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The gi2000 electrosurgery unit (ESU) is intended to deliver electrosurgical outputs to perform cutting and/or coagulation, in flexible endoscopic applications.

The gi2000 is an electrosurgical generator for use in flexible endoscopic clinical settings, employing high-frequency energies to carry out cutting and coagulation of tissue during gastrointestinal surgical procedures. This isolated output electrosurgical unit (ESU) includes all standard features commonly utilized in a broad spectrum of gastrointestinal surgical procedures, encompassing monopolar cutting, monopolar coagulation, and monopolar spray coagulation, along with bipolar coagulation.

The gi2000 consists of the high-frequency electrosurgical unit with control panel and an operating foot switch. The handpiece (electrodes) and dispersive pad (return electrode) are not included with the gi2000. Instead, device labeling lists optional pieces that have been validated with the device.

The provided text is a 510(k) clearance letter for an electrosurgical generator (gi2000). While it describes the device, its intended use, and generally mentions that performance testing was completed, it does not contain the specific acceptance criteria or detailed study results as typically found in a clinical study report or a more comprehensive summary of safety and effectiveness data.

The 510(k) summary states, "Performance testing on the gi2000 was completed by three methods," and then lists the types of studies: ex-vivo comparative study, accessory compatibility study, and in-vivo preclinical test. However, it only references internal document IDs (e.g., "40-0036-11 DS, Predicate Device Comparative Study Report, gi2000") rather than providing the data itself.

Therefore, I cannot extract the detailed information requested regarding acceptance criteria and device performance, sample sizes, expert qualifications, or multi-reader multi-case studies from the provided text. The document confirms that performance testing was done and demonstrated substantial equivalence, but it does not present the specific data points to fill out your requested table and study details.

To address your request, I would need a document that contains the actual results of these performance tests, including the quantitative acceptance criteria and the values measured for the gi2000.

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The CROMA Electrosurgical Generator is intended to provide Radiofrequency (RF) and microwave (MW) energy for cutting, coagulation, and ablation of soft tissue, when used in conjunction with compatible electrosurgical instruments and accessories.

The Creo Medical Electrosurgical System is not intended for use in cardiac procedures.

The Reusable Interface Cable is for connection only of a compatible Creo Medical Instrument (Surgical Accessory) to the CROMA Electrosurgical Generator to deliver Radiofrequency (RF) and/or Microwave (MW) energy for the cutting, coagulation and ablation of tissue via endoscopic access.

The CROMA Electrosurgical Generator is an Electrosurgical unit (ESU) to be used with compatible electrosurgical instruments and accessories.

The CROMA Electrosurgical Generator is a tabletop, non-network connected, mains powered ESU. It comprises two distinct energy sources for the independent generation of bipolar radiofrequency (RF) and microwave (MW) energies, intended to supply electrosurgical power for cutting, and ablation of soft tissues, when connected to compatible instruments via the interface cable.

The CROMA Electrosurgical Generator incorporates proprietary software developed by Creo Medical Ltd.

The CROMA Electrosurgical Generator subject of this submission incorporates a new front panel, which has been updated from a Vacuum Fluorescent Display (VFD) in the predicate device to a Liquid Crystal Display (LCD) screen in the subject device.

The provided text describes a 510(k) premarket notification for the CROMA Electrosurgical Generator. This document is a regulatory submission to the FDA demonstrating substantial equivalence to a predicate device, rather than a study designed to prove the device meets specific acceptance criteria in the context of an AI-powered diagnostic. Therefore, many of the requested sections are not applicable.

Here's the information that can be extracted or derived from the provided text:

1. Table of Acceptance Criteria and Reported Device Performance

The document does not explicitly state "acceptance criteria" in a quantitative, measurable sense for a diagnostic device's performance. Instead, it focuses on demonstrating substantial equivalence to a predicate electrosurgical generator by comparing technological characteristics and compliance with electrical safety and software standards.

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

Not applicable. This device is an electrosurgical generator, not an AI-powered diagnostic or a device that uses a "test set" of patient data in the way implied by the question. The testing described is primarily engineering and software verification/validation.

