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BrainLAB knee is intended to be an intraoperative image guided localization system to enable minimally invasive surgery. It links a freehand probe, tracked by a passive marker sensor system to virtual computer image space on an individual 3D-model of the patient's bone, which is generated through acquiring multiple landmarks on the bone surface. The system is indicated for any medical condition in which the use of stereotactic surgery may be appropriate and where a reference to a rigid anatomical structure, such as the skull, a long bone, or vertebra, can be identified relative to a CT, x-ray, MR-based model of the anatomy. The system aids the surgeon to accurately navigate a knee prosthesis to the intraoperatively planned position. Ligament balancing and measurements of bone alignment are provided by BrainLAB knee.

Example orthopedic surgical procedures include but are not limited to:

• · Total Knee Replacement
• · Ligament Balancing
• Range of Motion Analysis
• · Patella Tracking

BrainLAB knee is intended to enable operational planning and/navigation in orthopedic surgery. It links a surqical instrument, tracked by flexible passive markers to virtual computer image space on an individual 3D-model of the patient's bone, which is gegerated through acquiring multiple landmarks on the bone surface. BrainLAB knee uses the registered landmarks to navigate the femoral and tibial cutting guides and the implant to the planned optimally position.

BrainLAB knee allows 3-dimensional reconstruction of the mechanical axis and alignment of the implants. BrainLAB knee software registers the, patient data needed for planning and navigating the surgery intraoperatively. No preoperative CT-scanning is necessary.

The provided text does not contain specific acceptance criteria or a detailed study proving the device meets acceptance criteria. The document is a 510(k) summary, which focuses on demonstrating substantial equivalence to predicate devices rather than providing detailed performance data against specific acceptance criteria.

Therefore, most of the requested information cannot be extracted from the given text.

Here's what can be stated based on the provided text:

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

• Acceptance Criteria: Not specified in the provided text.
• Reported Device Performance: Not detailed in the provided text as specific quantitative performance metrics. The document broadly states that "The validation proves the safety and effectiveness of the information provided by BrainLAB in this 510 (k) application."

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

• Not specified in the provided text.

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):

• Not specified in the provided text.

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

• Not specified in the provided text.

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:

• Not specified in the provided text. The device is a surgical navigation system, not an AI-assisted diagnostic device, so an MRMC study with "human readers" is unlikely to be relevant in this context.

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

• The device is described as an "intraoperative image guided localization system" that "aids the surgeon." This implies a human-in-the-loop system, so a standalone algorithm-only performance study as typically understood for diagnostic AI might not be directly applicable or detailed here. The text does not provide information about such a study.

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

• Not specified in the provided text. The system generates a "3D-model of the patient's bone, which is generated through acquiring multiple landmarks on the bone surface." Performance would likely be evaluated against the accuracy of navigation to planned positions, but the method for establishing this "ground truth" for validation is not described.

8. The sample size for the training set:

• Not specified in the provided text. The device relies on an "individual 3D-model of the patient's bone" generated intraoperatively, rather than a pre-trained AI model in the typical sense that would require a "training set."

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

• Not applicable/Not specified. As noted in point 8, the system's operation doesn't suggest a "training set" in the context of machine learning model development.

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