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BrainLAB's VectorVision® hip SR is intended as an intraoperative image-guided localization system. It links a freehand probe, tracked by a passive marker sensor system to virtual computer image space on a VectorVision® navigation station. The image data is provided either in the form of preoperatively-acquired patient images or in the form of an individual 3D model of the patient's bone, which is generated by acquiring multiple landmarks on the bone surface. The system is indicated for any medical condition in which the use of stereotactic surgery may be considered to 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, or MR-based model of the anatomy. The system aids the surgeon in accurately navigating a hip endoprothesis to the preoperatively or intraoperatively planned position.

Example orthopedic surgical procedures include but are not limited to:

· Partial/hemi-hip resurfacing

BrainLAB's VectorVision® hip SR is intended to enable operational planning and navigation in orthopedic hemi resurfacing surgery. It links a surgical instrument, tracked by flexible passive markers 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. VectorVision® hip SR uses the registered landmarks to navigate the initial pin insertion into the femur with a pre-calibrated drillguide to the planned position.

VectorVision® hip SR allows 3-dimensional reconstruction of the relevant anatomical axes and planes of the femur and alignment of the implants. The VectorVision® hip SR software has been designed to read in data of implants and tools if provided by the implant manufacturer and offers to individually choose the prosthesis during each surgery. If no implant data is available it is possible to provide information in order to achieve a generally targeted alignment relative to the bone orientation as defined by the operating surgeon. The VectorVision® hip SR software registers the patient data needed for planning and navigating the surgery intraoperatively without CT-based imaging. The system can be used to generally align tool orientations according to the anatomy described and defined by the landmarks acquired by the surgeon.

The provided document is a 510(k) Summary of Safety and Effectiveness for the BrainLAB VectorVision® hip SR device. It details the device's intended use, description, and states that it has been verified and validated according to BrainLAB's procedures for product design and development, proving its safety and effectiveness. However, the document does not contain explicit acceptance criteria or a detailed study report with performance metrics, sample sizes, ground truth establishment, or expert qualifications as requested. It primarily focuses on demonstrating substantial equivalence to predicate devices (VectorVision® hip K040368 and VectorVision® osteotomy K042513) for regulatory clearance.

Therefore, much of the requested information cannot be extracted from this document.

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

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

• Not available in the document. The document states: "The validation proves the safety and effectiveness of the information provided by BrainLAB in this 510 (k) application was found to be substantially equivalent with the predicate device Vector Vision® hip (K 040368) and Vector Vision® osteotomy (K042513)." This indicates that validation was performed, but the specific acceptance criteria and detailed performance metrics (e.g., accuracy, precision) are not included in this summary.

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

• Not available in the document. The document does not provide details on sample sizes for any test sets or the provenance of data used for validation.

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 available in the document. The document does not describe the establishment of ground truth for any test sets or the involvement or qualifications of experts.

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

• Not available in the document. The document does not mention any adjudication methods.

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 available in the document. This document describes an image-guided navigation system for surgery, not an AI-assisted diagnostic device typically evaluated with MRMC studies. There is no mention of human reader studies or AI assistance for diagnostic interpretation.

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

• The device itself is an "intraoperative image-guided localization system" that aids a surgeon. While the "algorithm only" performance (e.g., system accuracy) would be part of its validation, the document does not provide details or results of such a standalone performance study. It only states that the device was "verified and validated."

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

• Not available in the document. The document does not specify the type of ground truth used for any validation. Given it's a navigation system, ground truth would likely relate to accuracy of tool positioning relative to planned positions or anatomical landmarks, but this is not detailed.

8. The sample size for the training set

• This device is described as an image-guided surgery system that uses either "preoperatively-acquired patient images" or an "individual 3D model... generated by acquiring multiple landmarks on the bone surface." It does not explicitly describe a machine learning model that would require a "training set" in the conventional sense for deep learning. If there are underlying algorithms, the training set size for those is not available in the document.

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

• As a "training set" is not explicitly mentioned in the context of machine learning, the establishment of its ground truth is not available in the document. If this refers to the data used to develop the system's underlying algorithms, those details are not provided.

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