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

Not applicable. As noted above, this device is not an AI-powered diagnostic, and therefore, the concept of "ground truth" established by experts for a test set of data does not apply in this context.

4. Adjudication Method for the Test Set

Not applicable.

5. If a Multi-Reader Multi-Case (MRMC) Comparative Effectiveness Study Was Done

No. The document explicitly states: "Animal & Clinical Studies: Not Applicable." An MRMC study would fall under clinical studies.

6. If a Standalone (Algorithm Only Without Human-in-the-Loop Performance) Was Done

No, an electrosurgical generator does not have a "standalone algorithm" in the context of AI performance. The software mentioned is for controlling the device's functions and user interface.

7. The Type of Ground Truth Used

Not applicable. The ground truth concept, as it pertains to diagnostic accuracy, is not relevant here. The "truth" for this device relates to its adherence to engineering specifications, safety standards, and functional performance.

8. The Sample Size for the Training Set

Not applicable. This device does not employ machine learning or AI that would require a "training set" of data in the common sense.

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

Not applicable.

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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 da Vinci E-200 Electrosurgical Generator is intended to deliver high-frequency energy for cutting, coagulation and vessel sealing of tissues in da Vinci robotic procedures, and non-robotic open and laparoscopic procedures.

The da Vinci E-200 Electrosurgical Generator is an electrosurgical unit (ESU) designed to provide high-frequency (HF) traditional monopolar, bipolar, and advanced bipolar outputs intended for cutting, coagulation and/or vessel sealing of tissues. The da Vinci E-200 Electrosurgical Generator is intended to be used with the da Vinci Xi, and da Vinci 5 surgical systems, and also operate as a standalone electrosurgical generator. When connected to the E-200 provides HF output to da Vinci instruments. Control and status messages are passed between the E-200 and the da Vinci system through an Ethernet communication cable. The E-200 is also compatible with open and laparoscopic third-party handheld monopolar and bipolar instruments, fingerswitch equipped instruments (where applicable) and Intuitive provided auxiliary footswitches. The primary function of the E-200 Electrosurgical Generator is to allow a surgeon to deliver HF out, seal, or coagulate tissue during surgery. The user interface includes audible indicator tones, LED indicators on the front of the generator, and status messages provided on its LCD display.

This document focuses on the da Vinci E-200 Electrosurgical Generator, which is an electrosurgical unit (ESU). The information provided is a 510(k) summary, which inherently focuses on demonstrating substantial equivalence to a predicate device rather than presenting a detailed de novo study with strict acceptance criteria and performance metrics for a novel technology.

Therefore, the requested information elements related to standalone performance, MRMC studies, specific acceptance criteria values, sample sizes for test and training sets, and expert details for ground truth establishment are not explicitly described in the provided text in the manner typically found for AI/ML device studies. The document describes a traditional medical device (electrosurgical generator) and its safety and efficacy testing, not an AI/ML diagnostic or predictive system.

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

1. Table of Acceptance Criteria and Reported Device Performance:

The document does not provide a table with specific numerical acceptance criteria and corresponding reported device performance values. Instead, it states that "Verfication and validation activities were successfully completed that the subject device performs as intended and is substantially equivalent to its predicate."

The "acceptance criteria" are implied by the successful completion of the following testing types:

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

• Sample Size for Test Set: Not explicitly stated. For bench testing, this would refer to the number of test cases or iterations. For simulated clinical use, it refers to the number of porcine models or procedures performed.
• Data Provenance:
  • Bench Testing: Likely internal laboratory testing at Intuitive Surgical.
  • Simulated Clinical Use: Performed with a porcine model, indicating animal tissue (non-human, in vivo or ex vivo animal studies).
  • Retrospective or Prospective: These tests are inherently prospective, as they are conducted specifically for the submission.

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

This concept of "ground truth" established by experts, as typically applied to image-based AI diagnostics, is not directly applicable here. The device is an electrosurgical generator, and its performance (e.g., cutting efficacy, coagulation, vessel sealing) is assessed through objective measurements (bench testing) and direct observation/clinical evaluation (simulated clinical use).

