PK Cutting Forceps are indicated for electrosurgical coagulation, mechanical cutting, and grasping of tissue during the performance of laparoscopic and open general surgical procedures.

ESG-410 Generator:
The subject device ESG-410 is a reusable, non-sterile electrosurgical generator that features different mono- and bipolar cutting and coagulation modes. The maximum output power is 320 W. The front panel of the proposed ESG-410 features a touch screen GUI (graphical user interface) that displays the connection status of accessories and peripherals connected to the electrosurgical generator. Soft keys are integrated into the GUI to switch between the output sockets, to enter the Menu in order to edit settings/ procedures (e.g. create/ edit user-defined settings/ procedures), to edit preferences (e.g. select language, touch tone control, output volume, or brightness) and to show service options (e.g. software version identifier, for service and maintenance purposes) or to assess user-defined settings and procedures.

PK Cutting Forceps:
The PK Cutting Forceps are a bipolar electrosurgical device that may be utilized in laparoscopic and open general surgery to grasp, coagulate, transect, dissect and retract tissue. The PK Cutting Forceps were cleared via K142759. They are currently intended to be used only with the existing ESG-400 generator per the Indications for Use statement. This submission will demonstrate compatibility with the new ESG-410 generator, and the Indications for Use statement will remove a specific generator model and the compatible generators will be reflected in the labeling. Minor modifications, which did not affect safety and effectiveness, were assessed via internal documentation since the original clearance and will be identified within the submission.

The provided text describes a 510(k) premarket notification for an electrosurgical generator (ESG-410) and accessories, including PK Cutting Forceps. This type of submission focuses on demonstrating substantial equivalence to a legally marketed predicate device, rather than proving novel effectiveness or safety through large-scale clinical trials.

Therefore, the information you're asking for, particularly regarding acceptance criteria tied to device performance in terms of patient outcomes or diagnostic accuracy, and detailed MRMC studies with expert adjudication and effect sizes, is not typically found in a 510(k) submission for a device like an electrosurgical generator.

The "acceptance criteria" for this type of device are primarily related to meeting recognized electrical safety, electromagnetic compatibility (EMC), thermal safety, software validation, and functional performance standards. The "study that proves the device meets the acceptance criteria" refers to a series of bench and non-clinical/preclinical tests designed to show that the new device performs comparably to the predicate device and meets relevant performance standards.

Let's break down what is available in the document related to your questions, and where specific requested information is not applicable or provided.

Acceptance Criteria and Reported Device Performance

The core "acceptance criterion" for a 510(k) submission is substantial equivalence to a predicate device. This is demonstrated by showing similar technological characteristics, intended use, and comparable safety and effectiveness. "Performance" here largely refers to meeting technical specifications and demonstrating similar tissue effects to the predicate, not clinical outcomes in patients.

Table of Acceptance Criteria and Reported Device Performance (as inferred from the document):

Detailed Responses to Specific Questions:

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

   • See table above. The "performance" for this type of device is predominantly technical and functional performance against standards and predicate devices, not clinical effectiveness in terms of patient outcomes.

2. Sample sizes used for the test set and the data provenance:

   • Sample Size: The document does not specify "sample sizes" in the typical clinical study sense for test sets of patients or cases. Instead, it refers to "bench studies" and "preclinical (simulated use) evaluation and testing of tissue effects." For tissue effects, it mentions "three clinically relevant tissue types were evaluated in all applicable modes." This implies material samples or ex-vivo tissue, not a patient cohort.
   • Data Provenance: The tests are described as bench and preclinical (simulated use), conducted by the manufacturer (Olympus Winter & Ibe GmbH) and contract manufacturer (STEUTE TECHNOLOGIES GMBH & CO. KG), both located in Germany. There is no mention of country of origin of patient data as no clinical studies were deemed necessary. The studies were non-clinical/preclinical.

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

   • This question is not applicable to this submission. Ground truth, in the context of diagnostic AI or similar devices, is established by expert review (e.g., radiologists) for specific patient cases. This device is an electrosurgical generator. Its "ground truth" is its ability to adhere to technical specifications, produce specific electrical waveforms, and achieve desired tissue effects in a controlled, non-clinical environment, which is assessed through engineering and physical measurements. No human expert "ground truth" for a test set of medical images/cases is mentioned or relevant here.

