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The Surgical Theater, LLC SNAP is intended for use as a software interface and image segmentation system for the transfer of imaging information from a CT, MR or X-ray 3D Angiography (XA) medical scanner to an output file. It is also intended for use in simulating surgical treatment options both pre-operatively and intra-operatively with validated systems as identified in the device labeling.

The Surgical Navigation Advanced Platform (SNAP) is intended for use as a software interface and image segmentation system for the transfer of imaging information from CT or MR medical scanner to an output file. It is also intended for use in simulating and evaluating surgical treatment options both pre-operatively and intra-operatively with validated systems as identified in the device labeling.

The Surgical Navigation Advanced Platform (SNAP) transforms medical images into a dynamic, interactive 3D scene, and connects to external 3rd party surgical navigation systems (i.e. "validated systems"), to extract and display intra-operative surgical navigation information (such as the 3D navigation pointer) inside the generated 3D scene. Current navigation systems usually display the navigation data on 2D black and white DICOM imagery within the external navigation system itself. The SNAP displays the same navigation data (pointer position and orientation), as it is received from the external 3rd party navigation system. in a 3D fashion inside the SNAP 3D model of the anatomy as it is reconstructed from the original DICOM slices.

The SNAP allows surgeons to analyze and plan a specific patient's case before surgery, and then take that plan into the operating room (OR) and use it in conjunction with a validated traditional navigation system during surgery. The SNAP then 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. The surgeon can perform a virtual / simulated "fly-through" inside the 3D scene using controls such as rotate, zoom in and zoom out. During pre-operative use a virtual reality (VR) headset 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.

The SNAP product does not include any custom hardware and is a software-based device that runs on a high performance desktop PC assembled using "commercial off-the-shelf" components. The design is based on an advanced, touch screen friendly, Graphical User Interface (GUI) that runs an underlying simulation engine to process medical images in DICOM format, and an image generator software engine.

Here's an analysis of the provided text regarding the acceptance criteria and supporting study for the Surgical Navigation Advanced Platform (SNAP):

Based on the provided text, the device is the Surgical Navigation Advanced Platform (SNAP), and the submission is for modifications to an existing cleared device (K140819). The modifications include adding support for X-ray 3D Angiography (XA) scans, incorporating a VR headset for intra-operative use (previously only pre-operative), and adding a video capture PCB and an "Endo View" GUI for displaying live endoscopy video side-by-side with 3D scenes.

It's important to note that the document primarily focuses on demonstrating substantial equivalence to a predicate device (K140819) rather than presenting a standalone study with specific performance metrics against acceptance criteria for the entire device's functionality. The performance data mentioned are related to Electromagnetic Compatibility (EMC) and Software Verification and Validation Testing to ensure the modifications do not negatively impact the device's safety and effectiveness.

Here's a breakdown of the requested information based only on the provided text:

1. Table of Acceptance Criteria and Reported Device Performance

The document does not present a table of specific quantitative acceptance criteria for the overall performance of the SNAP device (e.g., accuracy of segmentation, simulation fidelity). Instead, the acceptance criteria relate to regulatory compliance and the successful execution of specific tests for the modifications.

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

• Sample Size: The document does not specify a distinct "test set" sample size in terms of patient cases or imaging studies for the performance validation. The testing described largely pertains to software verification, EMC, and risk management. The "DICOM data acceptance" for XA scans implies testing with various DICOM files, but a specific number is not provided.
• Data Provenance: Not explicitly stated. The focus is on the software and hardware modifications rather than clinical data.

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

• Number of Experts & Qualifications: Not mentioned in the provided text. The evaluation is focused on technical compliance and validation by the manufacturer, not a clinical ground truth assessment by external experts.

4. Adjudication Method for the Test Set

• Adjudication Method: Not applicable or mentioned. The described testing is technical validation and verification, not a clinical study requiring adjudication of outcomes or interpretations.

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

• MRMC Study: No, an MRMC comparative effectiveness study is not described in the provided text. The submission is for substantial equivalence of device modifications, not a study evaluating human reader performance with or without AI assistance.

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

• Standalone Performance: The text does not describe a standalone performance study in the context of an algorithm's diagnostic or predictive capability. The SNAP device is described as a "software interface and image segmentation system" intended for simulation and surgical planning, interacting with a human surgeon. Its performance evaluation focuses on the accurate processing and display of data for human interpretation and use.

7. The Type of Ground Truth Used

• Type of Ground Truth: For the "DICOM data acceptance" related to XA scans, the ground truth would be adherence to the DICOM standard and the internal specifications for image modality and SOP Class. For software verification and validation, the ground truth is the predefined functional and performance requirements established by the manufacturer, verified through testing against those specifications (e.g., whether the software converts the image correctly, whether the display is accurate). There isn't a clinical "ground truth" (like pathology or outcomes data) discussed for this submission.

8. The Sample Size for the Training Set

• Training Set Sample Size: Not applicable or mentioned in the provided text. The SNAP device is described as an "image segmentation system," but the submission does not detail any machine learning or AI algorithm training that would require a distinct training set. The focus is on functionality and safety of the system as a whole.

