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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. It provides quidance of any surqical 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 touchscreen positioned close the operating table. 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 quidance 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 ROSA ONE Brain application, a robotized image-guided device for neurosurgery. It's an FDA 510(k) submission seeking substantial equivalence to a previously cleared version of the same device. The submission focuses on non-clinical performance data to demonstrate this equivalence.

Here's an analysis of the acceptance criteria and the study that proves the device meets them, based on the provided text:

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

The document doesn't explicitly state "acceptance criteria" for each test in a formal table with pass/fail. However, it does outline the tests performed and the results, implying that the predicate device's performance levels define the acceptance criteria for the new version. The most specific performance metric provided is for accuracy.

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

• System applicative accuracy: The text states "Performance bench Testing in compliance with internal Medtech/Zimmer Biomet robotics procedures." No specific sample size (number of tests or cases) for this in vitro testing is mentioned. The data provenance is internal company testing.
• For other tests (Electrical safety, EMC, Biocompatibility, Cleaning- and Sterilization Validation), the testing was largely performed on the predicate device. The subject device (ROSA ONE Brain application v. 3.1.7.0) was then evaluated against these predicate testing results, implying a comparison or re-evaluation rather than new, extensive testing on a new sample set for clinical endpoints.
• For Software Verification and Validation Testing, testing was performed on the subject device. No specific sample size (number of test cases or runs) is provided.

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 in the document. The studies listed are non-clinical (bench testing, software testing, electrical safety, biocompatibility, cleaning/sterilization), which do not typically involve experts establishing ground truth in the same way clinical studies with image interpretation or patient outcomes do.

4. Adjudication method for the test set

This information is not applicable as the studies are non-clinical performance and engineering tests, not involving human interpretation of data that would require an adjudication method.

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 comparative effectiveness study was done. The document explicitly states: "Clinical data were not required to support the safety and effectiveness of ROSA ONE Brain application. All validation was performed based on non-clinical performance tests." Therefore, there is no information about human reader improvement with or without AI assistance.

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

While the device is a "robotized image-guided device," the performance data presented is for the entire system's accuracy and various engineering aspects. The "Software Verification and Validation Testing" does cover the algorithm's performance within the broader software system. The statement that "Robot arm positioning accuracy <0.75 mm RMS Device applicative accuracy <2mm" can be considered a standalone performance metric for the device's mechanical and software-guided capabilities, demonstrating its ability to meet a precision target independently of a human's final surgical action. However, it's not an "algorithm-only" performance in the sense of an AI diagnostic tool.

7. The type of ground truth used

• System applicative accuracy: The ground truth would be precise, known physical measurements and positions in a controlled bench test environment.
• Electrical safety and EMC: Ground truth is defined by the absolute limits and requirements of the IEC 60601-1 and IEC 60601-1-2 standards.
• Biocompatibility testing: Ground truth is established by the specified biological responses (e.g., cell viability for cytotoxicity, skin reaction for irritation) determined according to ISO 10993-1.
• Software Verification and Validation Testing: Ground truth is defined by the software requirements and design specifications, against which the software's behavior is verified and validated.
• Cleaning- and Sterilization Validation: Ground truth is defined by the absence of viable microorganisms or acceptable residual soil levels, determined according to standards like ISO 17665-1, ISO 17664, ANSI/AAMI ST79, and AAMI TIR 12.

8. The sample size for the training set

This information is not provided and is not applicable to this submission. The device is a robotized stereotaxic instrument, not an AI/ML device that requires a distinct "training set" for model development. The software verification and validation are against pre-defined requirements, not derived from a training dataset.

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

This question is not applicable as there is no mention of a training set for an AI/ML model in this submission.

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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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The device is intended for the spationing 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-quided device that assists the surgeon during brain surgeries. It 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 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 5.1 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 ROSA ONE Brain application and its substantial equivalence to a predicate device. However, it does not include detailed acceptance criteria and a study proving the device meets those criteria in the way typically expected for an AI/ML medical device submission (e.g., performance metrics like sensitivity, specificity, AUC for a diagnostic algorithm).

Instead, this document focuses on demonstrating substantial equivalence based on engineering and quality control tests rather than clinical performance of an AI algorithm making diagnostic or treatment recommendations. The "performance data" section primarily discusses electrical safety, EMC, software verification, and biocompatibility, along with a statement about system applicative accuracy derived from the predicate device's testing.

Given the information provided, here's a breakdown of what is and is not available in the document regarding your request:

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

Based on the document, the primary "performance data" that could be interpreted as a performance criterion is the "System applicative accuracy."

Note: The document explicitly states: "Testing were performed on the predicate device. The subject devices were evaluated against the predicate testing and determined to be substantially equivalent." This implies that the current device is expected to meet these same performance levels, rather than providing new, independent test results for the current device's accuracy.

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

This information is not available in the provided text. The accuracy testing was "in vitro" and performed on the predicate device, not necessarily on a "test set" of clinical cases or data in the context of an AI/ML algorithm.

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 information is not available. The document does not describe the establishment of ground truth by experts in the context of clinical image interpretation or AI performance evaluation. The accuracy testing mentioned is an engineering performance bench test.

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

This information is not available. Adjudication methods are typically relevant for clinical studies involving human readers or expert consensus on clinical data, which is not described here.

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 available. The document explicitly states "Clinical data were not required to support the safety and effectiveness of ROSA ONE Brain application. All validation was performed based on non-clinical performance tests." Therefore, an MRMC comparative effectiveness study was not performed.

