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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 electrosurgical generator, in conjunction with compatible devices and electrosurgical accessories, is intended for cutting and coagulation of soft tissue and for ligation of vessels. The electrosurgical generator utilizes monopolar and bipolar high frequency current and supports ultrasonic instruments.

The electrosurgical generator is intended to be used in the following medical fields:

• · Open surgery
• · Laparoscopic surgery, including single-site surgery
• · Endoscopic surgery

Only for use by a qualified physician in an adequate medical environment.

The subject device ESG-410 is a reusable, non-sterile electrosurgical generator that features different high frequency monopolar and bipolar cutting and coagulation modes with a maximum output power of 320 W, as well as capability to power the existing Olympus ultrasonic THUNDERBEAT and SONICBEAT devices via a redesigned HYBRID ULTRASONIC socket and four new modes using high frequency (RF bipolar output) energy and supporting ultrasonic energy. The maximum RF output power for the THUNDERBEAT mode is 110 W.

The electrosurgical generator, in conjunction with compatible devices and electrosurgical accessories and ancillary equipment, is intended for cutting and coagulation of soft tissue in open surgery, laparoscopic surgery (including single-site surgery), endoscopic surgery and for ligation of vessels. The electrosurgical generator utilizes monopolar and bipolar high frequency current and supports ultrasonic instruments.

The front panel of the proposed ESG-410 features a touch screen GUI (graphical user interface) as well as the power switch (on/off), six output sockets and one neutral electrode socket.

The touch screen GUI displays the current settings of the chosen output mode, 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 access user-defined settings and procedures.

Here's a breakdown of the acceptance criteria and the study details for the ESG-410 (Models: WA91327U, WA91327W), based on the provided document:

This device is an electrosurgical generator, and the information provided is a 510(k) summary, which focuses on demonstrating substantial equivalence to a predicate device rather than presenting a novel AI algorithm's performance against specific clinical endpoints. Therefore, many of the typical acceptance criteria and study details for AI/ML devices aiming to improve diagnostic accuracy are not applicable here. This submission relies heavily on demonstrating equivalent technical characteristics and safety performance to existing, cleared devices.

1. Table of Acceptance Criteria and Reported Device Performance

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

• Sample Size: The document does not specify a distinct "test set" sample size in terms of number of cases or patients, as this is primarily a device safety and performance equivalence submission, not a diagnostic accuracy study.
  • For Vessel Burst Pressure and Thermal Spread, studies were conducted on "vessels" which implies a quantity of biological samples (e.g., animal tissue, ex vivo human tissue) but the exact number is not provided. The comparison was to "control groups."
  • For Performance Bench Testing (tissue effects, electrical waveforms, functional performance), the testing involved various "modes, instruments and test protocols/plans." The nature of these tests is laboratory bench testing using simulation and comparison.
• Data Provenance: The studies were non-clinical bench testing and preclinical (simulated use) evaluation.
  • No information on country of origin of data or whether it was retrospective or prospective is relevant or provided, as these are not clinical studies on human subjects.

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

• This information is not applicable to this 510(k) submission. The ground truth for functional equivalence, tissue effects, thermal safety, etc., was established through objective engineering measurements, comparisons to predicate device measurements, and compliance with recognized standards, rather than expert consensus on clinical cases.

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

• This information is not applicable. Adjudication methods like 2+1 are used in clinical diagnostic studies where expert reviewers resolve discrepancies in ground truth labeling. This submission relies on objective physical measurements and engineering evaluations.

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 MRMC study was done. This is not an AI-assisted diagnostic device; it is an electrosurgical generator used for cutting and coagulation. Therefore, the concept of human readers improving with AI assistance is not relevant.

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

• This is not an algorithm-only device. The device is hardware (an electrosurgical generator) with integrated software. Its performance is always "standalone" in the sense that the generator produces desired electrical outputs or ultrasonic vibrations based on its internal programming and user settings. The human operator is "in-the-loop" by controlling the device during a surgical procedure. The software validation tests mentioned (Section 2.8.5) assess the software's performance as part of the overall device.

