(119 days)
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.
| Acceptance Criteria (from text) | Reported Device Performance (from text) |
|---|---|
| DICOM Data Acceptance for XA Scans: 1) Image Modality file type is CT, MR or XA, and 2) Media Storage SOP Class and SOP Class UID are CT or MR (for scans exported as DICOM CT image storage type). | SNAP software verifies DICOM data meets these criteria; otherwise, data is rejected. (Implied: Successfully implemented and functioning as designed for XA support). |
| Electromagnetic Compatibility (EMC): Compliance with IEC 60601-1-2:2007 Third Edition to ensure the use of SNAP in the OR does not adversely affect other devices. (Specifically for the modified device, including the VR headset for intra-operative use). | "EMC evaluation per IEC 60601-1-2:2007 Third Edition was performed by a 3rd party test laboratory on the modified device and the SNAP was found to be in compliance." (No issues identified regarding the VR headset). |
| Software Verification and Validation Testing: Device continues to meet its intended use and performance requirements (for the modified SNAP). Adherence to FDA's "Guidance for Content of Premarket Submissions for Software Contained in Medical Devices" for a "medium" level of concern device. | "The SNAP was fully tested, verified and validated by Surgical Theater... A formal verification and validation test plan was executed to confirm that the modified SNAP continues to meet its intended use and performance requirements." "Verification and validation results demonstrate the modified SNAP is as safe and effective as the predicate SNAP, and performs as intended..." |
| Product Risk Management: Performed in accordance with ISO 14971:2012; risk mitigations are implemented. | "Product risk management activities were performed in accordance with ISO 14971:2012... Risk management verification and validation consisted of both a desk audit and software testing to ensure the implementation of all risk mitigations..." |
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.
§ 892.2050 Medical image management and processing system.
(a)
Identification. A medical image management and processing system is a device that provides one or more capabilities relating to the review and digital processing of medical images for the purposes of interpretation by a trained practitioner of disease detection, diagnosis, or patient management. The software components may provide advanced or complex image processing functions for image manipulation, enhancement, or quantification that are intended for use in the interpretation and analysis of medical images. Advanced image manipulation functions may include image segmentation, multimodality image registration, or 3D visualization. Complex quantitative functions may include semi-automated measurements or time-series measurements.(b)
Classification. Class II (special controls; voluntary standards—Digital Imaging and Communications in Medicine (DICOM) Std., Joint Photographic Experts Group (JPEG) Std., Society of Motion Picture and Television Engineers (SMPTE) Test Pattern).