For the Human Factor Evaluation, experts (likely human factors engineers and potentially medical professionals) would assess usability and safety, but they are evaluating the device's interaction with users, not establishing a "ground truth" for a diagnostic output.

4. Adjudication Method for the Test Set:

Not applicable in the context of electrosurgical generator testing as described. Adjudication methods (like 2+1 or 3+1) are typically used to resolve disagreements among multiple expert readers establishing ground truth for diagnostic decisions, which is not the primary output of this device.

5. If a Multi-Reader Multi-Case (MRMC) Comparative Effectiveness Study was Done:

No, an MRMC comparative effectiveness study is not mentioned. Such studies are generally performed for diagnostic devices, especially those incorporating AI, to compare human performance with and without AI assistance. This device is a surgical tool, not a diagnostic one.

6. If a Standalone (Algorithm Only Without Human-in-the-Loop Performance) Study was Done:

The "standalone" performance for this device refers to its ability to function as an electrosurgical generator without being integrated into a da Vinci robotic system. The device description explicitly states: "The da Vinci E-200 Electrosurgical Generator... also operate as a standalone electrosurgical generator." The testing described (bench testing, simulated clinical use, human factor evaluation) would have covered this standalone operation, ensuring its basic electrosurgical functions (cutting, coagulation, sealing) are performed as intended. However, "standalone" in the context of an algorithm's diagnostic performance (without human interpretation) is not relevant here.

7. The Type of Ground Truth Used:

• Bench Testing: Engineering specifications, electrical safety standards, EMC standards, software requirements, measured physical parameters (e.g., power output, frequency).
• Simulated Clinical Use (Porcine Model): Direct observation of tissue effects (e.g., cut quality, coagulation adequacy, seal strength) by qualified personnel, possibly confirmed by gross and/or histopathological examination. Performance against a "gold standard" of expected surgical outcomes for electrosurgery.
• Human Factor Evaluation: Usability metrics, error rates, user feedback, adherence to human factors engineering principles.

8. The Sample Size for the Training Set:

Not applicable. This is not an AI/ML device that requires a training set of data. The "training set" concept is relevant for machine learning algorithms, which "learn" from data. This device is a traditional electrosurgical generator engineered to specific design parameters.

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

Not applicable for the same reason as point 8.

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General-purpose solid-state bipolar generator used to supply High Frequency currents via electrosurgical handpieces for the function of cutting or coagulating soft body tissues where a wide range of tissue types, patient conditions, and load impedances are encountered.

The proposed Polaris™ Bipolar Electrosurgical Generator System includes a Generator as the main console, a Footswitch, an Irrigation Module, and a Light Source Module. The Generator contains a single bipolar channel for delivery with an electrode applying the energy to the patient. The Irrigation Module connects to tubing that allows fluid to be delivered to the surgical site to allow for clearing of debris from the surgical field. The Light Source Module connects to an optical fiber that supplies illumination to the surgical site to assist with surgeon visualization. The Footswitch is used to control the delivery of RF energy to the patient with one switch for Coagulate and one for Cut power. The Generator includes 4 Module ports for controlling and powering the Modules each have built in cords to connect to these ports on the Generator. The Modules mount to the Generator using a locking rail system so that they will not accidentally come loose during use. The Irrigation Module includes an IV pole that attaches to the Generator to support a saline bag that supplies the irrigation fluid.

The provided document is a 510(k) summary for the Polaris Bipolar Electrosurgical Generator System. It focuses on demonstrating substantial equivalence to a predicate device, rather than providing the detailed results of a clinical study for a new AI/ML-driven device. Therefore, much of the requested information regarding acceptance criteria and study details for an AI/ML device (e.g., sample size for training/test sets, expert adjudication, MRMC study, ground truth establishment) is not present in this document.

However, I can extract the relevant "acceptance criteria" and "study results" related to the functional performance of this electrosurgical generator, which are presented as non-clinical performance data comparing the new device to its predicate.