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

• The "ground truth" for the performance claims in this submission is primarily based on:
  • Objective engineering measurements: Verifying electrical waveform outputs, power levels, and adherence to specified performance parameters.
  • Direct comparisons to predicate devices: Establishing that the new device's performance (e.g., tissue effects, thermal spread, burst pressure) is either identical, equivalent, or statistically non-inferior/superior to the legally marketed predicate devices.
  • Compliance with recognized international standards: Demonstrating that the device meets established safety and performance benchmarks (e.g., AAMI/ANSI/IEC 60601 series for electrical safety, ISO 14971 for risk management).

8. The sample size for the training set

• This information is not applicable. This is not an AI/ML device that requires a training set in the typical sense for learning models. The software development and validation followed standard engineering practices, not machine learning model training.

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

• This information is not applicable, as there is no training set for an AI/ML model.

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The ESG-400 is an electrosurgical generator intended for tissue cutting and coagulation in open, laparoscopic and endoscopic surgery in conjunction with electrosurgical accessories and ancillary equipment.

The POWERSEAL Sealer and Divider is a bipolar electrosurgical device intended for use in laparoscopic/minimally invasive or open surgical procedures where ligation and division of vessels, tissue bundles, and lymphatics is desired. POWERSEAL devices can be used on vessels (arteries and veins, pulmonary arteries, pulmonary veins) up to and including 7 mm, lymphatics, and tissue bundles. POWERSEAL devices are indicated for use in general surgery and such surgical specialties as urologic, colorectal, bariatric, vascular, thoracic, and gynecologic. Procedures may include, but are not limited to, Nissen fundoplication, colectomy, cholecystectomy, adhesiolysis, sleeve gastrectomy, hysterectomy, oophorectomy.

The POWERSEAL Sealer and Divider has not been shown to be effective for tubal sterilization or tubal coagulation for sterilization procedures. Do not use the POWERSEAL devices for these procedures.

The subject device ESG-400 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 features a touch screen GUI that displays current settings, connection status, and allows access to menus for editing settings, procedures, and preferences. Compatible accessories include previously cleared footswitches.

The POWERSEAL 5mm laparoscopic curved jaw sealer divider is an electrosurgical bipolar device with an integral extending cutting blade. It features a pistol grip handle and will be provided in shaft lengths of 23, 37, and 44 cm. The POWERSEAL devices are sterile, single-use, hand-held bipolar electrosurgical instruments designed for use with Olympus electrosurgical generators to ligate (seal) and divide (cut) vessels, tissue bundles, and lymphatics. The jaws are designed to seal vessels, and grasp and dissect tissue during open and minimally invasive general surgical procedures using high frequency (HF) energy. A hand actuated mechanism opens and closes the jaws. A second control initiates bipolar energy delivery for sealing. A separate control activates a blade for tissue division.

The provided text details the 510(k) submission for the Olympus Electrosurgical Generator ESG-400 and Accessories, and the POWERSEAL Curved Jaw Sealer and Divider, Double Action. The study described focuses on demonstrating substantial equivalence to predicate devices, rather than establishing specific acceptance criteria and proving direct performance against them in a traditional sense. The performance data provided is primarily in the context of comparative testing to established predicate devices.

Here's an attempt to extract the information requested, with indications where the information is not explicitly available in the provided document:

1. Table of acceptance criteria and the reported device performance

The document doesn't explicitly state specific numerical acceptance criteria for performance metrics (e.g., burst pressure in mmHG, or a specific range for coagulation time). Instead, it states that the device "met all acceptance criteria" for biocompatibility and that "performance requirements defined in the User Requirements Specification and Design Specification were met for both subject devices, and that they exhibit comparable performance characteristics to the predicate device and reference devices."

The performance is described qualitatively as being "comparable" or having "equivalent technology and performance" to predicate/reference devices. For example, for vessel sealing, the key performance indicator mentioned is "vessel burst pressure testing."

Therefore, a table of acceptance criteria and reported numerical performance values cannot be fully constructed from the provided text in the typical quantitative manner.

