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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 Anatase Spine Surgery Navigation System is indicated for precise positioning of surgical instruments or spinal implants during general spinal surgery when reference to a rigid anatomical structure, such as the vertebra, can be identified relative to a patient's fluoroscopic or CT imagery. It is intended as a planning and intraoperative guidance system to enable open or percutaneous image guided surgery by means of registering intraoperative 2D fluoroscopic projections to pre-operative 3D CT imagery.

Example procedures include but are not limited to:

Posterior-approach spinal implant procedures, such as pedicle screw placement, within the lumbar region.

The Anatase Spine Surgery Navigation System, also known as an Image Guided System, is comprised of a platform, clinical software, surgical instruments, and a referencing system. The system uses wireless optical tracking technology to track the position of instruments in relation to the surgical anatomy and identifies this position on diagnostic or intraoperative images of a patient. The system helps guide surgeons during spine procedures such as spinal fusion. The software functionality in terms of its feature sets are categorized as imaging modalities, registration, planning, interfaces with medical devices, and views.

The provided text describes the Anatase Spine Surgery Navigation System and its 510(k) submission to the FDA. The document focuses on demonstrating substantial equivalence to a predicate device rather than presenting a standalone study with detailed clinical performance data, acceptance criteria, and ground truth establishment for a specific AI-driven diagnostic or prognostic task.

However, based on the Accuracy section under "Performance Data Verification and validation activities," we can infer the acceptance criteria and the nature of the study for positional accuracy.

Here's an analysis based on the provided text, while acknowledging limitations due to the nature of the document (a 510(k) summary for device clearance):

1. Table of Acceptance Criteria and Reported Device Performance:

The document explicitly states that the positional accuracy of the system is evaluated in accordance with ASTM F2554-18. While the specific numerical acceptance criteria and the reported performance values are not provided in this summary document, the fact that it passed verification and validation implies that it met predefined criteria based on this standard.

To get the specific values, one would need to consult a more detailed technical report or the full 510(k) submission, which is not fully available here.

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

The document does not specify the sample size used for the positional accuracy test set nor the data provenance (e.g., country of origin, retrospective/prospective). This information is typically found in detailed test reports, not a 510(k) summary.

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

The document does not mention the use of experts to establish ground truth for the positional accuracy testing. Positional accuracy testing typically relies on metrology equipment and precise physical measurements, not expert human evaluation.

4. Adjudication Method for the Test Set:

Not applicable, as ground truth for positional accuracy is established by metrological methods, not human adjudication.

5. Multi-Reader Multi-Case (MRMC) Comparative Effectiveness Study:

No, a multi-reader multi-case (MRMC) comparative effectiveness study was not explicitly mentioned or described. The document states "The Anatase Spine Surgery Navigation System is indicated for precise positioning of surgical instruments or spinal implants..." suggesting it is a tool for surgeons rather than an AI diagnostic device that assists human readers in interpreting images.

6. Standalone (Algorithm Only Without Human-in-the-Loop Performance) Study:

Yes, a standalone performance evaluation regarding positional accuracy was conducted for the device. The text states, "Accuracy: Positional accuracy of the system is evaluated in accordance with ASTM F2554-18." This refers to the objective measurement of the system's tracking and navigation capabilities.

7. Type of Ground Truth Used:

For positional accuracy, the ground truth would be established through metrological measurements using calibrated instruments and reference standards, as dictated by ASTM F2554-18. It is not expert consensus, pathology, or outcomes data in this context.

8. Sample Size for the Training Set:

The document does not mention a "training set" in the context of machine learning or AI. This device is a navigation system that uses optical tracking and image registration, implying a more traditional engineering validation process rather than a deep learning model requiring a large training dataset for a diagnostic task.

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

Not applicable, as there is no mention of a training set for an AI/machine learning model to establish ground truth.

Summary of Device Performance Study Information based on the provided text:

The information primarily focuses on regulatory conformity and substantial equivalence. For specific performance studies, the key takeaway is:

• Study Type for Accuracy: Evaluation of positional accuracy.
• Standard Used: ASTM F2554-18.
• Ground Truth for Accuracy: Metrological measurements (implied by the standard).
• Result: The device met performance requirements, indicating it passed the accuracy criteria.

The document does not provide the detailed numerical results, sample sizes, or specifics of the test setup beyond referencing the standard.

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