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

   • Not applicable. Adjudication methods are used in clinical studies where multiple readers interpret cases and their consensus/disagreement needs to be resolved to establish ground truth or compare diagnostic performance. This is a technical performance and safety submission for an electrosurgical device, not a diagnostic one.

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:

   • No, an MRMC comparative effectiveness study was not done. This type of study is typically performed for AI-powered diagnostic devices to assess how AI assistance impacts human reader performance. The ESG-410 is an electrosurgical generator, a surgical tool, and does not involve human "readers" interpreting images with or without AI assistance. Clinical and animal studies were explicitly stated as "not necessary."

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

   • The ESG-410 contains "software," and "software validation activities were performed." This implies standalone testing of the software's functionality, but not in the context of an "algorithm only" performance for things like diagnostic accuracy (e.g., a standalone AI imaging algorithm). For this device, standalone performance refers to its ability to generate the correct electrical outputs and control the surgical modes as programmed. This was part of the "non-clinical (electrical, dimensional, functional, stability)" testing.

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

   • The "ground truth" for this device's performance is based on engineering specifications, physical measurements of electrical output waveforms, and qualitative/quantitative assessment of tissue effects in simulated environments (preclinical/bench testing). This is not a "ground truth" derived from human experts, pathology, or patient outcomes data, as no clinical studies were performed.

8. The sample size for the training set:

   • Not applicable/Not mentioned. This is not an AI/machine learning device that requires a training set of data for an algorithm to learn from in the typical sense (e.g., imaging data for a diagnostic algorithm). The software validation refers to standard software development life cycle processes, not machine learning model training.

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

   • Not applicable. As explained above, there's no "training set" in the context of machine learning for this device. Its software and functional parameters are designed and validated based on established engineering principles and performance standards for electrosurgical devices, rather than learned from a data set with pre-established ground truth labels.

In summary, the provided FDA 510(k) document details a substantial equivalence claim for an electrosurgical generator and its accessories. The "acceptance criteria" and "proof" provided are consistent with a regulatory pathway for electrical surgical devices, focusing on meeting established technical performance standards and demonstrating comparability to predicate devices through bench and preclinical testing, rather than extensive clinical studies or AI algorithm performance validation metrics typical for imaging diagnostics.

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The Erbe APC 3 with accessories is intended to deliver argon gas for argon plasma coagulation and ablation of tissue as well as argon-assisted cutting of tissue when used in conjunction with a compatible Erbe VIO Electrosurgical Generator (ESU) and applicators or probes.

The Erbe Argon Plasma Coagulation Unit Model APC 3 is an APC unit for use in conjunction with an Erbe Electrosurgical Unit (ESU) compatible Model (currently Model VIO® 3). The APC 3 (as well as VIO 3) can be mounted/secured to a cart or on a ceiling mount using fastening sets, i.e. connecting cables and hardware. The APC 3 is connected to an argon gas source via a pressure reducer and connected to the VIO 3 which powers and controls the APC unit. The ESU further provides a touch-screen monitor (i.e., interactive display) with an onscreen tutorial, settings and operational information as well as safety features (i.e., audio and visual error system). When using the APC 3 together with the VIO 3, the user is offered various APC Modes as well as argon-supported Modes which can be adjusted by the physician via effect levels in order to achieve the desired tissue effect. The ESU VIO 3 provides the high-frequency (HF) current required for thermal coagulation. For argon plasma coagulation and ablation, the HF current is applied non-contact to the tissue to be coagulated through ionized and hence electrically conductive argon gas (argon plasma). The argon gas is ionized in the high-frequency electrical field between the electrode of the applicator and the tissue. For argon-assisted cutting, the ESU provides the regular cutting/coagulation Mode while the argon gas provided by the APC 3 circulates the active electrode. This application is with contact to tissue.

Applicators or probes connected to the APC 3 for coagulating tissue via argon plasma and/or for cutting tissue supported by argon gas may be activated via footswitches connected to the ESU or by use of applicators that have an activation finger switch.

The APC unit and its accessories (i.e. pressure reducer and fastening sets) are supplied non-sterile and are reusable. Cleaning and disinfections are provided in the APC user manual or notes on use of the involved accessory. The compatible applicators and probes are provided sterile as well as are single-use.