Here's the information that can be extracted and how it relates to your request, with a clear indication of what is not available in this document:

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

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

• Thermal Effects & Waveform Testing:

  • Test Set Sample Size: "one Aura Bipolar Electrosurgical Generator and one Polaris Bipolar Electrosurgical Generator." For thermal effects, they used "the same bipolar forceps sample part on the same tissue samples."
  • Data Provenance: The testing was performed comparatively between the new device and the predicate. The tissue samples are noted as "Bovine" (Liver, Kidney, Muscle Tissue). This is a retrospective comparison against a legally marketed predicate, using in-vitro or ex-vivo animal tissue samples in a lab setting. No information on country of origin of the data.

• Electrical Safety & EMC, Software V&V, Human Factors: These describe engineering tests or simulated user studies, not typically involving patient data. No specific "data provenance" in terms of patient population or geographic origin is applicable.

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

This document does not describe a study that uses "experts" to establish "ground truth" in the way one would for diagnostic AI. The "ground truth" in this context is the physical and electrical performance characteristics of the device, measured by engineering tests.

• Thermal Effects & Waveform Testing: The ground truth is established by direct physical and electrical measurements using test equipment and procedures. There's no mention of experts establishing a subjective ground truth.
• Human Factors Testing: This involved "user profiles" (Surgeons/PAs, Perioperative Nurses, Biomedical Equipment Technicians), who are indeed experts in their field, but they are the test subjects evaluating the device's usability, not establishing a diagnostic ground truth. No specific number of such users is given beyond "The first user profile [Surgeons & Surgical Physician Assistants], and the second [Perioperative Nurses and Biomedical Equipment Technicians]." It states "27 Tasks" and "19 Critical Tasks" were successfully performed. "The few Users who did not score perfect..." implies a number greater than a few but less than a large cohort.

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

Not applicable for this type of device and study. Adjudication methods like 2+1/3+1 are typically used for establishing ground truth in diagnostic imaging studies, where human expert interpretation might disagree. Here, the "truth" is based on objective, quantifiable engineering measurements or observation of user interaction.

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 not an AI-assisted diagnostic device, nor is it a study comparing human reader performance. It's an electrosurgical generator.

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

Not applicable. This is not an AI/algorithm-driven device. The "standalone" performance refers to the generator's electrical and thermal output, which was indeed tested.

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

As described in point 3:

• Thermal Effects and Waveform Testing: The ground truth is objective physical (e.g., coagulation site size, temperature) and electrical (e.g., frequency, voltage RMS) measurements, compared against a predicate device.
• Human Factors Testing: The ground truth is the observer's assessment of whether users successfully and safely completed predefined tasks, based on pre-established criteria for safe and effective operation.

8. The sample size for the training set

Not applicable. This device is not an AI/ML device that requires a training set.

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

Not applicable. As above, this device does not utilize a training set as it's not an AI/ML product.

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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 da Vinci E-200 Electrosurgical Generator is intended to deliver high-frequency energy for cutting, coagulation and vessel sealing of tissues in da Vinci robotic procedures, and non-robotic open and laparoscopic procedures.

The da Vinci E-200 is an electrosurgical unit (ESU) designed to provide high-frequency (HF) traditional monopolar, bipolar, and advanced bipolar outputs intended for cutting, coagulation and/or vessel sealing of tissues. The da Vinci E-200 is intended to be used with the IS4000/IS4200 da Vinci surgical systems, and also operate as a standalone electrosurgical generator. When connected to the da Vinci surgical system, the E-200 provides HF output to da Vinci instruments. Control and status messages are passed between the E-200 and the da Vinci system through an Ethernet communication cable. The E-200 is also compatible with open and laparoscopic third-party handheld monopolar and bipolar instruments, fingerswitch equipped instruments (where applicable) and Intuitive provided auxiliary footswitches. The primary function of the E-200 Electrosurgical Generator is to allow a surgeon to deliver HF output to cut, seal, or coagulate tissue during surgery. The user interface includes audible indicator tones, LED indicators on the front of the generator, and status messages provided on its LCD display.