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

• Ground Truth for Training Set: Not applicable, as no training set for a machine learning algorithm is discussed.

Summary of Scope:

The provided document (K160584) describes a 510(k) submission for modifications to an existing cleared device. The "performance data" presented are primarily focused on demonstrating that these modifications do not introduce new safety or effectiveness concerns and that the modified device remains in compliance with relevant technical standards (EMC) and internal software verification and validation processes. It is a regulatory submission for substantial equivalence, not a detailed clinical study report on the device's diagnostic or therapeutic performance.

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The Surgical Theater, LLC SNAP is intended for use as a software interface and image segmentation system for the transfer of imaging information from a CT or MR medical scanner to an output file. It is also intended for use in simulating and evaluating surgical treatment options both pre-operatively and intra- operatively with validated systems as identified in the device labeling.

The SNAP is software based medical image management system. It is intended for use as a software interface and image segmentation system for the transfer of imaging information from a CT or MR medical scanner to an output file. It is also intended for use in simulating and evaluating surgical treatment options both pre-operatively and intraoperatively with validated systems as identified in the device labeling. The SNAP system is based on the Surgical Theater Surgery Rehearsal Platform (SRP) image management system. The SNAP utilizes the same identical software as the SRP to create 3D models of patient data from 2D scan slices. This provides the user with ability to input, display, color, and manipulate the 2D scan slices via a 3D representation. The SNAP system enhances the SRP's capability by adding additional input and allowing the surgeon to connect to an external Image Guided System and Navigation systems (in general: "IGS"; for example Brainlab Kolibri or Brainlab Curve), and to see the incoming navigation data in the SNAP monitor. The incoming navigation data is then displayed to the surgeon inside the generated 3D model, so the surgeon gets a 3D representation of his surgery navigation session.

The provided document K140819 for the Surgical Navigation Advanced Platform (SNAP) does not contain a specific table of acceptance criteria with numerical performance targets or a detailed study report demonstrating how these criteria were met. Instead, it states that "Verification and validation results confirm that the SNAP Software meets its' performance requirements."

However, based on the information provided, we can infer the acceptance criteria for the device's functionality and its proven performance:

1. Table of Acceptance Criteria and Reported Device Performance:

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

• Sample Size for Test Set: The document states that the SNAP was "validated by two neurosurgeons based on historical DIOCM cases (of patients' cases who had their surgeries done in the past)." The specific number of DICOM cases used is not mentioned in the provided text.
• Data Provenance: The data used consisted of "historical DIOCM cases (of patients' cases who had their surgeries done in the past)." This indicates the data was retrospective. The country of origin is not specified.

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

• Number of Experts: Two neurosurgeons were used for validation.
• Qualifications of Experts: They are identified as "neurosurgeons," implying medical expertise relevant to the device's application. Specific years of experience or other detailed qualifications are not provided.

4. Adjudication method for the test set:

• The document states that the device was "validated by two neurosurgeons." It does not specify an adjudication method (e.g., 2+1, 3+1). It is only mentioned that they performed the validation.

5. If a multi-reader multi-case (MRMC) comparative effectiveness study was done, and if so, what was the effect size of how much human readers improve with AI vs without AI assistance:

• No MRMC comparative effectiveness study was done comparing human reader performance with and without AI assistance (or in this case, SNAP assistance). The SNAP is described as an image management and navigation assistance tool, not an AI diagnostic tool primarily aimed at improving human reader diagnostic performance. The validation focused on the software's functional correctness and suitability for use, particularly the new intra-operative navigation feature. There is no mention of an effect size related to human improvement with assistance.

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

• Yes, a standalone performance assessment was conducted in the sense that the software underwent internal "full testing, verification, and validation by Surgical Theater as part of its' own internal design control requirements" to confirm it meets its performance requirements. The subsequent validation by neurosurgeons assessed the usability and correctness of its output for clinical use, but the core functionality and technical performance were first verified in a standalone manner.

7. The type of ground truth used:

• The ground truth was implicitly derived from the "historical DIOCM cases (of patients' cases who had their surgeries done in the past)." This suggests that the ground truth for validating the 3D reconstructions and navigation display was based on the known anatomy and surgical outcomes of these historical cases, likely interpreted by the validating neurosurgeons. It is most akin to expert consensus/clinical data derived from previously treated cases.

8. The sample size for the training set:

• The document does not specify a sample size for a training set. The device is described as software that reconstructs 3D models from DICOM data based on algorithms, rather than a machine-learning model that would typically require a distinct training set. The SNAP system "utilizes the same identical software as the SRP to create 3D models."

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

• As the document does not mention a distinct training set in the context of machine learning, there is no information provided on how ground truth for a training set was established. The software's underlying algorithms for 3D reconstruction and visualization likely rely on established medical image processing principles and were developed and refined through engineering and standard software development practices, rather than by training on a labeled dataset in the modern AI sense.

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