This document describes the Erbe Argon Plasma Coagulation Unit APC 3 with Accessories, and its substantial equivalence to a predicate device. It primarily focuses on the device's technological characteristics and safety standards rather than clinical performance or AI-driven improvements in human reading. Therefore, much of the requested information regarding AI acceptance criteria, clinical study specifics (sample size, expert involvement, MRMC studies, ground truth), and training set details for AI cannot be extracted from this document because it is about an electrosurgical device, not an AI/ML-driven diagnostic device.

Here's a breakdown of what can be extracted, and where limitations exist based on the provided text:

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

The document doesn't provide specific quantitative acceptance criteria or detailed performance metrics in a table format for clinical efficacy. Instead, it focuses on general safety, performance, and substantial equivalence to a predicate device based on technical specifications and adherence to recognized standards.

Acceptance Criteria (Implied from the document):

• Safety: Adherence to electrical safety standards (e.g., IEC 60601-1, IEC 60601-1-2, IEC 60601-2-2).
• Performance: Ability to deliver argon gas for argon plasma coagulation, ablation, and argon-assisted cutting as intended. This is assessed through "Output/Mode Comparison," "Tissue Testing," and "Capacitive Coupling" as listed in the "Performance Evaluations/Testing" section.
• Usability: Conformance to usability engineering standards (e.g., IEC 62366, IEC 60601-1-6).
• Compatibility: Operation with compatible Erbe VIO Electrosurgical Generators (ESU) and specific applicators/probes.
• Substantial Equivalence: Demonstrating that the new device has "essentially the same principles of operation, technological characteristics, as well as performance characteristics as the predicate APC unit."

Reported Device Performance:
The document states that "Validation and verification activities in design control that includes testing/certification to designated standards (including electrical safety standards) and performance testing of the proposed devices has demonstrated substantial equivalence to the predicate." However, specific numerical performance data (e.g., coagulation efficacy rates, ablation depth measurements, cutting speeds) are not provided. The "Output/Mode Comparison" and "Tissue Testing" are mentioned as performance evaluations, but their results are summarized qualitatively as proving substantial equivalence.

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

This document describes a medical device (an electrosurgical unit), not an AI/ML diagnostic algorithm tested on a dataset of patient data. Therefore, the concepts of "test set sample size" and "data provenance" in the context of clinical/imaging data are not applicable here. The testing involves engineering and performance validation of the device itself, rather than testing on a retrospective or prospective clinical dataset.

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

Not applicable. This is not an AI/ML diagnostic device requiring expert annotation of medical images or data to establish ground truth for a test set. The validation is technical and engineering-focused.

4. Adjudication method for the test set

Not applicable. As this is not a diagnostic device involving human interpretation of data, there is no need for adjudication of readings.

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 not an AI-assisted diagnostic tool, so an MRMC study and
assessment of human reader improvement with AI assistance is irrelevant.

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

Not applicable. This is not an algorithm. It is a hardware medical device.

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

The "ground truth" for this device's performance evaluation is based on engineering standards, physical measurements, and comparison to the predicate device's established performance characteristics. It relies on:

• Compliance with recognized consensus standards: e.g., electrical safety, electromagnetic compatibility, usability (IEC, ISO standards listed).
• Physical (in-vitro/bench) testing: "Output/Mode Comparison," "Tissue Testing," "Capacitive Coupling." These tests would compare the device's output and tissue effects to specifications and the predicate.
• Functional verification: Ensuring the device operates as intended when connected to compatible components.

It does not involve "expert consensus," "pathology," or "outcomes data" in the sense of clinical diagnostic accuracy.

8. The sample size for the training set

Not applicable. This is not an AI/ML system that requires a "training set" of data.

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

Not applicable. See point 8.

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The EnSeal™ PTC is a bipolar electrosurgical instrument for use with an electrosurgical generator. It is intended for use during open and laparoscopic, general and gynerologic surgery to cut and seal vessels, cut, grasp and dissect tissue during surgery.

Indications for use include open and laparoscopic general and gynecological surgical procedures (including urologic, thoracic, plastic and reconstructive, bowel resetting, hysterectomies, cholecystectomies, gall bladder procedures, Nissen fundoplication, adhesiolysis, oophorectomies, etc.), or any procedure where vessel ligation (cutting and sealing), tissue grasping and dissection is performed. The devices can be used on vessels up to (and including) 7 mm and bundles as large as will fit in the jaws of the instruments.

