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The BrainLAB Cranial IGS System is intended to be an intra-operative image guided localization system to enable minimally invasive surgery. It links a freehand probe, tracked by a magnetic sensor system or a passive marker sensor system to a virtual computer image space on patient image data being processed by the IGS 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 long bone, or vertebra, can be identified relative to a CT, CTA, X-Ray, MR, MRA and ultrasound based model of the anatomy.

Example procedures include but are not limited to:

Cranial Procedures: Tumor resections Skull base surgery Cranial biopsies Craniotomies/ Craniectomies Pediatric Catheter Shunt Placement General Catheter Shunt Placement Thalamotomies/ Palliodotomies

ENT Procedures: Transphenoidal procedures

Maximillary antrostomies Ethmoidectomies Spheno-idotomies/ sphenoid explorations Turbinate resections Frontal sinusotomies Intranasal

The Cranial IGS System consists of the IGS workstation, the touch screen monitor and the 3D tracking system. A set of hardware accessories provides for comfortable and accurate use of the system.

The IGS workstation holds the patient data during the surgery and runs the cranial software application.

The patient data needed for the image-guided surgery is acquired pre-operatively or intraoperatively and is transferred to the IGS workstation via network, data carrier or data bus. The cranial software application offers the display of the patient data in various reconstructions, segmentations and overlays on the touch screen in addition to position information of tracked instruments – optionally combined with outlined information. The touch screen enables the control of the cranial software application and can be draped for sterile use by the surgeon.

The electro-magnetic or optical 3D tracking system performs the localization of patient and surgical tools within the operating field.

The virtual diagnostic image spaces are correlated ("registered") to the surgical environment by collecting the 3D position of anatomical landmarks or fiducial markers with a tracked pointer probe and relating them with the corresponding features extracted from the diagnostic image data sets. Alternatively, the patient's skin surface can be scanned with a laser device or touched with a pointer device and matched to the 3D reconstruction of the patient data set. If several diagnostic image spaces have been acquired from the same patient, only one of them has to be registered whereas the remaining ones can be fused to the registered data set.

Intra-operatively acquired patient data can furthermore be correlated ("registered") to the surgical environment by determining its spatial position to the patient during its acquisition.

Structures in the patient's body are localized using trackable pre-calibrated or intra-operatively calibrated surgical instruments. Examples of surgical instruments are the pointer tool, biopsy needles, catheter stylets or suction tubes.

Surgical microscopes, ultrasound devices and endoscopes are additional intra-operative image sources, which are connected with the Cranial IGS System via signal transmission cables. They can be calibrated and tracked similar as any other surgical instrument. Their images can be displayed on the touch screen or external monitors and combined with the available patient data in correct spatial relation. The settings of microscope and ultrasound devices offering a communication interface can be controlled from the Cranial IGS System. Navigation information can be displayed in the microscope's image injection module. Defined components of the Cranial IGS System are prepared for the use in magnet-resonance environments.

The Cranial IGS System contains hardware accessories and software features to improve the support and guidance of surgical instruments.

The Cranial IGS System contains a network based software interface that allows downloading medical data (such as image sets, objects, trajectories or points) and tracking data from the system as well as to upload and display an image stream to the system. This interface can be used to implement custom visualization of medical data (e.g. included modalities which are otherwise unknown to the cranial software application) as well as to control other devices. These view data is strictly under the responsibility of the user and clearly marked as such.

The provided text describes the Cranial Image Guided Surgery System, which includes the predicate devices VectorVision cranial, VectorVision ENT, Kolibri cranial, Kolibri ENT, Cranial Essential, Cranial Unlimited, ENT Essential, and ENT Unlimited. The modification discussed is the addition of a "Disposable Stylet." The document focuses on establishing substantial equivalence for this new accessory rather than presenting a detailed study with specific acceptance criteria and performance data for the entire system or the stylet.

Therefore, the information regarding acceptance criteria, specific device performance, sample sizes, ground truth establishment, expert qualifications, and MRMC studies is not explicitly available in the provided text. The text primarily outlines the device's intended use, description, and the regulatory determination of substantial equivalence for the "Disposable Stylet" accessory.