The provided text is a 510(k) Summary for the da Vinci E-200 Electrosurgical Generator. It focuses on demonstrating substantial equivalence to a predicate device through verification and validation activities. However, it does not resemble a study report for an AI/ML device, which would typically include acceptance criteria, specific performance metrics like AUC, sensitivity, specificity, and details about human reader studies or ground truth establishment relevant to AI/ML performance.

The document describes standard medical device testing, such as:

• Design Verification (bench testing): Hardware, EMC, Electrical Safety, Software (including Cybersecurity), System interface, Instrument compatibility, Packaging, and Labeling.
• Design Validation (in-vivo porcine model): Safety and efficacy evaluation, including accessories and compatible systems in a simulated surgical setting.
• Thermal effects testing: Comparing the new monopolar "Low" coagulation mode to a reference device (Valleylab FT-10 Energy Platform).
• Neutral electrode contact quality monitor (CQM) study: Conformance to IEC standards with a specific neutral electrode pad.
• Capacitive coupling behavior evaluation: Comparing to the predicate device.
• Human factors evaluation: Safety and effectiveness in various configurations.

Therefore, I cannot extract the information required for an AI/ML device acceptance criteria and study from this document. The questions posed in your prompt (e.g., sample size for test set, number of experts for ground truth, MRMC study, standalone performance, training set details) are specific to the evaluation of AI/ML algorithms and are not addressed in this 510(k) summary for an electrosurgical generator.

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The da Vinci E-200 Electrosurgical Generator is intended to deliver high-frequency energy for cutting, coagulation and vessel sealing of tissues in da Vinci robotic procedures, and non-robotic open and laparoscopic procedures.

The da Vinci E-200 is an electrosurgical unit (ESU) designed to provide high-frequency (HF) traditional monopolar, bipolar, and advanced bipolar outputs intended for cutting, coagulation and/or vessel sealing of tissues. The da Vinci E-200 is intended to be used with the IS4000/IS4200 da Vinci surgical systems and also operate as a standalone electrosurgical generator. When connected to the da Vinci surgical system, the E-200 provides HF output to da Vinci instruments. Control and status messages are passed between the E-200 and the da Vinci system through an Ethernet communication cable. The E-200 is also compatible with open and laparoscopic third-party handheld monopolar and bipolar instruments, fingerswitch equipped instruments (where applicable) and Intuitive provided auxiliary footswitches. The primary function of the E-200 Electrosurgical Generator is to allow a surgeon to deliver HF output to cut, seal, or coagulate human tissue during surgery. The user interface includes audible indicator tones, LED indicators on the front of the generator, and status messages provided on its LCD display.

This document is a 510(k) premarket notification for the da Vinci E-200 Electrosurgical Generator, indicating that it is a medical device approval document and not a study on AI-assisted human reading, or a diagnostic AI device. Therefore, many of the requested criteria related to AI studies (e.g., sample size for test/training sets, ground truth establishment, expert adjudication, MRMC studies, standalone performance) are not applicable to this submission.

The document primarily focuses on demonstrating the substantial equivalence of the da Vinci E-200 Electrosurgical Generator to existing predicate devices through bench testing, in-vivo/ex-vivo models, and human factors evaluation, rather than evaluating an AI algorithm's performance in a diagnostic context.

However, I can extract and present the available information regarding the device's performance and acceptance criteria based on the provided text.

1. Table of Acceptance Criteria and Reported Device Performance

Since this is an electrosurgical generator rather than a diagnostic AI device, the "acceptance criteria" are related to its functional performance, safety, and equivalence to predicate devices, rather than typical diagnostic performance metrics (e.g., sensitivity, specificity, AUC). The document outlines various tests conducted to demonstrate the device meets its design requirements and is safe and effective.

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 individual tests like "Design Verification" (bench testing), "Thermal Effects Testing," or "CQM Study." For the in-vivo validation, it mentions a "porcine model," but no specific number of animals or cases. Similarly, "vessel sealing bench testing" and "capacitive coupling evaluation" do not specify sample sizes.
• Data Provenance: The studies appear to be prospective bench tests and animal model studies conducted by the manufacturer, Intuitive Surgical, Inc. The document does not mention the country of origin for the data beyond implying it was conducted as part of the FDA submission process.