The SurgRx EnSeal Vessel Sealing & Hemostasis System has not been shown to be effective for tubal sterilization or tubal coagulation for sterilization procedures. Do not use this system for these procedures.

EnSeal™ PTC with ERBE VIO 300 D. The functionality of the device is the same as the predicate device.

The provided text is a 510(k) summary for the EnSeal™ PTC with ERBE VIO 300 D, an electrosurgical device. This document focuses on demonstrating substantial equivalence to predicate devices rather than conducting a de novo study with specific acceptance criteria as one might see for novel AI/software devices. Therefore, much of the requested information regarding acceptance criteria, specific performance metrics, sample sizes, expert involvement, and ground truth establishment, as typically outlined for AI/ML device studies, is not present in this type of submission.

Here's an analysis based on the provided text, highlighting what is and is not available:

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

Explanation: In a 510(k) for an electrosurgical device like this, acceptance criteria would typically revolve around electrical safety, mechanical integrity, and functional performance (e.g., sealing strength, cut time, minimal thermal spread) compared to the predicate. However, these specific criteria and quantitative results are not detailed in this summary. The summary simply states that the tests "ensure the devices function as intended and meet design specifications."

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: Not specified. The document only mentions "Preclinical laboratory (bench) and performance tests."
• Data Provenance: Not specified. The tests were "preclinical laboratory (bench)" tests, suggesting an in-house or contracted lab environment, but geographical origin is not mentioned. They would be considered prospective for the specific tests conducted.

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)

• Number of Experts: Not applicable/Not specified. This type of device performance testing does not typically involve human experts establishing "ground truth" in the way a diagnostic imaging AI algorithm would. Device performance is measured by objective metrics (e.g., burst pressure, temperature).
• Qualifications of Experts: Not applicable/Not specified.

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

• Adjudication Method: Not applicable/Not specified. Adjudication methods like 2+1 or 3+1 are typically used for establishing consensus ground truth in studies involving human interpretation (e.g., reviewing medical images). This is a physical device, and its performance is measured mechanically and electrically, not through human consensus on a diagnosis.

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

• MRMC Study: No. This is not an AI/ML diagnostic device, so an MRMC study comparing human readers with and without AI assistance is not relevant and was not conducted.

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

• Standalone Study: The device itself is "standalone" in its electrosurgical function, but the context of "standalone performance" typically refers to an algorithm's performance without human intervention. This device performs its function directly. Therefore, this question is not fully applicable in the context of an AI/ML algorithm. The "Preclinical laboratory (bench) and performance tests" assess the device's inherent function, which could be considered its standalone performance.

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

• Type of Ground Truth: For electrosurgical devices, "ground truth" is established through engineering and scientific principles. Examples might include:
  • Physical measurements: Tensile strength of sealed vessels, burst pressure, impedance, power output.
  • Histopathology: To assess thermal damage zones in sealed tissue (though not mentioned in the summary).
  • Objective functional assessments: Such as sealing time, cutting efficacy, and lack of electrical leakage.
    The summary does not detail the specific ground truth metrics but states tests were performed to ensure it "function[s] as intended and meet[s] design specifications."

8. The sample size for the training set

• Training Set Sample Size: Not applicable. This is not an AI/ML device, so there is no "training set" in the machine learning sense.

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

• Ground Truth Establishment for Training Set: Not applicable. As there is no training set for an AI/ML model, this question does not apply.

Study Description:

The study proving the device meets its "acceptance criteria" (which are generally implied to be equivalent performance to the predicate) is described as "Preclinical laboratory (bench) and performance tests." The conclusion drawn from these tests is that the device "function[s] as intended and meet[s] design specifications" and is "safe and effective and substantially equivalent to the predicate device." The study's focus was on demonstrating substantial equivalence to the previously cleared devices (EnSeal™ Vessel Sealing and Hemostasis System # K031133, and the ERBE VIO ESU, Model VIO 300 D, # K060484 and EnSeal PTC # K061526). This type of submission relies on comparative analysis of technological characteristics and performance data, rather than establishing de novo safety and effectiveness through a comprehensive multi-site clinical trial with specific performance endpoints often seen for novel therapeutic or diagnostic devices.

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