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

A standalone performance test in the typical AI/ML sense (e.g., evaluating an algorithm's diagnostic accuracy on images) was not done or at least not described. The "System applicative accuracy" is a standalone test of the robot's physical positioning capabilities, not an AI algorithm's interpretive performance.

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

For the "System applicative accuracy," the ground truth would be precise physical measurements or a known, highly accurate reference system on a test bench. It is not based on expert consensus, pathology, or outcomes data in a clinical sense.

8. The sample size for the training set

This information is not available. The device describes a "robotized image-guided device" that assists surgeons; it does not explicitly mention an AI algorithm that is "trained" on a dataset in the way a diagnostic AI would be. The software verification and validation are for the overall embedded software system, not specifically for an AI model's training.

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

This information is not available, for the same reasons as point 8. No specific AI training set or its ground truth establishment is described in this document.

Summary of the Study that Proves the Device Meets Acceptance Criteria:

The "study" referenced in the document is a series of non-clinical performance tests, primarily conducted on the predicate device, and the new device (ROSA ONE 3.1.3.2) was evaluated for substantial equivalence against these established tests and performance levels.

• System Applicative Accuracy In vitro testing: This was a performance bench test designed to evaluate the physical accuracy of the robotic arm's positioning. The results stated were "<0.75 mm RMS" for robot arm positioning accuracy and "<2mm" for device applicative accuracy. These tests were performed on the predicate device, and the subject device was deemed substantially equivalent. The specific methodology would involve measuring the robot's ability to reach planned targets with precision using specialized measurement tools, but the details of the "Medtech/Zimmer robotics procedures" are not provided.
• Electrical safety and electromagnetic compatibility (EMC): Testing against IEC 60601-1 and IEC 60601-1-2 standards.
• Biocompatibility testing: Evaluation according to ISO 10993-1, including cytotoxicity, sensitization, irritation, and acute systemic toxicity performed on the predicate device.
• Software Verification and Validation Testing: Conducted according to FDA guidance and IEC 62304 standards, with the software designated as "major" concern level. This involved code inspections, unit tests, integration tests, and verification tests against requirements, followed by validation against user needs.
• Cleaning- and Sterilization Validation: Performed according to FDA guidance and ISO/AAMI standards.

In essence, the document confirms that the ROSA ONE Brain application (v.3.1.3.2), as a stereotactic instrument, relies on demonstrating its performance through engineering and quality control tests, showing substantial equivalence to a previously cleared predicate device, rather than a clinical study evaluating an AI's interpretive performance.

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The device is intended for the spatial positioning and orientation of instrument holders or tool guides to be used by 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.

It provides quidance 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 end of 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 5.1 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 ROSA ONE Brain Application device is a robotized image-guided device that assists neurosurgeons during brain surgeries by providing guidance for instruments.

Here's a breakdown of the acceptance criteria and the study that proves the device meets them:

1. Table of Acceptance Criteria and Reported Device Performance

2. Sample Size and Data Provenance

• Test Set Sample Size: Not explicitly stated but inferred to be a series of physical bench tests on the device.
• Data Provenance: The studies were non-clinical performance tests conducted to support the substantial equivalence determination for the ROSA ONE Brain application. The tests are described as "Performance bench Testing in compliance with internal Medtech/Zimmer Biomet robotics procedures." No specific country of origin for the direct test data is mentioned, but Medtech S.A. is based in Montpellier, France.

3. Number of Experts and Qualifications for Ground Truth

This information is not provided in the document. Given that the studies were non-clinical performance tests for engineering specifications, a panel of clinical experts for ground truth establishment, as might be used for diagnostic AI, would likely not be applicable in the same way. The "ground truth" for these tests would be the established engineering specifications and recognized international standards.

4. Adjudication Method

An adjudication method (e.g., 2+1, 3+1) is not applicable as this study involved non-clinical performance and engineering validation tests, not clinical assessment of results by multiple human readers.

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

No, a Multi-Reader Multi-Case (MRMC) comparative effectiveness study was not done. The submission explicitly states: "Clinical data were not required to support the safety and effectiveness of ROSA ONE Brain application. All validation was performed based on non-clinical performance tests." Therefore, there is no effect size reported for human readers improving with AI assistance.

6. Standalone Performance

The performance data presented are for the standalone (algorithm only without human-in-the-loop performance) of the robotic system's accuracy and compliance with various engineering and safety standards. The "System applicative accuracy" directly refers to the device's inherent precision.

7. Type of Ground Truth Used

The ground truth for the non-clinical performance tests was based on:

• Established engineering specifications (e.g., target accuracy metrics).
• Compliance with recognized international standards (e.g., IEC 60601-1, IEC 60601-1-2, ISO 10993-1, IEC 62304, ISO 17665-1, ISO 17664, ANSI/AAMI ST79, AAMI TIR 12).
• Predicate device testing results for biocompatibility, where the subject device was evaluated for substantial equivalence.

8. Sample Size for the Training Set

The document does not specify a separate training set or its sample size. This device is a robotic surgical assistance system, and the accuracy and performance data provided relates to the hardware and software's adherence to engineering specifications and regulatory standards, rather than a machine learning model trained on a dataset. The software validation refers to verification and validation activities according to IEC 62304, which are standard for medical device software development, not necessarily the training of an AI model with a distinct training dataset.

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

As no specific "training set" for a machine learning model is mentioned, the method for establishing its ground truth is not applicable or described in this document. The ground truth for the device's overall performance validation was based on compliance with engineering specifications and regulatory standards as described in point 7.

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