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

This concept is not applicable to this type of device submission. Ground truth in this context refers to the physical and electrical performance of an electrosurgical unit, verified through established engineering and medical device testing standards, rather than expert interpretation of medical images or data. Performance is measured against physical specifications and validated against in-vivo/ex-vivo results.

4. Adjudication Method for the Test Set

This concept is not applicable. Adjudication typically refers to resolving disagreements among human readers or labelers in diagnostic AI studies. The performance validation of an electrosurgical generator involves objective measurements and functional tests, not human interpretation that requires adjudication.

5. If a Multi-Reader Multi-Case (MRMC) Comparative Effectiveness Study was done

No, a Multi-Reader Multi-Case (MRMC) comparative effectiveness study was not done. This type of study is relevant for evaluating the impact of AI assistance on human reader performance in diagnostic tasks, which is not the purpose of this submission for an electrosurgical generator.

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

Not applicable in the context of AI algorithm performance. The "standalone" operation mentioned in the document refers to the E-200 Electrosurgical Generator operating independently of the da Vinci robotic system, as a traditional ESU. It's a device operating standalone, not an AI algorithm.

7. The Type of Ground Truth Used

The "ground truth" for this device's performance is established by:

• Design Input Requirements/Specifications: The device's performance is validated against its pre-defined engineering and functional requirements.
• Industry and IEC Standards: Conformance to relevant electrical safety and performance standards.
• Predicate Device Performance: Demonstrating comparable performance to legally marketed predicate devices (Covidien ForceTRIAD and Intuitive E-100) through direct comparative testing (e.g., thermal effects, HF output characteristics, capacitive coupling).
• In-vivo/Ex-vivo Models: Actual physiological effects in tissue (e.g., cutting, coagulation, vessel sealing effectiveness) observed in animal (porcine) models and ex-vivo settings.
• User Needs/Safe and Effective Use: Confirmed through human factors evaluation with intended users.

8. The Sample Size for the Training Set

Not applicable. This submission is for an electrosurgical generator, not an AI model, so there is no "training set."

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

Not applicable. As there is no AI model or training set, the concept of establishing ground truth for it does not apply.

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The Beamer AVEO™ Electrosurgical Generator System with Accessories is intended to deliver electrosurgical current and Argon gas for the cutting, coagulation and argon beam assisted coagulation of tissue. The Beamer AVEO™ System is used in conjunction with compatible applicators or probes.

The Beamer AVEO™ Electrosurgical Generator and Argon Module (aka Beamer AVEO System / Beamer AVEO Electrosurgical System) is an electrosurgical generator that is used in surgical procedures and uses High-Frequency (HF) current through an accessory electrode for cutting and coagulation at the operative site. It is also intended to be used for enhanced control of bleeding by the delivery of HF electrosurgical current in combination with Argon gas through a compatible accessory. The Beamer AVEO System can be used for all patient types, conditions where electrosurgery is relevant, on all parts of human body.

The Beamer AVEO System is comprised of an Electrosurgical Generator (AVEO-ESU), Argon Module control (AVEO-ABC), Beamer AVEO cart and accessories (wired foot pedal, pressure reducer). A Graphical User Interface (GUI) and associated footswitches are provided for user interaction. Monopolar instruments, bipolar instruments, Argon probes, and a footswitch are connected to the AVEO system.

The provided text is a 510(k) summary for a medical device (Beamer AVEO Electrosurgical Generator and Argon Module). It describes the device, its intended use, and its substantial equivalence to predicate devices, supported by performance testing. However, it does NOT contain the specific details required to answer your questions about acceptance criteria, study methodologies (like sample size, number of experts, adjudication methods, MRMC studies), ground truth establishment, or training set details for an AI/machine learning device.

