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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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The Da Vinci Monopolar and Bipolar Adapters are intended for connecting monopolar or bipolar electrosurgical instruments to an electrosurgical generator to provide transmission of high frequency energy from the electrosurgical generator to the surgical instrument.

The Da Vinci Monopolar and Bipolar Adapters are an accessory for the Da Vinci E-200 Electrosurgical Generator (K223039, K231212). The Da Vinci Monopolar and Bipolar Adapters (referred to herein as "Adapters") are non-sterile. They are designed to enable the use of certain, third-party electrosurgical instruments with specific monopolar and bipolar plug formats (refer to with the E-200 Electrosurgical Generator). This enables the transmission of high-frequency energy from the electrosurgical generator to the surgical instrument.

The provided text is a 510(k) summary for the Da Vinci Monopolar and Bipolar Adapters. It describes the device, its intended use, and the studies conducted to demonstrate its substantial equivalence to a predicate device. However, it does not contain the specific details required to answer all parts of your request regarding the acceptance criteria and the study proving the device meets those criteria for an AI/ML-based medical device.

The Da Vinci Monopolar and Bipolar Adapters are described as an accessory for an electrosurgical generator, designed to connect instruments to the generator for transmission of high-frequency energy. This is a hardware device, not an AI/ML software device for diagnostic or prognostic purposes. Therefore, the types of studies and acceptance criteria you've outlined (e.g., MRMC studies, ground truth establishment, training sets, AI assistance metrics) are not applicable to this device.

The performance data mentioned are typical for hardware accessories:

• Design Verification (Bench Testing): Covered hardware requirements, reliability, EMC and Electrical Safety, and Packaging and Labeling.
• Design Validation: Simulated clinical use with a cadaver model.
• Human Factor Evaluation: Assessed safety and effectiveness for intended users and use environments.
• Transit Testing: Evaluated packaging.

Since your request is tailored for an AI/ML medical device, and the provided document is for a hardware accessory, I cannot extract the information you're asking for directly from this text. The acceptance criteria for this device would be based on the successful completion of the described engineering and usability tests, demonstrating functional performance, electrical safety, and mechanical integrity, rather than diagnostic accuracy or reader improvement metrics.

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