Table 1: Acceptance Criteria and Reported Device Performance (as inferred and stated qualitatively)

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

• Test Set Sample Size: The document does not specify exact sample sizes for the "test set" in terms of number of cases or samples for the animal studies or bench testing (e.g., how many vessels were sealed for burst pressure testing). It only states that "ex-vivo Vessel Burst Pressure testing was conducted."
• Data Provenance (Country of Origin, Retrospective/Prospective): Not explicitly stated. The studies were likely conducted in a controlled lab or animal facility. The document refers to FDA guidance documents, which are for U.S. regulatory submissions, but doesn't specify where the actual testing took place. It also doesn't specify if the animal studies or bench tests were retrospective or prospective, though performance testing is generally prospective.

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

This type of information (number of experts, their qualifications, and their role in establishing ground truth) is typically related to clinical studies involving human interpretation or pathology. The reported studies are primarily bench (ex-vivo) and animal studies. Therefore, this information is not applicable in the context of the provided document. The "ground truth" for these studies would be objective measurements (e.g., burst pressure from instrumentation) rather than expert consensus on clinical findings.

4. Adjudication method for the test set

This is also typically relevant for clinical studies with human interpretation. For bench and animal studies (e.g., measuring vessel burst pressure), the "adjudication" is typically through objective measurements and statistical analysis, not a consensus process among experts as described by methods like 2+1 or 3+1. Therefore, this information is not applicable in the context of the provided document.

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 Done: No. This study is for an electrosurgical device, not an AI-assisted diagnostic tool involving human readers.
• Effect Size of Human Readers: Not applicable.

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

• Standalone Performance: Not applicable to this type of device. The device itself is an electrosurgical tool, not an algorithm, and it is used by a human surgeon.

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

For the performance testing, the ground truth was established through objective physical measurements in bench and animal studies:

• Biocompatibility: In vitro and in vivo (animal tissue/systems) tests following ISO 10993 standards.
• Electrical/Thermal Safety & EMC: Measurements against IEC standards.
• Vessel Sealing Performance: Chronic and acute animal studies demonstrating seal performance, and ex-vivo vessel burst pressure testing. The "ground truth" here would be the measured burst pressure values and observed tissue effects.

8. The sample size for the training set

The document describes an electrosurgical device, not a machine learning or AI algorithm in the context of diagnostic imaging. Therefore, the concept of a "training set" in this context is not applicable. The "training" for such a device involves engineering design, prototyping, and testing against specifications and regulatory standards.

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

Not applicable, as there is no "training set" in the context of an AI/ML algorithm being developed.

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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 Electrosurgical Generator is an electrosurgical generator containing monopolar and bipolar technology. It is intended for use with accessories during surgical procedures where the surgeon requires electrosurgical cutting and coagulating.

The application device, the Electrosurgical Generator, is a solid state generator designed to supply radiofrequency electrical energy for general electrosurgical purposes, with physical dimensions around 37cm (L) x 29cm (W) x 17cm (H) and 5kg unit weight. The Electrosurgical Generator supplies high frequency electrosurgical power from low power (<30W), medium power (Cut: 30100W, Coag: 3070W), to high power (Cut >100W, Coag>70W). The Electrosurgical Generator outputs high frequency energy in the following modes: 1. Monopolar CUT: Pure Blend 1 Blend 2 2. Monopolar COAG: Pin Point Spray 3. Bipolar: Standard The Electrosurgical Generator can be activated via a hand switch or a foot switch. The Electrosurgical Generator has a Return Electrode Contact Quality Monitor to alert the user when inadequate contact is being made with the Return (aka Neutral) Electrode. The Electrosurgical Generator is mains powered.

This document is a 510(k) premarket notification for an Electrosurgical Generator (Model: ES300) and describes its substantial equivalence to a predicate device, the Covidien Force FX™ Electrosurgical Generator (K143161). The focus of the provided text is on demonstrating the device's adherence to relevant standards and its similarity in function and design to the predicate, rather than providing details of a study with specific acceptance criteria and performance metrics in the format requested.

Therefore, the requested information (acceptance criteria, device performance, sample sizes, ground truth establishment, expert involvement, and MRMC study details) is not explicitly available in the provided text in the detailed format you requested for an AI/device performance study. The document primarily focuses on regulatory compliance through comparison to a predicate device and adherence to established electrical safety and performance standards.

