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BrainLAB VectorVision trauma is intended to be a pre- and intraoperative image quided 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 a patient's pre- or intraoperative image data being processed by a VectorVision workstation. 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 bone structure like tubular bones, pelvic, calcaneus and talus, or vertebra, can be identified relative to a CT, fluoroscopic, X-ray or MR based model of the anatomy.

Example procedures include but are not limited to:

Spinal procedures and spinal implant procedures such as pedicle screw placement.

Pelvis and acetabular fracture treatment such as screw placement or illo-sacral screw fixation.

Fracture treatment procedures, such as intramedullary nailing or screwing or external fixation procedures in the tubular bones.

BrainLAB VectorVision trauma is intended to enable operational navigation in spinal, orthopedic and traumatologic surgery. It links a surgical instrument, tracked by flexible passive markers to virtual computer image space on a patient's intraoperative image data being processed by a VectorVision workstation.

VectorVision trauma allows navigation of intraoperatively acquired images considering patient's movement in correlation to calibrated surgical instruments. This allows implant positioning, screw placement and bone reduction in different views and reduces the need for treatments under permanent fluoroscopic radiation.

The provided text is a 510(k) summary for the VectorVision trauma device. It lacks detailed information about specific acceptance criteria and a structured study demonstrating the device's performance against those criteria. The provided text states "VectorVision trauma has been verified and validated according to BrainLAB's procedures for product design and development. The validation proves the safety and effectiveness of the system." This suggests that internal verification and validation studies were conducted, but the specifics are not included in this document.

Therefore, I cannot extract the information required for the table and other detailed questions from the provided text. The document focuses on the regulatory submission and substantial equivalence to a predicate device rather than presenting a performance study report.

If you have a document that details the specific verification and validation study results, I would be able to answer your request.

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BrainLAB VectorVision Trauma is intended to be a pre- and 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 a patient's pre- or intraoperative image data being processed by a VectorVision workstation. 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 bone structure like tubular bones, pelvic, calcaneus and talus, or vertebra, can be identified relative to a CT, fluoroscopic, X-ray or MR based model of the anatomy.

Example procedures include but are not limited to:

Spinal Procedures and spinal implant procedures such as pedicle screw placement. Pelvis and acetabular fracture treatment such as screw placement or ilio-sacral screw fixation. Fracture treatment procedures, such as intramedullary nailing or screwing or external fixation procedures in the tubular bones.

BrainLAB VectorVision® Trauma is intended to enable operational navigation in spinal, orthopedic and traumatologic surgery, It links a surgical instrument, tracked by flexible passive markers to virtual computer image space on a patients intraoperative image data being processed by a VectorVision workstation.

Vector/sion® Trauma allows navigation of intraopative acquired images considering patients movement in correlation to calibrated surgical instruments. This allows implant positioning, screw placement and bone reduction in different views and reduces the need for treatments under permanent fluoroscopic radiation.

The provided text is a 510(k) summary for the BrainLAB VectorVision Trauma system. It describes the device, its intended use, and its substantial equivalence to predicate devices. However, it does not contain the detailed study information required to answer all the posed questions about acceptance criteria and device performance.

Based on the available text, here's what can be extracted and what cannot:

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

The document does not explicitly state acceptance criteria or provide a table of performance metrics. It generally states that "The validation proves the safety and effectiveness of the system," but no specific numerical criteria or performance results are given.

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.

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

The document describes the device as a "pre- and intraoperative image guided localization system" and states it "links a surgical instrument, tracked by flexible passive markers to virtual computer image space on a patients intraoperative image data." This indicates it's an image-guided surgery system, not an AI-assisted diagnostic or interpretive tool that would typically involve human "readers" in the sense of an MRMC study. Therefore, an MRMC study comparing human readers with and without AI assistance is not applicable to this type of device based on its description, and no such study is mentioned.

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

The device is an image-guided navigation system, inherently designed for human-in-the-loop use during surgery. The concept of "standalone" algorithm performance as typically applied to AI diagnostics (e.g., sensitivity/specificity of an algorithm detecting disease) is not directly applicable in the same way to this device. Its performance would be evaluated in terms of accuracy of instrument tracking, registration, and guidance, which are integral to its use by a surgeon. The validation mentioned "proves the safety and effectiveness of the system," implying comprehensive testing, but not a "standalone" algorithm performance in the diagnostic sense.

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

This information is not provided in the document. For an image-guided surgery system, common ground truths might include phantom studies with known fiducial locations, cadaver studies with direct measurements, or intraoperative verification of instrument tip position relative to anatomical landmarks. However, the document does not specify.

8. The sample size for the training set

As this is an image-guided navigation system and not a machine learning-based diagnostic algorithm in the AI sense, there isn't typically a "training set" as understood for deep learning models. The system's "training" refers to its design, calibration, and engineering. This information is not provided in the document.

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

As there isn't a "training set" in the context of supervised machine learning for this device, how its ground truth was established is not applicable/provided in that sense. The system's accuracy and performance are established through validation and verification tests, but these are not for "training" an AI model.

Summary of what is missing:

The provided text from the 510(k) summary focuses on general device description, indications for use, and a statement of substantial equivalence and validation. It lacks specific details regarding any performance study, acceptance criteria, sample sizes, ground truth establishment, or expert involvement for testing the device's accuracy or effectiveness. This level of detail is typically found in the full 510(k) submission, not necessarily in the public summary.

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