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The Brainlab Navigation System - Microscope Navigation Software module, that when used with a Brainlab navigation system and compatible instrument accessories, is intended as image guided planning and navigation system to enable open and minimally invasive surgery.
It links an instrument and the view of the surgical field (e.g., video, view through surgical microscope) to a virtual computer image space on patient image data being processed by the navigation. The system is indicated for any medical condition in which a reference to a rigid anatomical structure can be identified relative to images (CT, CTA, X-Ray, MR, MRA and ultrasound) of the anatomy.

The Microscope Navigation Software (also referred to as subject device or Microscope App) is a software. It runs on a Brainlab navigation system consisting of a computer, a display and an IR tracking camera (referred to as platform) and a Brainlab Image Guided Surgery software (referred to as IGS software).
The Microscope Navigation encapsulates microscope specific functionality and separates it from the IGS software.
The device interfaces with the IGS software and e.g. utilizes the registration provided by the IGS software.
The device assists surgeries where a surgical microscope is used. It provides information based on the field of view through the microscope, the microscope position relative to the patient and the medical imaging data of the patient.
The subject device provides functionality to verify and correct a patient registration. The Microscope Navigation does not provide its own registration.

The provided text does not contain information about acceptance criteria and a study that proves the device meets those criteria in the typical sense of a clinical or standalone performance study.

The document is a 510(k) summary for the "Microscope Navigation" software, which is a component of a larger Brainlab navigation system. The submission is a Special 510(k), indicating that the changes made (re-implementing motorized movement functionality that was already present in an earlier predicate device, K082060 VECTORVISION CRANIAL) do not significantly alter the device's fundamental technology or indications for use.

Instead of a traditional clinical performance study, the verification and validation for this submission focus on demonstrating that the re-implemented features work as expected and maintain substantial equivalence to the predicate device.

Here's a breakdown of the requested information based on the provided text, highlighting what is present and what is absent:

1. Table of Acceptance Criteria and Reported Device Performance

The document provides a "Verification summary" and "Validation summary" which describe tests performed and their results, but it does not explicitly state quantitative acceptance criteria for performance metrics like accuracy, sensitivity, or specificity. The acceptance criteria described are qualitative ("All tests passed") and relate to functional and safety aspects.

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

• Test Set Sample Size: Not explicitly stated. The verification and validation summaries suggest testing of the software's functionality and usability rather than a dataset of patient cases.
• Data Provenance: Not applicable in the context of this type of software verification. There are no patient data sets mentioned for testing. The validation was performed "with software and equipment that are identical or equivalent to the final version of the product," indicating lab-based testing.

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

• Number of Experts: Not specified. The document refers to "usability tests," which typically involve target users, but their number and specific qualifications are not detailed beyond being users of a surgical microscope navigation system.
• Qualifications of Experts: Not specified.

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

• Adjudication Method: Not applicable. The tests described are functional verification and usability validation, not diagnostic performance studies requiring human expert adjudication of medical images.

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

• MRMC Study: No, an MRMC comparative effectiveness study was not done. This device is a microscope navigation software, not an AI-assisted diagnostic tool that aids human readers in interpreting medical images. Its purpose is to guide surgeons using a microscope by linking instruments and the surgical field view to patient image data.

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

• Standalone Performance: The verification tests the algorithm's functional aspects (safety measures, motorized movement UI, communication protocols) in a standalone manner (i.e., the software itself is tested without a human actively interpreting results in a diagnostic sense). However, its ultimate intended use is within a human-in-the-loop surgical navigation system. There isn't a "standalone performance" metric provided that would typically correspond to diagnostic algorithm performance.

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

• Type of Ground Truth: The concept of "ground truth" as typically defined for diagnostic AI (e.g., pathology, expert consensus) is not directly applicable here. The "ground truth" for these tests would be the correct functional behavior of the software and hardware according to specifications (e.g., a motorized movement correctly moves a certain distance, communication protocols correctly transmit data). The usability tests would have "ground truth" as the expected successful and safe interaction of users with the system.

8. The sample size for the training set

• Training Set Sample Size: Not applicable. This document describes a software module for a navigation system, not a machine learning or AI model that requires a training set of data.

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

• Ground Truth for Training Set: Not applicable, as there is no training set mentioned for this device.

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The Brainlab Navigation System - Microscope Navigation Software module, that when used with a Brainlab navigation system and compatible instrument accessories, is intended as image guided planning and navigation system to enable open and minimally invasive surgery.

It links an instrument and the view of the surgical field (e.g. video, view through surgical microscope) to a virtual computer image space on patient mage data being processed by the navigation. The system is indicated for any medical condition in which a reference to a rigid anatomical structure can be identified relative to images (CT, CTA, X-Ray, MR, MRA and ultrasound) of the anatomy

The Microscope Navigation Software (also referred to as subject device or Microscope App) is a software. It runs on a Brainlab navigation system consisting of a computer, a display and an IR tracking camera (referred to as platform) and a Brainlab Image Guided Surgery software (referred to as IGS software).
The Microscope Navigation encapsulates microscope specific functionality and separates it from the IGS software.
The device interfaces with the IGS software and e.g. utilizes the registration provided by the IGS software.
The device assists surgeries where a surgical microscope is used. It provides information based on the field of view through the microscope, the microscope position relative to the patient and the medical imaging data of the patient.
The subject device provides functionality to verify and correct a patient registration. The Microscope Navigation does not provide its own registration.

Here is a detailed breakdown of the acceptance criteria and study information for the "Microscope Navigation Software":

Acceptance Criteria and Device Performance

Study Details

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

   • Test Set Sample Size: Not explicitly stated as a number of patients or cases. The tests were performed "on a phantom."
   • Data Provenance: The studies were non-clinical, utilizing phantoms. The origin of the phantom or simulated data is not specified beyond being "non-clinical data according to Brainlab procedures."

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

   • Number of Experts: Not applicable. The ground truth was established through physical measurements on a phantom, not by expert consensus on clinical cases.
   • Qualifications of Experts: Not applicable.

3. Adjudication method for the test set:

   • Adjudication Method: Not applicable. The "ground truth" was established through direct measurement against a known physical standard (phantom landmarks) using the IGS system, not through human interpretation requiring adjudication.

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 MRMC comparative effectiveness study was performed or reported. This device is a navigation software for surgical microscopes and does not involve "human readers" in the sense of interpreting medical images like an AI diagnostic tool would. It assists surgeons during procedures.

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

   • Yes, the verification tests described for "Microscope focus point accuracy" and "Navigation Update" are standalone performance tests of the algorithm/device's accuracy on a phantom. The "Microscope Navigation Software" (the subject device) is a software module that runs on a Brainlab navigation system and operates objectively to determine accuracy in these tests.

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

   • The ground truth for the verification tests was established through physical measurements on a phantom with known landmarks and dimensions, as measured by the existing IGS System or external measurement tools.

7. The sample size for the training set:

   • The document does not provide a sample size for a training set. This is not an AI/ML-based diagnostic device that typically requires a large training dataset for model development. It's a software for image-guided navigation based on established principles of tracking and image registration.

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

   • Not applicable, as no training set for an AI/ML model is mentioned. The device's functionality is based on known physical principles and software algorithms, not trained on a dataset in the manner of machine learning.

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