However, based on the information provided, we can infer some aspects related to testing for the "Disposable Stylet" and general regulatory context.

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

Specific numerical acceptance criteria and reported device performance metrics are not detailed in the provided document. The submission focuses on demonstrating substantial equivalence, which implies that the device (specifically the Disposable Stylet) performs at least as well as the predicate device in its intended function.

The text mentions:

• "The added accessory 'Disposable Stylet' 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."
• "Compatibility of the Disposable Stylet to third-party catheters has been shown in performance testing. Therefore the force necessary to extract the Disposable Stylet vs. third-party stylets out of catheters representing common boundary conditions like materials, coatings and sizes was measured. This had been done in air as well as in saline to simulate the intended use."

This indicates that performance testing was conducted, but the specific acceptance criteria (e.g., maximum extraction force, accuracy of calibration) and the quantitative results are not provided 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):

This information is not provided in the document. The text refers generally to "performance testing" and "studies" but does not specify sample sizes or data provenance.

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. The testing described for the "Disposable Stylet" appears to be more focused on physical performance (e.g., extraction force) rather than expert interpretation of images or clinical outcomes that would require a ground truth established by medical experts 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 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. The device in question is an image-guided surgery system and a disposable stylet, which are tools for surgical navigation and instrument placement, not diagnostic AI systems for image interpretation that would typically involve MRMC studies.

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

The device is an "intra-operative image guided localization system" that tracks a freehand probe relative to patient image data. It also includes the "Disposable Stylet" as a pre-calibrated guiding stylet. The nature of these devices inherently involves human interaction (the surgeon using the system and stylet). While the calibration of the stylet is "pre-calibrated" (meaning the software contains information optimized for it and user calibration is not necessary), the system itself is designed as a human-in-the-loop tool. Therefore, a purely standalone algorithm performance study, in the sense of a diagnostic AI, is unlikely to be relevant or described for this type of device. The verification and validation mentioned would likely involve testing the accuracy and reliability of the tracking system and the stylet's calibration.

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

For the Cranial IGS System generally, the "ground truth" for navigation is derived from the patient's own diagnostic image data (CT, CTA, X-Ray, MR, MRA, ultrasound) which are then registered to the patient's anatomy during surgery. This is a form of image-based anatomical ground truth.

For the Disposable Stylet, the "ground truth" for the performance testing cited (extraction force) would be the physical properties and measurements of the stylet and the catheters. The "calibration information optimized for the Disposable Stylet" stored in the software serves as its internal "ground truth" for tracking.

8. The sample size for the training set:

This information is not provided in the document. The device does not appear to be an AI/ML system that undergoes a "training" phase with a distinct training set in the typical sense. The software contains "calibration information optimized for the Disposable Stylet," which implies pre-determined parameters rather than a learned model from a large dataset.

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

This information is not provided in the document, as a "training set" and associated ground truth establishment (in the AI/ML sense) are not explicitly mentioned or implied for this type of device. The pre-calibration of the stylet would have been established through engineering and metrology processes to define its geometric properties to a high degree of accuracy.

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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 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'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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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.

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BrainLAB's VectorVision spine is intended for use as an intraoperative image-quided localization system for minimally invasive surgery. It links a freehand probe, tracked by a passive marker sensor system to virtual computer image space on a patient's preoperative image data that is 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, the pelvis, a long bone or vertebra can be identified relative to a CT, x-ray or MR-based model of the anatomy. Example procedures include but are not limited to:

• · Navigated spinal procedures in support of standard approaches (e.g. anterior, lateral, oblique)
• · Spinal implant procedures such as
  • · Pedicle screw placement
  • · Anterior plating
• · Kyphoplasty and vertebroplasty procedures
• · Placement of other temporary or permanent devices such as k-wires, needles, catheters or electrodes
• · Thoracic spine surgerv
• · Tumor surgery on the spinal column and adjacent soft tissue
• Placement of acetabular and SI screws on the pelvis

VectorVision spine is a device that allows surgical planning and navigation. It links a surgical instrument. (tracked by passive marker sensor system) to a location on a virtual computer image, which is either based ou patient's preoperative 3D information of a CT or MR dataset or based on patient's intraoperative acquired 2D fluoro image(s) of a c-arm.

