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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 only indicated 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 document, a 510(k) Premarket Notification for the Zeta Cranial Navigation System, focuses on demonstrating substantial equivalence to a predicate device rather than providing a detailed study proving the device meets specific acceptance criteria with quantifiable performance metrics.

However, based on the information provided, we can infer the types of performance aspects tested and the general conclusion of the submitter.

Here's an attempt to structure the information based on your request, highlighting where the document provides details and where it lacks them for a comprehensive answer:

1. Table of Acceptance Criteria and Reported Device Performance

The document does not explicitly present a table of acceptance criteria with corresponding performance numbers. Instead, it describes general testing categories. We can infer the "acceptance criteria" were implicit in passing these tests.

Important Note: The document states "The device passed all tests" for electrical safety, EMC, software, and human factors. It also states that accuracy testing was performed and implies successful demonstration of accuracy under various conditions. However, specific quantifiable acceptance thresholds or detailed results (e.g., actual accuracy measurements in mm) are not provided in this summary.

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

• Sample Size for Test Set: Not explicitly stated. The "Bench Testing" section mentions "Simulated clinical procedures using virtual targets" and testing under "Dynamic patient motion" and "Other worst-case physical and environmental conditions," but does not provide the number of simulations, patients, or instances.
• Data Provenance: The nature of the testing (bench testing, simulations) suggests a controlled laboratory environment. The country of origin for the data is not specified, but the applicant (Zeta Surgical Inc.) is based in Boston, Massachusetts, USA. The testing appears to be retrospective as it was conducted to support the 510(k) submission.

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 testing described is primarily technical bench testing and simulations, and does not appear to involve human expert ground truth for interpretation of medical images or surgical outcomes in the way a clinical study would.

4. Adjudication method for the test set

This information is not provided. Given that the testing focuses on technical performance and simulations, an adjudication method in the context of expert review (e.g., 2+1, 3+1) is unlikely to be applicable or described for these types of tests.

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

• Was an MRMC study done? No. The document explicitly states under "Clinical Data": "Not applicable. Clinical studies are not necessary to establish the substantial equivalence of this device."
• Effect Size: Not applicable, as no MRMC study was conducted.

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

The device is a "stereotaxic image guidance system intended for the spatial positioning and orientation of neurosurgical instruments used by surgeons." It is an intraoperative guidance system that assists surgeons. Therefore, its performance is inherently tied to human use (human-in-the-loop). While its component technologies (like tracking accuracy or latency) are tested in a standalone manner, the overall device function is not a standalone "algorithm only" product in the sense of an AI diagnostic tool. The performance metrics provided (maximum tracking speed, effective navigation latency) are for the system's technical operation.

7. The type of ground truth used

For the bench tests, the ground truth would be:

• Engineering/Physical Measurements: Precision instruments and known physical parameters (e.g., for accuracy testing against virtual targets, illumination levels, simulated motion).
• Standard Compliance: Adherence to the requirements of the referenced IEC and other standards (e.g., for electrical safety, EMC, software, human factors).

8. The sample size for the training set

• Not Applicable / Not Provided. This device is a navigation system, not a machine learning model that would typically have a "training set" for image interpretation or diagnosis. It uses pre-operative 3D CT/MRI scans and machine vision for patient registration and instrument tracking, but the document does not describe a separate machine learning training phase for its core image guidance functionality. The "machine vision" mentioned for registration technology typically refers to computer vision algorithms, but not necessarily a "deep learning" or "AI" model trained on a large dataset in the way a diagnostic AI would be.

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

• Not Applicable / Not Provided. (See point 8).

In summary, the 510(k) submission focuses on demonstrating the technical performance and safety of the device through bench testing and compliance with recognized standards, arguing for substantial equivalence to a predicate device, rather than providing a clinical effectiveness study with human readers or AI performance metrics.

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