This document is for an electrosurgical generator, which is a hardware device, not an AI/ML-driven diagnostic or assistive technology that would typically have the kind of "acceptance criteria" and "study" details you're asking about (e.g., related to accuracy, sensitivity, specificity, or reader performance improvement with AI). The performance testing mentioned ("Thermal Effects on Tissue testing") is a standard engineering and safety performance test for hardware devices, not a clinical study to validate an AI model's diagnostic accuracy.

Therefore, I cannot provide the requested information from the given text. The text does not describe an AI/ML device or a study with the characteristics you outlined.

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The Ethicon Megadyne Electrosurgical Generator (ESU) is intended as a general-purpose electrosurgical generator designed to produce radio frequency (RF) current for cutting and coagulation to be delivered to target tissue through an accessory electrode during open and laparoscopic surgical procedures.

The Ethicon Megadyne TM Electrosurgical Generator is a microprocessor controlled, isolated output, high frequency generator designed for use in cutting and coagulation of tissue. The generator has the ability to perform both monopolar cutting and coagulation and bipolar coagulation of tissue in a wide range of surgical applications.

The Megadyne Electrosurgical Generator, a general-purpose electrosurgical generator designed to produce radio frequency (RF) current for cutting and coagulation of tissue during surgical procedures, underwent various performance tests to ensure its safety and effectiveness following design changes.

1. Table of Acceptance Criteria and Reported Device Performance:

The document primarily focuses on demonstrating substantial equivalence to a predicate device (K193145) rather than explicitly stating acceptance criteria and reporting performance for each individual criterion in a pass/fail format. However, based on the provided "Performance Data" and the "Comparison of Technological Characteristics" tables, the acceptance criteria implicitly involve demonstrating that the device's technical specifications and performance are either identical to the predicate or that any differences do not adversely affect safety and effectiveness.

Here's a summary of the implied acceptance criteria and the device's reported performance:

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

• Sample Size: For "Bench Testing (Thermal Effects on Tissue)," testing was performed "in triplicate at minimum". This indicates at least three repetitions for each combination of Generator mode and power setting. The document does not specify a total number of tissue samples or test runs beyond this.
• Data Provenance: The data appears to be prospective bench testing conducted specifically for the purpose of this 510(k) submission to demonstrate the performance of the modified device. The document does not mention the country of origin of the data, but it is implied to be generated by the manufacturer or its contracted laboratories for the FDA submission.

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

• The document does not mention the use of experts to establish ground truth for the bench testing. The evaluation of thermal effects was done through "image analysis using open source image processing software." This suggests an objective, quantitative measurement rather than expert interpretation for the thermal effects study. For other tests like electrical safety and EMC, ground truth is established by adherence to specified standards.

4. Adjudication method for the test set:

• None specified. Given the nature of the tests (compliance with standards, objective thermal measurement via software), an adjudication method in the context of expert consensus or disagreement is not applicable or explicitly 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:

• No MRMC study was done. This device is an electrosurgical generator, not an AI-powered diagnostic or assistive tool for human readers. Therefore, an MRMC study and analysis of AI-assisted human performance are not relevant to this submission.

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

• Not applicable directly as an "algorithm only" study. The device itself is electro-mechanical, with embedded software. Software verification and validation (V&V) was performed to ensure the software functions correctly independently, but this is not a "standalone algorithm" performance in the sense of a diagnostic or predictive AI. The entire device's performance, including its software, was assessed.

7. The type of ground truth used:

• Compliance with International Standards: For electrical safety and EMC, the ground truth is defined by the requirements and test methods specified in the relevant IEC 60601 series standards.
• Objective Measurement: For thermal effects on tissue, the ground truth was based on quantitative measurements of thermal damage obtained through image analysis software.
• Manufacturer's Specifications/Design Intent: For other characteristics like operating parameters and device features, the ground truth is established by the device's design specifications and comparison to the predicate device's known characteristics.

8. The sample size for the training set:

• Not applicable. This submission is for an electrosurgical generator, which is not an AI/ML-based device that would typically involve a "training set" for model development. The software V&V confirms the performance of the embedded software, but it's not a machine learning model that learns from a training dataset.

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

• Not applicable. As a training set is not relevant for this type of device, this question is not pertinent.

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