The device enables the navigation based on 3D data and / or based on acquired fluoro images,

Based on 2D fluoro images, the registration is done automatically by using the exact spatial position informat. of the intra-operatively acquired fluoro images.

Based on 3D data, the procedure of linking the surgical instrument to the virtual computer image is achieved by performing registration methods as paired point matching, surface matching, region matching or CT fluoro matching. The last registration method uses 2D fluoro images to register the previously acquired 3D dataset. Thus, CT fluoro matching combines 2D fluoro imaging with 3D datasets,

After registration, the device assists the surgeon in performing certain surgical procedures as described in the indications for use.

Here’s an analysis of the provided text regarding the acceptance criteria and study for the VectorVision spine device:

The provided 510(k) summary for the BrainLAB VectorVision spine (K053159) does not explicitly state specific acceptance criteria or provide a detailed study report with performance metrics for the device itself.

Instead, the submission primarily focuses on establishing substantial equivalence to predicate devices (BrainLAB VectorVision CT / Fluoro K010968 and BrainLAB Kolibri spine K042721) based on the intended use and device description.

The key statement regarding validation is:
"VectorVision spine 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 indicates that internal validation was performed, but the specific details of that validation (e.g., acceptance criteria, test results, statistical analysis) are not presented in this publicly available summary. Such detailed information is typically kept by the manufacturer and is part of the full 510(k) submission, not necessarily released in the abbreviated summary.

Therefore, many of the requested points cannot be answered from the provided text.

Here's an attempt to answer the questions based only on the provided text, noting limitations:

Acceptance Criteria and Study for BrainLAB VectorVision spine (K053159)

This 510(k) summary focuses on demonstrating substantial equivalence by outlining the device's intended use and functionality, rather than presenting a detailed study with explicit acceptance criteria and corresponding performance metrics. The summary states that the device was "verified and validated according to BrainLAB's procedures for product design and development. The validation proves the safety and effectiveness of the system." However, the specifics of these validation activities, including numerical acceptance criteria or performance results, are not provided in this document.

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

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

Not specified in the provided 510(k) summary. The internal verification and validation studies are not detailed.

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 specified in the provided 510(k) summary. Given the nature of a surgical navigation system, "ground truth" for performance testing would likely involve highly precise measurement tools and potentially expert surgeons in a simulated or cadaveric setting, but no details are given.

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

Not specified in the provided 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. This device is a surgical navigation system, not an AI-assisted diagnostic imaging device for "human readers" in the typical sense of MRMC studies. Its purpose is to guide surgeons, not to interpret images or improve human diagnostic accuracy in a reading task.

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

The device is inherently a "human-in-the-loop" system, assisting surgeons. While its underlying algorithms have standalone performance characteristics (e.g., registration accuracy), the overall device performance is always in the context of aiding a surgeon. No details on purely "algorithm only" performance are provided in isolation from the human user.

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

Not specified in the provided 510(k) summary. For a surgical navigation system, ground truth would typically refer to the true anatomical position or the true position of tracked instruments, often established by high-precision physical measurements or imaging techniques.

8. The sample size for the training set

Not applicable. This device, being a system for surgical navigation in 2005, is not described as utilizing machine learning or AI models in the same way modern devices do, which would typically rely on a "training set." Its functionality is based on established image processing, registration algorithms, and passive marker tracking.

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

Not applicable, as there is no mention of a "training set" in the context of contemporary AI/ML.

In summary: The provided 510(k) document is a summary of the device's intended use and design, culminating in the FDA's finding of substantial equivalence to predicate devices. It does not contain the detailed technical data, specific acceptance criteria, or performance study results that would typically be included in the full technical file or detailed clinical study reports.

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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.

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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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BrainLAB VectorVision ACL is intended to be an intra-operative 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 intraoperative image data which is processed by a VectorVision workstation. A virtual individual 3D-surface model of the patient's bone, which is generated through acquiring multiple landmarks on the bone surface, supplements the 2D information of the intra-operative image data.

The system is indicated for any medical condition in which the use of stereotactic surgery may be appropriate and where a reference to a bone structure, such as tibia and femur, can be identified relative to a CT, fluoroscopic, x-ray or MR-based model of the anatomy.

