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The xvision Spine System, with xvision System Software, is intended as an aid for precisely locating anatomical structures in either open or percutaneous spine procedures.

Their use is indicated for any medical condition in which the use of stereotactic surgery may be appropriate, and where reference to a rigid anatomical structure, such as the spine or pelvis, can be identified relative to a patient's fluoroscopic or CT imagery of the anatomy. This can include the following spinal procedures:

• Posterior Pedicle Screw Placement in the thoracic and sacro-lumbar region.
• Posterior Screw Placement in C3-C7 vertebrae
• Iliosacral Screw Placement
• Angular procedures requiring access to the disc space
• Lateral trajectories required to access the Sacro-Iliac joint

The Headset of the xvision Spine System displays 2D stereotaxic screens and a virtual anatomy screen. The stereotaxic screen is indicated for correlating the tracked instrument location to the registered patient imagery. The virtual screen is indicated for displaying the virtual instrument location to the virtual anatomy to assist in percutaneous visualization and trajectory planning.

The virtual display should not be relied upon solely for absolute positional information and should always be used in conjunction with the displayed stereotaxic information.

The xvision Spine System (XVS) is an image-guided navigation system designed to assist surgeons in placing pedicle screws accurately, during open or percutaneous computer-assisted spinal surgery by displaying stereoscopic augmented reality (AR) navigation onto the patient anatomy. The system consists of dedicated software running on a PC, a headset, single use passive reflective markers, and reusable components. It uses wireless optical tracking technology and displays to the surgeon the location of the tracked surgical instruments relative to the acquired patient's scan, onto the surgical field. The 2D data and 3D reconstructed model, along with tracking information, are projected to the surgeons' retina using a transparent near-eye-display Headset, allowing the surgeon to both look at the patient and the navigation data at the same time.

The purpose of this 510(k) submission is to introduce a new registration algorithm that enables registering a 3D CT scan that was acquired prior to surgery (pre-operative CT) using 2D X-ray images, taken intra-operatively with a C-Arm. As part of the development of this registration method, the company developed a new software algorithm that includes a deep learning-based spine segmentation algorithm that segments individual vertebrae including the sacrum and ilium. The segmentation output is used as an input to the new registration algorithm.

The indications for use of the subject device compared to its predicate are expanded and include the use of patient's X-ray images for registration and support of angular and lateral procedures requiring access to the disc space and sacro-iliac joint. These modifications do not alter the intended purpose of the system as an aid in localization of anatomical structures during spine surgery or its principles of operation, it just enables additional inputs for registration and supports navigation in additional traiectories.

Here's a summary of the acceptance criteria and study information for the xvision Spine System, based on the provided text:

Acceptance Criteria and Device Performance

Study Details

1. Sample Size Used for the Test Set and Data Provenance:

• Registration Accuracy and Overall System Accuracy (Phantom Tests): Tests were performed using "phantoms" under different scenarios simulating clinical conditions. The exact number of phantoms or test cases within these phantom studies is not specified.
• System Accuracy (Cadaver Studies): Three cadaver studies were conducted. The number of individual screws positioned or specific cadaver count is not specified, but it covered sacro-iliac, sacro-lumbar, thoracic, and C3-C7 vertebrae levels.
• Segmentation Algorithm Validation: "A set of CT scans" was used for validation. The exact number of CT scans is not specified.
• Data Provenance: Not explicitly stated for specific datasets. Phantom tests are simulated. Cadaver studies are typically prospective. The CT scans for segmentation validation were reviewed by "US physicians," implying the retrospective or prospective origin of this data could be from US sources, but this is not definite.

2. Number of Experts Used to Establish the Ground Truth for the Test Set and Qualifications:

• Cadaver Studies: Ground truth for positional and trajectory errors was determined by measuring the difference between actual and virtual screw tip positions and orientations. Clinical accuracy was evaluated using the Gertzbein-Robbins score by viewing post-op scans. The number and qualifications of experts involved in this evaluation are not specified.
• Segmentation Algorithm Validation: Ground truth was established by "manual segmentations that were approved by US physicians." The number of physicians and their specific qualifications (e.g., years of experience, specialty) are not specified.

3. Adjudication Method for the Test Set:

• The document does not explicitly describe an adjudication method (e.g., 2+1, 3+1). For the segmentation algorithm, manual segmentations were "approved by US physicians," which could imply a consensus or review process, but details are not provided.

4. If a Multi-Reader Multi-Case (MRMC) Comparative Effectiveness Study was Done:

• No MRMC comparative effectiveness study involving human readers with and without AI assistance is mentioned in the provided text. The study focuses on the device's standalone performance and its comparison to its predicate.

5. If a Standalone (i.e., algorithm only without human-in-the-loop performance) was Done:

• Yes, a standalone performance evaluation was done for the segmentation algorithm. Its performance was validated against manual segmentations.
• The system-level accuracy and registration accuracy were also evaluated as standalone (device-only) performance, using phantoms and cadavers, measuring inherent accuracy rather than human-AI interaction.

6. The Type of Ground Truth Used:

• Cadaver Studies:
  • Positional and Trajectory Errors: Based on measurements of actual vs. virtual screw tip positions and orientations. This is an objective, measured ground truth.
  • Clinical Accuracy: Evaluated using the Gertzbein-Robbins score by viewing post-op scans. This relies on expert assessment of imaging.
• Segmentation Algorithm Validation: Expert consensus/manual annotation, as it was compared with "manual segmentations that were approved by US physicians."

7. The Sample Size for the Training Set:

• The document does not specify the sample size for the training set used for the deep learning-based spine segmentation algorithm.