Here's an attempt to extract and infer information based on the provided text, while also noting what is not present:

1. Table of Acceptance Criteria and Reported Device Performance

The document does not present acceptance criteria and reported device performance in a numerical table for clinical or diagnostic accuracy as would be found in a study for an AI-powered diagnostic device. Instead, "acceptance criteria" are implied by adherence to and compliance with recognized standards and guidelines. "Device performance" is largely described qualitatively and by comparison to the predicate device's established performance.

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

This information is not provided in the document. The testing described is primarily related to compliance with electrical, safety, and performance standards for the device itself, rather than a clinical study with a test set of patient data. The provenance of data from such compliance testing would typically be from laboratory settings or engineering evaluations, not patient data.

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

This information is not provided. The document describes regulatory compliance and engineering testing, not a study involving expert-established ground truth for a test set of cases.

4. Adjudication Method for the Test Set

This information is not provided. As there's no mention of a clinical test set with ground truth, an adjudication method is not applicable in this context.

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 provided. The device is an electrosurgical generator, not an AI-assisted diagnostic tool for human readers. Therefore, an MRMC study comparing human reader performance with and without AI assistance is not relevant to this device and was not conducted.

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

This information is not applicable/not provided. The device is a physical electrosurgical generator, not an algorithm. Performance tests mentioned are for the physical device's electrical outputs and safety features, not standalone algorithmic performance.

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

For the type of device described (an electrosurgical generator), "ground truth" would typically refer to objective measurements and adherence to specified performance parameters defined by engineering standards (e.g., output power within tolerance, impedance monitoring accuracy, alarm functionality). The document indicates that the device was "tested and found to be in compliance" with various IEC standards and FDA guidance, which serve as the "ground truth" or benchmarks for electrical safety and performance.

8. The sample size for the training set

This information is not applicable/not provided. As this is not an AI/machine learning device, there is no "training set" in the computational sense.

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

This information is not applicable/not provided. There is no training set for this type of device.

In summary: The provided FDA 510(k) summary document focuses on demonstrating substantial equivalence of an electrosurgical generator to a predicate device through adherence to recognized technical standards and comparison of features. It does not contain information about a clinical or diagnostic study with acceptance criteria, sample sizes, expert ground truth, or AI-related performance metrics as requested for an AI/CAD-type device.

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The Electrosurgical Generator 8070 is indicated for monopolar or bipolar surgery to achieve cut or coagulation for the tissue. It is intended to be used with monopolar handpiece and dispersive electrode or bipolar handpiece and footswitches.

8070 is an Electrosurgical Generator with Monopolar Pure cut, Monopolar Bland Cut, Bipolar function. 8070 consists of HF generator and accessories. The device generates HF power, which is transmitted to the tissue through the output Pencil. Operator selects the output function and power with the front panel. The indicator is used to indicate the setup status and output status of the output is controlled by foot or hand switch.

The provided document is a 510(k) premarket notification for an Electrosurgical Generator and Accessories. This type of submission focuses on demonstrating substantial equivalence to a legally marketed predicate device rather than fulfilling specific acceptance criteria for a novel device's performance in a clinical setting like an AI algorithm would.

Therefore, the document does not contain the information requested regarding acceptance criteria for an AI device, a study proving it meets those criteria, sample sizes for test/training sets, expert adjudication methods, MRMC studies, or specific ground truth establishment for AI.

Instead, the document details:

• Acceptance Criteria (for this device's type): Demonstrated safety and effectiveness through compliance with recognized standards for electromedical devices (IEC 60601-1, IEC 60601-1-2, IEC 60601-2-2) and FDA guidance for electrosurgical devices.
• Study Proving Acceptance: Non-clinical (bench) performance testing, electrical safety testing (IEC 60601-1), electromagnetic compatibility testing (IEC 60601-1-2), high-frequency safety testing (IEC 60601-2-2), and usability testing (IEC 60601-1-6). The document states these tests were "successfully completed."
• Performance Comparison: The subject device's technological characteristics (e.g., maximum power, voltage output, waveforms for different modes) are compared in a table to its predicate device (Medrange Corporation MB 8010). The differences are presented, and the conclusion is that these differences "do not raise new questions of safety and effectiveness."