An example procedure includes but is not limited to:

Planning and drill-tunnel guidance of interosseous canals for ligament repair on the knee.

BrainLAB VectorVision® ACL is a touchscreen-based-intra-operative planning and navigation software, designed for use in anterior cruciate ligament surgey. It is intended to support the surgeon in the planning and drilling of ideal graft canals for the replacement of a torn Anterior Cruciate Liqament (ACL). VVACL uses registered fluoroscopic images and their defined exact spatial position to the patient. These images are acquired intra-operatively using a C-arm. A virtual individual 3D-surface model of the patient's generated through acquiring multiple landmarks on the bone surface, supplements the 2D information of the intra-operative image data.

The provided text is a 510(k) Summary for the VectorVision® ACL device, which is an intraoperative image-guided localization system. It describes the device, its indications for use, and its substantial equivalence to predicate devices, but it does not contain information about acceptance criteria, specific device performance metrics, or details of a study proving the device meets acceptance criteria.

The text states:
"VectorVision® ACL 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."
And:
"The information provided by BrainLAB in this 510 (k) application was found to be substantially equivalent with the 510(k)-clearance of VectorVision® CT-free knee (K 021306) and VectorVision® Trauma (K012448)."

This indicates that a validation was performed according to the manufacturer's internal procedures and that the FDA found it substantially equivalent to previously cleared devices. However, the specific details of how "safety and effectiveness" were proven, what the acceptance criteria were, or the results of any performance studies are not provided in this document.

Therefore, I cannot populate the table or answer the specific questions about acceptance criteria and study details based on the provided text. The document focuses on the regulatory submission process and the determination of substantial equivalence, not the detailed technical performance study results.

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BrainLAB VectorVision is intended to be an 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 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 knee endoprothesis to the intraoperatively planned position. Ligament balancing and measurements of bone alignment are provided by VectorVision® uni-knee.

Example orthopedic surgical procedures include but are not limited to:

Knee Procedures: Unicondylar Knee Replacement Ligament Balancing Range of Motion Analysis Patella Tracking

BrainLAB VectorVision®uni-knee is intended to enable 3 dimensional implant planning and navigation for unicompartimental orthopedic knee surgery. The SW links a surgical instrument tracked by passive markers to a 3D-model of the patient's bone, which is generated by acquiring multiple landmarks on the bone surface. Vector/ision® uni-knee uses the registered landmarks to navigate the femoral and tibial cutting guides to the pre-planned position.

VectorVision® uni-knee allows 3-dimensional reconstruction of the mechanical axes and alignment of the implants. The VectorVision® uni-knee software has been designed to read in implant data and tool data from different manufacturers and offers to individually choose the prosthesis during each surgery. The VectorVision® uni-knee software registers the patient data needed for planning and navigating intraoperatively. No preoperative CT-scanning is necessary.

The provided text is a 510(k) Summary of Safety and Effectiveness for the BrainLAB VectorVision® uni-knee system, dated October 5, 2004. It describes the device, its intended use, and claims substantial equivalence to predicate devices. However, this document does not contain the specific information requested about acceptance criteria, device performance results, sample sizes, ground truth establishment, or study methodologies.

Here's a breakdown of why the requested information cannot be extracted from the provided text:

• No Acceptance Criteria or Performance Data: The document states that the device "has been verified and validated according to BrainLAB's procedures for product design and development" and that "The validation proves the safety and effectiveness." However, it does not detail what those specific acceptance criteria were (e.g., accuracy thresholds, precision measurements) nor does it report the actual device performance against any such criteria.
• No Study Details: There is no mention of specific studies conducted to prove the device meets acceptance criteria. Information regarding test set sample sizes, data provenance, number or qualifications of experts, adjudication methods, MRMC studies, standalone performance, ground truth types, training set sample sizes, or how training set ground truth was established is entirely absent.

In summary, the provided document is a regulatory submission claiming safety and effectiveness based on internal validation, but it does not provide the details of that validation or the performance data against defined acceptance criteria, as an engineering or clinical study report would.

Therefore, I cannot populate the requested table or answer the specific questions below it. The entire response would be "Not provided in the given text."

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