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The ERBE ESU Model VIO dV with Accessories is intended to deliver High Frequency (HF) current for the cutting and/ or coagulation of tissue.

The ERBE ESU Model VIO dV with Accessories is an ElectroSurgical Unit that generates High Frequency (HF) electrical current to cut and/or coagulate tissue. The ESU has clearly defined Cutting and Coagulation Modes with different electrical waveforms and electrical parameters, which are programmed with defined Effect levels. The available Modes are Auto Cut, Dry Cut, Swift Coag, Forced Coag, and Bipolar Soft Coag with and without Auto Stop and BiClamp. Each Effect level corresponds to a specific voltage. The Modes provide the physician flexibility in interventional applications. Thus the Unit may be used for a broad array of surgical applications. The ESU user interface consists primarily of a touchscreen surrounded by a small number of physical controls, such as a power switch and connection points for the instruments and accessories with which the generator is compatible. Various hand instruments and neutral electrodes from ERBE and different manufacturers may be attached to and operated by the generator. The standard Accessories for the ESU consist of reusable Footswitches as well as Monopolar and Bipolar Cables. The VIO dV has a RCB (Remote Communication Bus) for remote control operation by the da da Vinci Xi Surgical System by Intuitive Surgical Inc.

The provided text describes a 510(k) premarket notification for the "ERBE ESU Model VIO dV with Accessories," an electrosurgical cutting and coagulation device. The document primarily focuses on demonstrating substantial equivalence to a predicate device, rather than presenting a detailed study with specific acceptance criteria and performance data.

Therefore, many of the requested sections below cannot be fully completed or are not applicable based on the provided text. The document states that "Animal or clinical performance testing was not considered necessary," which indicates that a detailed performance study as typically understood for AI/ML devices or novel medical devices with acceptance criteria was not performed.

Here's an attempt to extract and infer information where possible:

Acceptance Criteria and Device Performance Study Summary

The provided document, a 510(k) premarket notification, focuses on demonstrating substantial equivalence of the ERBE ESU Model VIO dV with Accessories (Software Version 3.1) to its predicate device (ERBE ESU Model VIO dV with Accessories, 510(k) Number K133180, and ERBE ESU Model 300 D with Accessories, 510(k) Number K083452). A formal, independent study with explicit acceptance criteria and corresponding reported performance metrics in the format typically seen for AI/ML or diagnostic devices is not detailed in this document.

Instead, compliance and equivalence are demonstrated through design control activities, verification and validation of software and hardware changes, and adherence to recognized consensus standards. The performance aspects discussed relate to the functional operation of the electrosurgical unit (ESU) in terms of its ability to deliver HF current for cutting and coagulation as specified, and the proper functioning of the BiClamp-Mode.

1. Table of Acceptance Criteria and Reported Device Performance

As a formal study with explicit acceptance criteria for a new AI/ML device was not conducted, this table cannot be populated as requested. The document indicates that tests were performed to show the ESU's BiClamp-Mode performs as specified and in accordance with the predicate device. The general "acceptance criteria" here implicitly refer to the device performing according to its design specifications and being substantially equivalent to the predicate.

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

• Sample Size for Test Set: Not explicitly stated. The document refers to "testing/measurement of the output parameters of the BiClamp-Mode" and "software validation for remote control functions," implying internal testing. The specific number of test cases or samples for these internal verification and validation activities is not provided.
• Data Provenance: The testing appears to be internal verification and validation conducted by the manufacturer, ERBE Elektromedizin GmbH, in Germany. This would be considered prospective internal testing.

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

• This information is not applicable to the type of testing described. The "ground truth" for an electrosurgical unit's performance typically refers to its physical output parameters matching design specifications, not expert interpretation of outputs or clinical outcomes from a diagnostic device.

4. Adjudication Method for the Test Set

• Not applicable as this was not an expert-based diagnostic study. The "adjudication" would involve comparing measured electrical output parameters against predefined engineering specifications.

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

• No, an MRMC comparative effectiveness study was not performed, nor was it considered necessary. The device is an electrosurgical unit, not an AI-assisted diagnostic or decision-making system where human reader improvement with AI would be relevant.

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

• This concept is not directly applicable in the context of an electrosurgical unit's performance evaluation as described. The software (version 3.1) enabling remote control was validated for its functionality (standalone as software, and integrated with the da Vinci system). The performance is inherent to the machine's operation, not an algorithm providing interpretations that a human might then use or act upon.

7. Type of Ground Truth Used

• The "ground truth" implicitly used for this device's performance validation is engineering specifications and verified electrical output parameters. For example, the BiClamp-Mode's output parameters were measured and compared against its specified performance and the performance of the predicate device (ERBE ESU Model VIO 300 D's specifications).

8. Sample Size for the Training Set

• Not applicable. This is not an AI/ML device that "learns" from a training set. The software is deterministic and was developed and validated through traditional software engineering paradigms.

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

• Not applicable, as there is no training set for this type of device.

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The ERBE ESU Model VIO dV with Accessories is intended to deliver High Frequency (HF) electrical current for the cutting and/or coagulation of tissue.