In summary, the information requested in your prompt (especially points 2-9) is typical for AI/ML device submissions, which often involve clinical performance studies. The provided document is for an electrosurgical generator, a hardware device, and thus the basis for its clearance is non-clinical testing and comparison to a predicate device, not in-depth clinical studies with human readers or AI ground truth establishment.

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The Electrosurgical Generator – Model FW-350A is a non-sterile, reusable multi-purpose electrosurgical generator that is designed to perform monopolar and bipolar functions in the surgical operation area.

Electrosurgical Generator-Model FW-350A has Monopolar and Bipolar modes that can satisfy the general requirements of electrosurgical field. The CQMS function can monitor the area of patient body applied with ESU pad during the operation every 40ps. Once the pad loses or that area shrinks, the computer will cut off all outputs within 40us to prevent patient from getting burnt. The PPS system ensure the safety of a constant output that send out compulsory stimulation at the initial instant of every CUT or COAG to make the operation process go smoothly. Moreover, the memory function can decrease the medical staff workload and human errors. At last, the Power ON self diagnostics function can diagnosis whether all working modes and functions are working normally and feedback information by code displaying and alarm voice prompt that the operational safety is improved greatly.

The provided text describes a 510(k) premarket notification for an Electrosurgical Generator, Model FW-350A. This type of submission focuses on demonstrating substantial equivalence to a legally marketed predicate device, rather than providing extensive details on novel acceptance criteria and comprehensive study results as would be typical for a de novo submission or a new technology.

Therefore, the information available in the document primarily addresses the safety and performance of the device relative to existing standards and a predicate device. It does not contain an "acceptance criteria" table in the classical sense, nor detailed study results that prove the device meets these criteria in a standalone, statistically robust manner. Instead, the "acceptance criteria" are implied by compliance with recognized standards and a demonstration of comparable performance to the predicate device through bench testing.

Here's a breakdown of the information that can be extracted, interpreted based on the context of a 510(k) submission:

1. Table of Implied Acceptance Criteria and Reported Device Performance

Since this is a 510(k) submission, the "acceptance criteria" are primarily based on demonstrating equivalence to the predicate device and compliance with relevant safety standards. The reported device performance is presented in comparison to the predicate.

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

• Test Set Sample Size: The document does not specify a numerical "sample size" for the performance tests in the typical sense (e.g., number of patients or cases). For the thermal effects testing, it states "ex vivo animal tissues (porcine muscle, liver, and kidney)," but does not quantify the number of tissue samples or repetitions.
• Data Provenance: The thermal effects testing was performed on "ex vivo animal tissues" (porcine). These are non-clinical, bench tests. The document does not specify the country of origin for the tissue data, nor if it was retrospective or prospective, though "ex vivo" implies a laboratory setting. Clinical or animal studies were explicitly stated as "not necessary."

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

There is no mention of experts used to establish ground truth for a test set, expert qualifications, or adjudication methods in this document. This is typical for electrosurgical generators in a 510(k) submission, where performance is evaluated through physical and electrical bench testing against established standards and comparison to a predicate, rather than human expert assessment of outcomes.

4. Adjudication Method for the Test Set

Not applicable/Not mentioned. As performance evaluation relies on objective bench testing against standards and direct comparison of measurable parameters to a predicate device, human adjudication of "ground truth" for a test set is not part of this submission process.

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/Not mentioned. This document concerns an electrosurgical generator, which is not an AI-powered diagnostic or interpretive device that would involve human "readers" or an "AI assistance" component in the clinical workflow. Therefore, an MRMC comparative effectiveness study is not relevant to this device.

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

Not applicable/Not mentioned. This is an electrosurgical generator, a hardware device, not a standalone algorithm. Its performance is evaluated for physical and electrical characteristics and tissue effects, not as an interpretive algorithm.

7. The Type of Ground Truth Used (expert consensus, pathology, outcomes data, etc.)

For the performance testing, specifically thermal effects testing, the "ground truth" (or objective measure) was the directly measured "size (length, width and depth) of the thermal zone" on the ex vivo animal tissues. This is a direct physical measurement. For other aspects, compliance with international standards (IEC 60601 series) served as the "ground truth" for safety and basic performance parameters.

