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The device is intended for the spatial positioning and orientation of instruments holders or tool guides to be used by trained neurosurgeons to guide standard neurosurgical instruments (biopsy needle, stimulation or recording electrode, endoscope). The device is indicated for any neurosurgical procedure in which the use of stereotactic neurosurgery may be appropriate.

The ROSA One Brain application device is a robotized image-guided device that assists the surgeon during brain surgeries. lt provides guidance of any surgical instruments compatible with the diameter of the adaptors supplied by Medtech. It allows the user to plan the position of instruments or implants on medical images and provides stable, accurate and reproducible guidance in accordance with the planning. The device is composed of a robot stand with a compact robotic arm and a touch screen. Different types of instruments may be attached to the robot arm and changed according to the intended surgical procedure. For Brain applications, these neurosurgical instruments (e.g. biopsy needle, stimulation or recording electrode, endoscope) remain applicable for a variety of procedures as shown below in Figure 1 for the placement of recording electrodes. The touchscreen ensures the communication between the device and its user by indicating the actions to be performed with respect to the procedure. Adequate guidance of instruments is obtained from three-dimensional calculations performed from desired surgical planning parameters and registration of spatial position of the patient.

The provided text describes the 510(k) summary for the ROSA ONE Brain Application (K214065), a robotized image-guided device assisting in brain surgeries. The document highlights the substantial equivalence of the new version (v.3.1.6.0) to its predicate device (v.3.1.3.2) (K200511).

However, the provided document does not contain information about acceptance criteria or a study that proves the device meets specific performance criteria beyond general safety and equivalence. Instead, it refers to performance testing completed for the predicate device and states that the subject device was evaluated against that predicate testing and determined to be "substantially equivalent."

Therefore, I cannot provide a table of acceptance criteria or details about a study proving the device meets those criteria directly from the provided text, as the document explicitly states: "All validation was performed based on non-clinical performance tests." and "Clinical data were not required to support the safety and effectiveness of ROSA ONE Brain application."

Based on the provided document, here's what can be inferred and what cannot:

1. Table of Acceptance Criteria and Reported Device Performance

The document presents the following performance data as part of the summary, largely relying on the predicate device's testing and then asserting equivalence for the new device. It does not explicitly state "acceptance criteria" but rather "results" from testing.

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

• Test Set Sample Size: The document does not specify a quantitative "sample size" for a test set in the traditional sense of a clinical or human-in-the-loop study. The performance data presented are primarily from bench testing and evaluations related to software, electrical safety, biocompatibility, and cleaning/sterilization. These tests are generally conducted on representative units of the device or its components.
• Data Provenance: The document does not explicitly state the country of origin for the data for all tests. It mentions "internal Biomet Medtech/Zimmer robotics procedures" for a system accuracy test. The studies mentioned (biocompatibility, electrical safety, software V&V, cleaning/sterilization, and system accuracy) are non-clinical performance tests. They are retrospective in the sense that they refer to data typically collected during product development and validation phases, rather than prospective clinical trials.

3. Number of Experts and Qualifications for Ground Truth

• Not Applicable: The document describes non-clinical performance tests. There is no mention of human experts being used to establish ground truth for a test set in the context of image interpretation or diagnostic accuracy, as this is a device for surgical guidance, not diagnostic interpretation.

4. Adjudication Method for the Test Set

• Not Applicable: As there's no mention of human experts establishing ground truth for a test set for diagnostic or interpretative purposes, adjudication methods are not relevant here.

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

• No: The document explicitly states: "Clinical data were not required to support the safety and effectiveness of ROSA ONE Brain application." Therefore, no MRMC comparative effectiveness study was performed.

6. Standalone (Algorithm Only) Performance

• Not Applicable in the traditional sense: The device is a robotized surgical guidance system. Its "performance" is inherently tied to its mechanical and software accuracy in positioning. The software verification and validation is a standalone assessment of the software's functionality, but there isn't an "algorithm only without human-in-the-loop performance" study in the way one would assess an AI diagnostic algorithm's standalone performance. The system's output is guidance for a human surgeon.

7. Type of Ground Truth Used

• Engineering/Physical Ground Truth: For the system applicative accuracy, the ground truth would be established by precise measurements using metrology equipment on a bench setup, comparing the device's guided positions against known, true positions or planned trajectories. For other tests (biocompatibility, electrical safety, software, cleaning/sterilization), the "ground truth" is compliance with established international standards and internal protocols.

8. Sample Size for the Training Set

• Not Applicable: The ROSA ONE Brain Application is a robotized surgical guidance system, not an AI/ML model that would typically have a "training set" in the context of machine learning. The software is developed through standard engineering practices (design, coding, unit testing, integration testing, verification, validation) rather than being "trained" on a dataset like a deep learning model.

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

• Not Applicable: As there's no "training set" in the machine learning sense, this question is not relevant based on the provided document. The device's "knowledge" or functionality is engineered and programmed, not learned from a dataset.

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