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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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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.

Ask a specific question about this device

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 for any medical condition in which the use of stereotactic surgery may be considered to be safe and effective 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 hip endoprothesis to the preoperatively or intraoperatively planned position.

Example orthopedic surgical procedures include but are not limited to:

Total Joint Replacement (TJR) Revision surgery of TJR Minimal Invasive Orthopedic Surgery Tumor resection and bone/joint reconstruction

BrainLAB VectorVision® hip Software is intended to enable operational planning and navigation in orthopedic 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, generated through acquiring multiple landmarks on the bone surface either by a pointer probe or by acquiring pairs of registered fluoroscopic images. VectorVision® hip Software uses the registered landmarks to navigate the needed surgical tools like cup reamer, cup inserter, stem rasp, bone saw and the implant to the planned position. 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.

Vector\Vision® hip Software allows 3-dimensional reconstruction of the relevant mechanical axes and planes of femur and pelvis and alignment of the implants. The VectorVision® hip 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. The VectorVision® hip Software registers the patient data needed for planning and navigating the surgery intra-operatively within the CT free module. 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 BrainLAB VectorVision® hip Software, which focuses on demonstrating substantial equivalence to a predicate device rather than providing a detailed study report with specific acceptance criteria and performance metrics. Therefore, much of the requested information about a study proving the device meets acceptance criteria is not explicitly stated in this document.

However, I can extract the information that is present and indicate where the requested details are missing based on the content provided.

Here's an attempt to answer your request based only on the provided text:

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

The document does not explicitly state quantitative acceptance criteria or report specific device performance metrics (e.g., accuracy in angle or translation measurements) from a validation study. It broadly claims "The validation proves the safety and effectiveness of the system."

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

The document does not provide details on any specific test set, sample size, or data provenance (country of origin, retrospective/prospective). It mentions "BrainLAB VectorVision® hip Software has been verified and validated according to BrainLABs procedures for product design and development," but no specifics on the validation study are given.

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 does not describe an MRMC comparative effectiveness study or assess human reader improvement with AI assistance. The device is a surgical navigation system, not an AI diagnostic tool for interpreting medical images by human readers.

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

The document does not explicitly present data from a standalone performance study. The entire description of the device implies human-in-the-loop usage (surgeon using the system to navigate). The "validation" broadly states safety and effectiveness, but no specific study design or results are provided.

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

The document does not specify the type of ground truth used for any validation testing. It mentions the system's ability to navigate to a "preoperatively or intraoperatively planned position," suggesting that surgical plans and intraoperative reality would be relevant for ground truth, but this is not detailed.

8. The sample size for the training set

The document does not mention a training set or its sample size. This type of detail is typically associated with machine learning model development, which is not the primary focus of this 510(k) summary (focused on a navigation system).

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

As no training set is mentioned, information on how its ground truth was established is also not present.

Ask a specific question about this device

BrainLAB VectorVision hip is intended to be an intraoperative image guided localization system. It links a freehand probe, tracked by a passive marker sensor system to virtual computer image space either on a patient's preoperative image data being processed by a VectorVision workstation or 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 hip endoprothesis to the preoperatively or intraoperatively planned position. Example orthopedic surgical procedures include but are not limited to: Total Joint Replacement (TJR), Revision surgery of TJR, Tumor resection and bone/joint reconstruction.

BrainLAB VectorVision® Hip is intended to enable operational planning and navigation in orthopedic 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 either based on a patients preoperative image data or generated through acquiring multiple landmarks on the bone surface. VectorVision® Hip uses the registered landmarks to navigate the needed surgical tools like cup reamer, cup inserter, stem rasp, bone saw and the implant to the planned position. 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. VectorVision® Hip allows 3-dimensional reconstruction of the relevant mechanical axes and planes of femur and pelvis and alignment of the VectorVision® Hip 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. The VectorVision® Hip software registers the patient data needed for planning and navigating the surgery intra-operatively within the CT free module. The System can be used to generally align tool orientations according to the anatomy described and defined by the landmarks acquired by the surgeon. Using the CT based module the patient data can be used additionally for surgery, the patient data is then is provided by the CT data stored on a standard data storage media.

The provided document is a 510(k) summary for the VectorVision® hip system. It describes the device, its intended use, and states that it has been verified and validated according to BrainLAB's procedures. However, this document does not contain the specific acceptance criteria and detailed study results that would typically be used to prove a device meets those criteria.

The document states: "VectorVision® hip 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 a study was conducted, but the details of that study, including the acceptance criteria and performance metrics, are not included in this publicly available summary.

Therefore, I cannot provide the requested information based only on the input you shared. A 510(k) summary is a high-level overview. The detailed validation and verification studies, including acceptance criteria and performance data, are typically found in the full 510(k) submission, which is not publicly accessible in its entirety.

Here's what I can tell you based on the provided text, and what I cannot:

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

• Cannot be provided. This information is not present in the 510(k) summary.

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

• Cannot be provided. This information is not present in the 510(k) summary.

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)

• Cannot be provided. This information is not present in the 510(k) summary. The nature of the device (a surgical navigation system) suggests that "ground truth" might relate more to mechanical accuracy and alignment compared to a planned position, rather than diagnostic interpretation by experts.

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

• Cannot be provided. This information is not present 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

• Not applicable and cannot be provided. This device is a surgical navigation system, not an AI-assisted diagnostic tool for "human readers." Its purpose is to guide a surgeon during an operation to accurately navigate a hip endoprothesis.