8. The Sample Size for the Training Set

Not applicable/Not mentioned. This document does not describe an AI/machine learning model that would require a "training set." The software validation refers to standard software development and verification/validation processes, not machine learning model training.

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

Not applicable/Not mentioned. As there is no described AI/machine learning model or training set, the establishment of ground truth for such a set is not discussed.

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Electrosurgical generator intended for tissue cutting and coagulation with electrosurgical accessories and ancillary equipment.

The Olympus ESG-200 Electrosurgical Generators: (CELON Elite WA90001A, WA90002A and CELON Precision WA90008A, WA90009A), including its two variants the Celon Elite and Celon Precision are reusable, non-sterile electrosurgical generators that feature different mono- and bipolar cutting and coagulation modes. The maximum output power is 120 W. Both variants of the ESG-200 are intended for cutting and coagulation of tissue in open, laparoscopic and endoscopic surgery in conjunction with electrosurgical accessories and ancillary equipment. The front panels of the proposed ESG-200 features a touch screen GUI (graphical user interface) that displays the connection status of accessories and peripherals connected to the electrosurgical generator. Two contact quality indicators (one for split and one for non-split electrodes) are green illuminated if neutral electrodes are correctly connected and red if not. Three additional push buttons allow recalling a previously saved setting (Select Procedure), to assign a footswitch to a specific output socket (Footswitch), and to control several other functions (Menu), e.g. select language, touch tone control, output volume, or brightness. The touch screen is also used to show and modify the output settings (e.g. mode, output power, effect) as well as to control other functions (e.g. save settings). In addition, the proposed ESG-200 has a bipolar socket (with automatic instrument recognition of selected Olympus instruments), a monopolar socket (E- or B-Type depending on the model), and a neutral electrode socket.

This document is a 510(k) summary for the Olympus ESG-200 Electrosurgical Generator, asserting its substantial equivalence to predicate devices. It describes the device, its intended use, technological characteristics, and performance data. However, it does not explicitly define acceptance criteria or detail a specific study proving the device meets them in the format requested.

Here's an analysis based on the provided text, addressing the points where information is available or can be inferred:

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

The document does not provide a formal table of acceptance criteria with corresponding performance metrics. Instead, it discusses the assessment of "substantial equivalence" based on comparing the ESG-200's performance with predicate devices.

The general performance criteria for electrosurgical generators would typically revolve around:

• Tissue Effects: The ability to achieve comparable cutting and coagulation effects as the predicate device.
• Electrical Waveforms: Generation of electrical waveforms that are comparable to those produced by the predicate device for various modes.
• Safety Standards Compliance: Adherence to recognized international safety standards.

Based on the text:

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

• Sample Size for Test Set: The document mentions the use of "Porcine muscle, kidney and liver tissue" for tissue effect equivalence testing, but it does not specify the sample size (e.g., number of tissue samples or repetitions).
• Data Provenance: Not explicitly stated, but bench testing on animal tissue (porcine) is indicated. The manufacturer, Olympus Winter & Ibe GmbH, is located in Hamburg, Germany, suggesting the testing likely occurred there or in a related facility. This would be prospective bench testing.

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 section is not applicable as the study described is a bench performance test comparing physical characteristics (waveforms, tissue effects) between devices, not a study requiring human expert assessment for ground truth.

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

This section is not applicable for the reasons stated above. The comparison was objective measurement of electrical waveforms and qualitative assessment of tissue effects.

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 section is not applicable. The device is an electrosurgical generator, not an AI-powered diagnostic or assistive tool.

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

This section is not applicable as the device is not an algorithm or AI. The performance testing was of the electrosurgical generator itself.

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

For the bench testing described:

• Electrical Waveforms: The "ground truth" for electrical waveforms would be their direct measurement using an oscilloscope and high voltage probe and comparison against the established characteristics of the predicate device (which is itself a legally marketed, validated device).
• Tissue Effects: The "ground truth" for tissue effects would be the observed impact on porcine tissue. The assessment was a direct comparison to the effects produced by the predicate device, implying the predicate's tissue effects serve as the reference standard.

8. The sample size for the training set

This section is not applicable as the device is not an AI/machine learning device that requires a training set.

