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BrainLAB VectorVision 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 to assist a surgeon to perform one (open wedge) or two (closed wedge) cuts to achieve a leg angle correction.
Example orthopedic surgical procedures include but are not limited to:
Open wedge osteotomy for the lower limb
Closed wedge osteotomy for the lower limb

BrainLAB VectorVision®Osteotomy is intended to enable 3 dimensional correction planning and navigation for lower limb osteotomies. The SW links a surgical instrument tracked by passive markers to a model of the patient's bone geometry, which is generated by acquiring multiple landmarks on the bone surface. VectorVision® Osteotomy uses the registered landmarks tonavigate the tibial cutting guides to the preplanned position. Leg geometry correction can be tracked continuously until osteosynthesis.

The provided document is a 510(k) summary for the BrainLAB VectorVision® Osteotomy system. This document focuses on establishing substantial equivalence to a predicate device and does not contain detailed acceptance criteria, device performance studies, or the specific information required to complete all parts of your request.

However, based on the information provided, here's what can be extracted and inferred:

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

The document states: "The VectorVision® CT-free knee software calculates all planning values based on the same registered landmark and parameters equally to the VectorVision® osteotomy software. The initial geometry of the registered including in same way. For the knee software the registered leg geometry is used to calculate position and size of the used implants. In the osteotomy software the leg geometry itself is used to create the plan of treatment, as the geometry correction is the task. In summary it can be stated the both applications use the same calculation, th output of the VectorVision® CT-free knee software contains several continuative steps until planning result is completed."

This implies that the acceptance criteria are related to the accuracy and reliability of the planning values, registered landmarks, and leg geometry calculations, which are considered to be equivalent to the predicate device (VectorVision® CT-free knee, K021306). However, specific numerical acceptance criteria (e.g., error margins in mm or degrees) are not provided in this document.

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 provided in the document. The filing is a 510(k) summary for substantial equivalence, which often relies on demonstrating that the new device uses the same fundamental technology and principles as a predicate. It does not detail specific clinical or non-clinical test sets used for validation in the same way a PMA or a full clinical study report would.

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 provided in the document.

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

This information is not provided in the document.

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 provided in the document, as the device described is an image-guided surgery system, not an AI-assisted diagnostic or interpretation tool that would typically involve human "readers." The system assists surgeons with planning and navigation.

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

The document describes the device as an "intraoperative image guided localization system to enable minimally invasive surgery. It links a freehand probe...to virtual computer image space on an individual 3D-model of the patient's bone." This implies a human-in-the-loop system where the surgeon uses the navigation for guidance. There is no information provided to suggest a standalone algorithm-only performance assessment was conducted or is relevant to this device's intended use.

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

This information is not specified in the document. Given the description, ground truth for an image-guided surgery system would typically involve precise measurements of alignment, accuracy of probe localization relative to planned targets (e.g., in a phantom or cadaver study), or intraoperative verification of cut planes. However, the document does not detail how "ground truth" was established for any validation testing.

8. The sample size for the training set:

This information is not provided in the document. The device uses "multiple landmarks on the bone surface" to generate a 3D model, implying it's a model-building and navigation system rather than a machine learning system that requires a "training set" in the typical AI sense.

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

This information is not provided in the document, and as noted above, the concept of a "training set" in the context of this device's description is not clearly applicable.

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