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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 for the VectorVision® hip SR device and does not contain detailed information about specific acceptance criteria, study methodologies, or performance metrics in a structured experimental study. The document primarily focuses on establishing substantial equivalence to predicate devices and detailing the device's intended use and description.

Therefore, many of the requested fields cannot be directly extracted from the provided text. However, I can infer some general information about the validation process as stated in the document.

Here's an attempt to answer your questions based on the available information:

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

The document states: "VectorVision® hip SR has been verified and validated according to the BrainLAB procedures for product design and development. 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 devices Vector Vision® Hip 3.0 (K 040368) and Vector Vision® Hip SR 1.0 (K 063028)."

This indicates that the acceptance criteria are tied to demonstrating substantial equivalence to its predicate devices. However, specific quantitative acceptance criteria (e.g., accuracy thresholds, precision targets) and reported performance metrics against those criteria are not detailed in this summary. The summary implies that the device meets the functional and safety requirements comparable to its predicates.

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 510(k) summary. The document does not specify any particular test set size or data provenance for performance 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)

This information is not provided in the 510(k) summary. The document does not describe the establishment of a ground truth for a test set.

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

This information is not provided in the 510(k) summary.

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 document describes a surgical navigation system, not an AI-based diagnostic or analysis tool that would typically involve human "readers" or an MRMC study comparing AI assistance. Therefore, an MRMC study as described is not applicable/not mentioned in this context.

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

The device is an "intraoperative image-guided localization system" designed to "aid the surgeon." This implies a human-in-the-loop system where the surgeon uses the device for navigation. A standalone algorithm-only performance assessment in the context of clinical outcomes is not described or implied in this document. The system's performance is inherently tied to its use by a surgeon during a procedure.

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

The document does not explicitly state the type of ground truth used for validation. Given that it's a surgical navigation system, the "ground truth" for its performance validation would likely relate to the accuracy of instrument positioning relative to planned targets or anatomical landmarks, possibly verified through intraoperative measurements or post-operative imaging, or phantom studies. However, the specific methodology is not detailed.

8. The sample size for the training set

This information is not provided in the 510(k) summary. The document describes a navigation system that generates a 3D model from "acquiring multiple landmarks on the bone surface" rather than a system extensively trained on a large dataset in the sense of modern machine learning.

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

This information is not provided in the 510(k) summary. As mentioned for #8, the concept of a "training set" in the context of recent AI/ML devices might not directly apply here, as the system relies on intraoperative landmark acquisition and established geometric principles for navigation rather than large-scale data training to learn patterns. The "ground truth" for the system's underlying algorithms and models would have been established through engineering principles, calibration, and geometry, rather than an external "training set" of patient data.

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