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

This section is not applicable as the device is not an AI/machine learning device that requires a training set.

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The Electrosurgical Generator (HV-300A, HV-400) is a non-sterile, reusable multi-purpose electrosurgical generator that is designed to perform monopolar and bipolar functions in the operating arena.

The proposed device (HV-400 and HV-300A) is an electrosurgical generator with, monopolar and bipolar, high frequency current isolation outputs. The proposed device is independent equipment, which include generator and accessories. The main components are front panel ( power switch, touchable screen, operation key, indicator, connector for accessories), back panel (volume controller, foot switch interface, power cable outlet and fuse) and inner component (display panel, main board, control board, speaker etc.). The accessories include power cable, III-2 cable and foot switch). The two models of proposed device share appearance, function, accessories, the only difference is the output parameter.

The provided text describes an Electrosurgical Generator (HV-300A, HV-400) and its 510(k) summary, which focuses on demonstrating substantial equivalence to predicate devices rather than proving performance against specific acceptance criteria for a novel AI/software function. Therefore, much of the requested information (e.g., sample size for test/training sets, ground truth methodology, expert qualifications, MRMC study, standalone algorithm performance) is not applicable or not provided in this document.

However, based on the non-clinical tests conducted, we can infer the acceptance criteria for basic safety and essential performance, and the study showing the device meets these criteria.

1. Table of Acceptance Criteria and Reported Device Performance

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

• Sample Size: Not applicable/Not provided. The tests described are non-clinical hardware tests against international standards, not performance evaluations on a data set with a specific sample size of patient data.
• Data Provenance: Not applicable/Not provided.

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

• Not applicable/Not provided. Ground truth in the context of diagnostic or AI performance is not relevant for these hardware safety and performance tests. Compliance with standards is typically assessed by engineers and testing bodies.

4. Adjudication method for the test set

• Not applicable/Not provided. This concept is relevant for expert disagreement resolution in diagnostic studies, not for compliance testing of electrosurgical generators to established ISO/IEC standards.

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. This device is an electrosurgical generator, which is a hardware device for surgical procedures, not an AI or diagnostic imaging system that would involve human readers.

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

• No. This device is a hardware electrosurgical generator. It does not involve a standalone algorithm in the sense of AI or software performance evaluation.

7. The type of ground truth used

• The "ground truth" here is the requirements set forth by the specific international safety and performance standards (IEC 60601-1, IEC 60601-1-2, IEC 60601-2-2). The device's performance was compared directly against these documented technical specifications and limits within the standards.

8. The sample size for the training set

• Not applicable/Not provided. This is not an AI/machine learning device that involves training sets.

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

• Not applicable/Not provided.

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The SenoRx ES 3000 is an electrosurgical generator which is intended for general surgical procedures where electrosurgical cutting or coagulation of soft tissues is required.

Not Found

I am sorry, but based on the provided document, there is no information about acceptance criteria for a device's performance, nor any study proving the device meets said criteria.

The document is a 510(k) clearance letter from the FDA for the "ES 3000 Electrosurgical Generator System." This letter confirms that the device has been found substantially equivalent to a legally marketed predicate device, allowing it to proceed to market.

Here's why the information you're looking for is not present:

• 510(k) clearance primarily focuses on demonstrating substantial equivalence to a predicate device, not necessarily on proving new performance criteria through extensive studies. While performance data may be submitted as part of a 510(k), the letter itself does not detail specific acceptance criteria or study results.
• The document mentions the device name (ES 3000 Electrosurgical Generator System) and its intended indications for use (general surgical procedures where electrosurgical cutting or coagulation of soft tissues is required). These are regulatory classifications and intended uses, not performance metrics or study outcomes.
• The letter refers to general controls provisions of the Act (annual registration, listing, good manufacturing practice, labeling, prohibitions against misbranding and adulteration) and, if applicable, special controls. These are regulatory requirements for all devices, not performance-specific acceptance criteria for this particular device.

Therefore, I cannot populate the table or answer the specific questions about acceptance criteria, sample sizes, expert qualifications, adjudication, MRMC studies, standalone performance, ground truth, or training set details as this information is not contained within the provided FDA 510(k) clearance letter.

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