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

• Not explicitly stated, but the nature of the device implies a "standalone" accuracy assessment. Surgical navigation systems are generally evaluated on their inherent accuracy in tracking and displaying anatomical structures and instrument positions relative to a plan. This would be an "algorithm and hardware only" performance assessment, where the performance of the system itself (tracking accuracy, registration accuracy) is measured. However, the specific details or results of such a test are not included in this document.

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

• Cannot be explicitly stated. For a surgical navigation system, ground truth would likely involve highly precise measurements of physical alignment and position (e.g., using a coordinate measuring machine or highly accurate phantom models) compared to the system's reported measurements or the surgical plan. This is not "expert consensus" or "pathology" in the typical sense for a diagnostic device.

8. The sample size for the training set

• Not applicable and cannot be provided. This device is a surgical navigation system. It does not typically "learn" or require a "training set" in the way a machine learning algorithm for image analysis does. Its functionality is based on geometric computations and tracking algorithms.

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

• Not applicable and cannot be provided. As above, it's not a machine learning device in the sense that it has a "training set" to establish ground truth for.

In summary, the provided 510(k) summary confirms that validation and verification were performed but does not contain the detailed study results or acceptance criteria. You would need to access the full 510(k) submission to find this level of detail.

Ask a specific question about this device

BrainLAB VectorVision is intended to be an intraoperative image guided localization system. It links a freehand probe, tracked by a passive marker sensor system to virtual computer image space either on a patient's preoperative image data being processed by a Vector vision workstation 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 hip endoprothesis to the preoperatively or intraoperatively planned position.

Example orthopedic surgical procedures include but are not limited to:

Total Joint Replacement (TJR)
Revision surgery of TJR
Tumor resection and bone/joint reconstruction

BrainLAB VectorVision® Hip is intended to enable operational planning and navigation in orthopedic and surgical procedures. It links a surgical instrument, tracked by passive markers to virtual computer image space on a patient's preoperative image data or an individual 3D-model of the patient's bone, which is generated through acquiring multiple landmarks on the bone surface. Vector Vision® Hip uses the registered patient data to guide the cup reamer, cup inserter, stem rasp, bone saw and the implant to the planned position. If no implant data is available it is possible to provide information in order to define the implant and the bone orientation as defined by the operating surgeon.

VectorVision® Hip allows 3-dimensional reconstruction of the relevant mechanical axes and planes of femur and pelvis. VectorVision® Hip software has been designed to read in data and perform calculations based on the geometry of implants and instruments if provided by the implant manufacturer and offers to individually choose the type of implants and instruments. VectorVision® Hip software registers the patient data needed for prosthesis implantation using the CT free module. The system can be used for planning and navigating the surgery intraoperatively. Special landmarks and any other anatomical information referring to the patient data can be used additionally for surgery, the patient data is then provided by the CT data stored on a standard data storage media.

The provided text (K040368) is a 510(k) summary for the VectorVision® Hip system. It focuses on establishing substantial equivalence to a predicate device and does not contain detailed information about specific acceptance criteria, study designs, sample sizes, or ground truth establishment typically found in performance studies for new medical devices.

Therefore, many of the requested details cannot be extracted directly from this document.

Here's a breakdown of what can be extracted and what information is missing:

1. Table of Acceptance Criteria and Reported Device Performance:

This information is not provided in the 510(k) summary. A 510(k) often focuses on comparing the new device's features and intended use to a predicate device to argue for substantial equivalence, rather than presenting detailed performance study results against specific acceptance criteria.

2. Sample size used for the test set and the data provenance:

• Sample size for test set: Not provided. The document states that the device "has been verified and validated according to BrainLAB's procedures for product design and development," but no specific test set sample sizes are given for this validation.
• Data provenance: Not provided. No information about country of origin, or if data was retrospective or prospective, is mentioned.

3. Number of experts used to establish the ground truth for the test set and the qualifications of those experts:

This information is not provided.

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

This information is not provided.

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:

• MRMC study: Not provided. This document does not describe an MRMC study. The VectorVision® Hip is described as an "intraoperative image guided localization system" that "aids the surgeon to accurately navigate a hip endoprothesis." It's not an AI-driven diagnostic tool where "human readers improve with AI vs without AI assistance" would typically be measured.

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

• Standalone performance: Not explicitly stated in the context of an "algorithm only" study. The device is described as an "intraoperative image guided localization system" that assists the surgeon. Its performance is inherently tied to human interaction (the surgeon using the system to navigate). The summary focuses on its function as a navigation aid.

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

This information is not provided.

8. The sample size for the training set:

Not applicable/Not provided. This device is described as an image-guided surgery system, not a machine learning model that would typically have a "training set" in the sense of supervised learning. While it processes image data and creates 3D models, the core functionality is navigation and not predictive modeling based on a trained dataset.

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

Not applicable/Not provided. (See point 8).

Summary of what the document DOES state regarding verification and validation:

The document briefly mentions:
"VectorVision® Hip has been verified and validated according to BrainLAB's procedures for product design and development. This verification and validation demonstrated the safety and effectiveness of the system."

However, it does not elaborate on the specifics of these verification and validation activities, such as:

• What parameters were measured.
• What the quantitative acceptance criteria were.
• What the results of those measurements were.
• How ground truth was established for any performance testing.
• The exact study design or participant details.

This 510(k) submission primarily relies on demonstrating substantial equivalence to its predicate device (VectorVision® Hip K010602 and Vector Vision® CT-free Knee K021306) rather than providing extensive de novo performance study details.

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Ask a specific question about this device