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SpineAR SNAP is intended for use for pre-operative surgical planning on-screen and in a virtual environment, and intra-operative surgical planning and visualization on-screen and in an augmented environment using the HoloLens2 AR headset display with validated navigation systems as identified in the device labeling.

SpineAR SNAP is indicated for spinal stereotaxic surgery, and where reference to a rigid anatomical structure, such as the spine, can be identified relative to images of the anatomy. SpineAR is intended for use in spinal implant procedures, such as Pedicle Screw Placement, in the lumbar and thoracic regions with the HoloLens2 AR headset.

The virtual display should not be relied upon solely for absolute positional information and should always be used in conjunction with the displayed 2D stereotaxic information.

The SpineAR SNAP does not require any custom hardware and is a software-based device that runs on a high-performance desktop PC assembled using "commercial off-the-shelf" components that meet minimum performance requirements.

The SpineAR SNAP software transforms 2D medical images into a dynamic interactive 3D scene with multiple point of views for viewing on a high-definition (HD) touch screen monitor. The surgeon prepares a pre-operative plan for stereotaxic spine surgery by inserting guidance objects such as directional markers and virtual screws into the 3D scene. Surgical planning tools and functions are available on-screen and when using a virtual reality (VR) headset. The use of a VR headset for preoperative surgical planning further increases the surgeon's immersion level in the 3D scene by providing a 3D stereoscopic display of the same 3D scene displayed on the touch screen monitor.

By interfacing to a 3rd party navigation system such as a Medtronic StealthStation S8, the SpineAR SNAP extracts the navigation data (i.e. tool position and orientation) and presents the navigation data into the advanced interactive, high quality 3D image, with multiple point of views on a high-definition (HD) touch screen monitor. Once connected, the surgeon can then execute the plan through the intra-operative use of the SpineAR SNAP's enhanced visualization and guidance tools.

The SpineAR SNAP supports three (3) guidance options from which the surgeon selects the level of guidance that will be shown in the 3D scene. The guidance options are dotted line (indicates deviation distance), orientation line (indicates both distance and angular deviation), and ILS (indicates both distance and angular deviation using crosshairs). Visual color-coded cues indicate alignment of the tracker tip to the guidance object (e.g. green = aligned).

The SpineAR SNAP is capable of projecting all the live navigated and guidance information into an AR headset such as the Microsoft HoloLens2 that is worn by the surgeon during surgery. When activated, the surgeon sees a projection of the 3D model along with the optional live navigated DICOM (Floating DICOM) and guidance cues. This AR projection is placed above, not directly over the patient in order to not impede the surgeon's field of view, but still allow the surgeon to visualize all the desired information (navigation tracker, DICOM images, guidance data) while maintaining their focus on the patient and the surgical field of view (see Figure 1).

SpineAR Software Version SPR.2.0.0 incorporates AI/ML-enabled vertebra segmentation into the clinical workflow to optimize the preparation of a spine surgical plan for screw placement and decompression. The use of the AI/ML device software function is not intended as a diagnostic tool, but as visualization tool for surgical planning.

The use of AI/ML-enabled vertebrae segmentation streamlines the initial processing stage by generating a segmented poly object of each volume-rendered vertebra that requires only minimal to no manual processing, which may significantly reduce the overall processing time.

Here's a detailed breakdown of the acceptance criteria and the study that proves the device meets them, based on the provided FDA 510(k) clearance letter for SpineAR SNAP:

1. Table of Acceptance Criteria and Reported Device Performance

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

• CT Performance Validation:
  • Sample Size: 95 scans from 92 unique patients.
  • Data Provenance: Retrospective. The validation set was composed of the entire Spine-Mets-CT-SEG dataset and the original test set from the VerSe dataset.
    • Country of Origin: Diverse, with 60% of scans from the United States and 40% from Europe.
    • Representativeness: Included a balanced distribution of patient sex, a wide age range (18-90), and data from three major scanner manufacturers (Siemens, Philips, GE).
• Sacrum Validation (CT):
  • Sample Size: 38 scans.
  • Data Provenance: A separate set from the TotalSegmentator dataset, reserved exclusively for testing. Implicitly retrospective.
• MRI Performance Validation:
  • Sample Size: 31 scans from 15 unique patients.
  • Data Provenance: A portion of the publicly available SPIDER dataset, reserved exclusively for performance validation. Implicitly retrospective.
    • Country of Origin: The training data for the MRI model (SPIDER dataset) was collected from four different hospitals in the Netherlands, suggesting the validation data is also from the Netherlands.
    • Representativeness: Included data from both Philips and Siemens scanners and a balanced distribution of male and female patients.

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

The document states that the ground truth segmentation was "provided by expert radiologists." It does not specify the number of experts or their specific qualifications (e.g., years of experience). This information would typically be found in a more detailed study report.

4. Adjudication Method for the Test Set

The document does not explicitly state the adjudication method used for establishing the ground truth for the test set. It only mentions that the ground truth was "provided by expert radiologists."

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

No, a Multi-Reader Multi-Case (MRMC) comparative effectiveness study was not mentioned. The study focused on the standalone performance of the AI algorithm for segmentation. The document mentions "Human Factors and Usability testing," which often involves user interaction, but does not describe a comparative study measuring human reader improvement with AI assistance.

6. If a Standalone (i.e., algorithm only without human-in-the-loop performance) Was Done

Yes, a standalone performance study of the AI algorithm was done. The document reports the Mean Dice Coefficient (MDC) and its 95% confidence interval for the AI model's segmentation accuracy against expert-provided ground truth, indicating an algorithm-only performance evaluation.

7. The Type of Ground Truth Used

The ground truth used for both training and validation sets was expert consensus / expert-provided segmentation. Specifically, the document states: "This score measures the degree of overlap between the AI's segmentation and the ground truth segmentation provided by expert radiologists."

8. The Sample Size for the Training Set

• CT Vertebrae Model Development: A total of 1,244 scans were used for model development (training and tuning).
• CT Sacrum Model Development: A total of 430 scans were used for model development.
• MRI Vertebrae Model Development: A total of 348 scans were used for model development.

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

The training data was aggregated from several independent, publicly-available academic datasets: VerSe 2020, TotalSegmentator, and SPIDER. For these datasets, the ground truth would have been established by medical experts (radiologists, clinicians) often as part of larger research initiatives, typically through manual or semi-automated segmentation and subsequent review, often involving expert consensus to ensure accuracy and consistency. The document mentions "sacrum ground-truth data" for the TotalSegmentator dataset, implying expert-derived ground truth.

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