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The Zeta Cranial Navigation System is a stereotaxic image guidance system intended for the spatial positioning and orientation of neurosurgical instruments used by surgeons. The device is indicated only for cranial surgery where reference to a rigid anatomical structure can be identified, does not require rigid fixation of the patient, and does not require fixation of a navigated instrument guide to the patient. The system is intended to be used in operating rooms and in less acute surgical settings such as interventional procedure suites.

The Zeta Cranial Navigation System is a stereotaxic, image guided planning and intraoperative guidance system enabling computer-assisted cranial interventional procedures. The system assists surgeons with the precise positioning of surgical instruments relative to patient anatomy by displaying the position of navigated surgical instruments relative to 3D preoperative medical scans.

Here's a breakdown of the acceptance criteria and study information for the Zeta Cranial Navigation System, based on the provided document:

1. Table of Acceptance Criteria and Reported Device Performance

The document focuses on demonstrating substantial equivalence to a predicate device (Zeta Cranial Navigation System, K233903) rather than explicitly stating quantitative performance acceptance criteria in an easily digestible table. However, it implicitly uses various testing results to show the device meets safety and effectiveness requirements.

Below is an attempted table based on the provided information, where "Acceptance Criteria" are implied by successful test completion and "Reported Device Performance" is the outcome of those tests.

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

The document does not specify a separate "test set" in the context of image data or patient studies for the subject device. The performance data provided is primarily from bench testing, electrical safety, software validation, and human factors testing.

Specifically, for accuracy testing, it mentions:

• "Simulated clinical procedures using virtual targets"
• "Surgical illumination levels"
• "Dynamic patient motion"
• "Other worst-case physical and environmental conditions"

However, the document does not provide details on:

• The sample size of virtual targets, simulated procedures, or specific cases used for accuracy testing.
• The provenance of any data (e.g., country of origin, retrospective/prospective) for these bench tests.

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 document describes bench testing and simulated environments rather than expert-adjudicated clinical data sets.

4. Adjudication method for the test set

This information is not provided as the testing described does not involve expert adjudication of a test set. The accuracy testing was likely measured against pre-defined virtual targets or established physical ground truths in a bench setting.

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

There is no mention of a multi-reader multi-case (MRMC) comparative effectiveness study being done, nor any data regarding human reader improvement with AI assistance. The Zeta Cranial Navigation System is described as an image guidance system for surgeons, not an AI-assisted diagnostic or interpretative tool for human readers of medical images.

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

The document does not explicitly state "standalone performance" in the context of an algorithm. However, the accuracy testing and other bench tests (e.g., electrical safety, software validation) would inherently assess the device's functional performance independent of human interaction beyond operating the device. The "Navigation Frames Per Second" (mean 21fps) is a standalone performance metric for the system's real-time tracking capability.

7. The type of ground truth used

For the accuracy testing, the ground truth appears to be:

• Virtual targets: For simulated clinical procedures.
• Established physical ground truths: For accuracy measurements under various physical and environmental conditions.

The document does not mention pathology, outcomes data, or expert consensus as ground truth for the performance evaluations described.

8. The sample size for the training set

The document does not provide any information regarding a training set or its sample size. This is consistent with the device being primarily a navigation system, where the software's role is real-time tracking and display based on established principles, rather than a machine learning model that requires extensive training data for image interpretation or diagnosis.

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

As no training set is mentioned, this information is not provided.

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The Zeta Cranial Navigation System is a stereotaxic image guidance system intended for the spatial positioning and orientation of neurosurgical instruments used by surgeons. The device is indicated only for cranial surgery where reference to a rigid anatomical structure can be identified, does not require rigid fixation of the patient, and does not require fixation of a navigated instrument guide to the patient. The system is intended to be used in operating rooms and in less acute surgical settings such as interventional procedure suites.

The Zeta Cranial Navigation System is a stereotaxic, image guided planning and intraoperative guidance system enabling computer-assisted cranial interventional procedures. The system assists surgeons with the precise positioning of surgical instruments relative to patient anatomy by displaying the position of navigated surgical instruments relative to 3D preoperative medical scans.

The provided FDA 510(k) summary for the Zeta Cranial Navigation System (K233903) describes its performance data and the studies conducted to demonstrate substantial equivalence to its predicate device (K230661). However, it does not contain the specific statistical acceptance criteria or detailed results of a comparative effectiveness study (like an MRMC study) for human readers, or a standalone AI performance study.

The document primarily focuses on technical performance testing (accuracy, electrical safety, EMC, software V&V, and human factors) rather than clinical performance or AI algorithm-specific metrics.

Based on the provided text, here's what can be extracted and what information is not available:

Acceptance Criteria and Reported Device Performance

The document states that "The device passed all tests" for electrical safety, EMC, and software verification and validation. For bench testing, it indicates "Accuracy testing under different conditions," but does not specify the quantitative acceptance criteria for "accuracy" or the achieved performance values.

Table 1: Acceptance Criteria and Reported Device Performance (as inferred and stated)

Study Details

The document details various types of testing, but does not describe a study involving human readers or an AI algorithm in the way typically associated with diagnostic AI tools (e.g., for image interpretation). The "Zeta Cranial Navigation System" is a stereotaxic image guidance system, where the "AI" or "machine vision" component likely refers to the system's ability to perform automatic, pinless, and markerless patient registration and instrument tracking, rather than an AI that interprets medical images for diagnostic purposes.

Therefore, many of the questions below related to AI study specifics (training sets, ground truth methodology for AI, MRMC studies) are not applicable (N/A) to the information provided in this 510(k) summary, as it does not describe such a study.

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

   • N/A (for clinical AI performance evaluation): The document mentions "accuracy testing under different conditions" including "simulated clinical procedures using virtual targets" and "dynamic patient motion," but it does not specify a "test set" in the context of patient data or clinical images for an AI diagnostic algorithm. The testing described is bench testing.
   • Data Provenance: The nature of the "virtual targets" and "simulated clinical procedures" means the data is synthetically generated or simulated in a lab setting, not from clinical patients.

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

   • N/A: As this is bench testing of a navigation system's mechanical and software accuracy (e.g., tracking instruments relative to a known position), the "ground truth" is established through engineering and metrology standards rather than expert medical interpretations.

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

   • N/A: Not relevant for the type of bench testing described.

4. 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: The document explicitly states "Clinical Data: Not applicable. Clinical studies are not necessary to establish the substantial equivalence of this device." This confirms that an MRMC study comparing human performance with and without AI assistance was not performed or deemed necessary for this 510(k) submission.

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

   • Yes (for specific functions): The document describes "Accuracy testing" and "Software Verification and Validation." The system's intrinsic functions like "automatic, pinless, and markerless" registration and "optical tracking" of instruments would have been tested in a standalone manner to demonstrate their performance against defined metrics. However, "standalone" in this context refers to the system's technical performance characteristics, not an AI interpreting medical images.

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

   • Engineering/Metrology Standards: For accuracy testing of a navigation system, ground truth is typically established through precise measurement devices (e.g., coordinate measuring machines, high-precision optical tracking systems) and simulated conditions. It's not based on medical expert consensus or pathological findings.

7. The sample size for the training set:

   • N/A: The document does not describe an AI model that requires a "training set" in the context of machine learning for diagnostic image analysis. The "machine vision" and "structured light" technologies employed are likely more akin to traditional computer vision algorithms for feature recognition and localization, which might be "calibrated" or "tuned" but not typically "trained" on large datasets in the way a deep learning model would be.

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

   • N/A: See point 7.

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