The ERBE ESU Model VIO dV with Accessories is an ElectroSurgical Unit that generates High Frequency (HF) electrical current to cut and/or coagulate tissue. It is a stand-alone generator. The ESU has five clearly defined Cutting and Coagulation Modes with different electrical waveforms and electrical parameters, which are programmed with defined Effect levels. Each Effect level corresponds to a specific voltage. The Modes provide the physician flexibility in interventional applications. Thus the Unit may be used for a broad array of surgical applications. The ESU user interface consists primarily of a touchscreen surrounded by a small number of physical controls, such as a power switch and connection points for the instruments and accessories with which the generator is compatible. Various hand instruments and neutral electrodes from ERBE and different manufacturers may be attached to and operated by the generator. The standard Accessories for the ESU consist of reusable Footswitches as well as Monopolar and Bipolar Cables.

The provided document describes a Special 510(k) for an updated electrosurgical unit (ESU), the ERBE ESU Model VIO dV with Accessories. This type of submission relies on demonstrating substantial equivalence to a legally marketed predicate device, rather than requiring extensive clinical trials with specific acceptance criteria and performance metrics for an AI algorithm.

Therefore, many of the requested elements regarding acceptance criteria for device performance, sample sizes for test and training sets, expert involvement, and ground truth establishment are not applicable to this type of device submission. The focus here is on design control, verification, and validation against recognized consensus standards, and demonstrating that the modifications do not raise new questions of safety or effectiveness.

Here's a breakdown of the available information:

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

• Not applicable in the context of a 510(k) for an ESU based on substantial equivalence. The submission focuses on demonstrating that the modified device's performance, as verified through design controls and testing to recognized consensus standards, is equivalent to the predicate device. Specific numerical "acceptance criteria" and "reported device performance" in terms of clinical accuracy metrics (like sensitivity, specificity, F1-score for AI) are not provided nor typically required for this type of medical device.
• The document states: "The ERBE ESU Model VIO dV with Accessories was tested to FDA's "Recognized Consensus Standards"." This implies adherence to established engineering and safety standards relevant to ESUs, which serve as the "acceptance criteria" for the device's technical functionality and safety.

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 information is relevant for studies involving data-driven algorithms (like AI/ML) where test sets are used to evaluate model performance on unseen data. This document describes a hardware device (electrosurgical unit), not a data-driven algorithm requiring a test set in that sense. Testing would involve engineering verification and validation on physical units to ensure compliance with standards.

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 is typically used for AI/ML performance evaluation. For an ESU, the "truth" is its ability to safely and effectively cut and coagulate tissue as specified by engineering standards, verified through testing, not through expert-labeled data.

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

• Not applicable. Adjudication methods are used in studies where multiple human readers or algorithms produce outputs that need to be resolved to establish a definitive ground truth. This is not relevant to the verification and validation of an ESU's hardware and software against engineering 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

• Not applicable. MRMC studies are used to evaluate the impact of AI assistance on human reader performance, typically in diagnostic imaging. This device is an electrosurgical unit, not an AI diagnostic tool.

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

• Not applicable. This question pertains to the performance of an AI algorithm independently. The device described is a medical instrument (ESU) that operates under human control, not an autonomous AI algorithm.

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

• Not applicable in the AI/ML context. For this device, the "ground truth" for verification and validation would be adherence to recognized consensus standards for electrical safety, electromagnetic compatibility, performance characteristics (e.g., power output, voltage curves for different modes), and functional specifications. This is established through objective measurements and compliance with regulatory standards.

8. The sample size for the training set

• Not applicable. This information is relevant for machine learning algorithms. The ESU is a hardware device with programmed functionalities, not a system that is "trained" on a dataset in the AI sense.

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

• Not applicable. As above, the concept of a "training set" and "ground truth" for it is not relevant to this ESU's design and approval process.

Summary of Device Acceptance / Study Information from the Document:

• Acceptance Criteria Basis: The primary "acceptance criteria" for this Special 510(k) submission are based on demonstrating substantial equivalence to the predicate device (ERBE ESU Model VIO 300 D) and compliance with FDA's "Recognized Consensus Standards."
• Study Proving Acceptance: The study conducted was a design control process involving:
  • Design and development planning: (Page 3)
  • Design input: (Page 3)
  • Design review: (Page 3)
  • Design verification/design output: (Page 3)
  • Design validation: (Page 3)
  • Design transfer: (Page 3)
  • Design change control: (Page 3)
• Specific evidence of meeting acceptance criteria:
  • "The ERBE ESU Model VIO dV with Accessories has been verified or validated in design control." (Page 3)
  • "The ERBE ESU Model VIO dV with Accessories was tested to FDA's "Recognized Consensus Standards"." (Page 3)
  • The conclusion explicitly states: "The ERBE ESU Model VIO dV with Accessories has the same intended use, principles of operation and technological characteristics as the predicate device... The subject device has been verified or validated in design control. In conclusion, there are no issues with the subject device that would raise additional safety or efficacy issues, when compared to the predicate devices." (Page 3)
• Nature of Testing: The document indicates that animal or clinical performance testing was not considered necessary (Page 3), reinforcing that the acceptance was based on engineering verification, validation, and standard compliance, demonstrating equivalence to a lawfully marketed predicate device.

In essence, the "study" for this device was its robust design and development process, including verification and validation activities conducted according to established procedures and adherence to recognized consensus standards, which collectively demonstrated its safety and effectiveness as being substantially equivalent to the predicate device.

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