8. How the Ground Truth for the Training Set Was Established:

• The document does not specify how the ground truth for the training set was established for the deep learning-based spine segmentation algorithm. It only mentions that the validation of the algorithm was done by comparing it with manual segmentations approved by US physicians.

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The xvision Spine System, with xvision System Software, is intended as an aid for precisely locating anatomical structures in either open or percutaneous spine procedures. Their use is indicated for any medical condition in which the use of stereotactic surgery may be appropriate, and where reference to a rigid anatomical structure, such as the spine or pelvis, can be identified relative to CT imagery of the anatomy. This can include the following spinal implant procedures:

• Posterior Pedicle Screw Placement in the thoracic and sacro-lumbar region.
• Posterior Screw Placement in C3-C7 vertebrae
• Iliosacral Screw Placement

The Headset of the xvision System displays 2D stereotaxic screens and a virtual anatomy screen. The stereotaxic screen is indicated for correlating the tracked instrument location to the registered patient imagery. The virtual screen is indicated for displaying the virtual instrument location to the virtual anatomy to assist in percutaneous visualization and trajectory planning.

The virtual display should not be relied upon solely for absolute positional information and should always be used in conjunction with the displayed stereotaxic information.

The xvision Spine (XVS) system is an image-guided navigation system that is designed to assist surgeons in placing pedicle screws accurately, during open or percutaneous computer-assisted spinal surgery. The system consists of a dedicated software, Headset, single use passive reflective markers and reusable components. It uses wireless optical tracking technology and displays to the surgeon the location of the tracked surgical instruments relative to the acquired intraoperative patient's scan, onto the surgical field. The 2D scanned data and 3D reconstructed model, along with tracking information, are projected to the surgeons' retina using a transparent near-eye-display Headset, allowing the surgeon to both look at the patient and the navigation data at the same time.

The following modifications have been applied to the previously cleared XVS system:

The indications for use of the subject device are expanded compared to the cleared predicate device and include screw instrumentation in additional spine segments, i.e., cervical C3-C7 vertebrae and iliosacral region. Additionally, an Artificial Intelligence (AI) spine segmentation algorithm, based on Convolutional Neural Network (CNN), has been added to provide an improved virtual 3D spine model. The virtual 3D model can be built from the original CT scan or from the Al segmented CT scan. Neither of these modifications alters the intended use of the device as an aid in localization during spine surgery or its principles of operation.

Here's a breakdown of the acceptance criteria and study details for the xvision Spine System, based on the provided FDA 510(k) summary:

1. Table of Acceptance Criteria and Reported Device Performance

The document provides the "System Accuracy Requirement" as the primary acceptance criterion related to performance. The study then reports on validation studies that demonstrate the device meets these specifications.

2. Sample Size Used for the Test Set and Data Provenance

• Test Set Sample Size: Two cadaver studies were conducted. The specific number of cases, screws, or segments tested within these cadaver studies is not explicitly stated in the provided document.
• Data Provenance: The document states "two cadaver studies." This suggests the data is prospective (generated for this specific testing) and likely from a laboratory or research setting. The country of origin of the cadavers or the study location is not specified.

3. Number of Experts Used to Establish Ground Truth for the Test Set and Their Qualifications

• For the cadaver studies (positional and trajectory errors), the method of establishing ground truth (e.g., through physical measurements) is implied by "actual... screw tip position" and "actual... screw orientation" but the number or qualifications of experts involved in these measurements are not specified.
• For the AI segmentation algorithm validation: "manual segmentations that were approved by US physicians" were used as ground truth. The number of physicians/experts and their specific qualifications (e.g., years of experience as radiologists or surgeons) are not specified.

4. Adjudication Method for the Test Set

• For the cadaver studies, no adjudication method is described. Measurements for positional and trajectory errors are typically objective and can be directly measured.
• For the AI segmentation validation, the manual segmentations were "approved by US physicians." This suggests a consensus or review process, but the specific adjudication method (e.g., 2+1, 3+1) is not detailed.

5. If a Multi-Reader Multi-Case (MRMC) Comparative Effectiveness Study Was Done, and Effect Size

• The document does not indicate that an MRMC comparative effectiveness study was done to evaluate how human readers improve with AI vs. without AI assistance. The AI component is described as providing an "improved virtual 3D spine model" but its impact on human reader performance is not measured in this submission.

6. If a Standalone (Algorithm Only Without Human-in-the-Loop Performance) Was Done

• Yes, a standalone performance evaluation was conducted for the AI segmentation algorithm. The "mean Dice coefficient was calculated" to measure the quality of the algorithm's segmentation compared to manual ground truth. This is a common metric for evaluating the performance of segmentation algorithms independently.

7. The Type of Ground Truth Used

• For system accuracy (positional/trajectory errors): The ground truth was based on physical measurements of actual screw tip position and orientation in cadavers.
• For AI segmentation algorithm: The ground truth was established by manual segmentations approved by US physicians.

8. The Sample Size for the Training Set

• The document does not specify the sample size for the training set used for the Convolutional Neural Network (CNN) based AI spine segmentation algorithm. It only mentions that the algorithm has been "added to provide an improved virtual 3D spine model."

9. How the Ground Truth for the Training Set Was Established

• The document does not explicitly state how the ground truth for the training set of the AI algorithm was established. While it mentions manual segmentations by US physicians for the validation set, it does not detail the process for the training data. It's common practice for training data ground truth to also be established by expert annotation, but this is not explicitly confirmed in the provided text.

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