(72 days)
The InstaTrak 3500 Plus System (K983529, originally cleared under the product name InstaTrak 3000) is intended as an aid to the surgeon for precisely locating anatomical structures anywhere on the human body during either open or percutaneous procedures. It is indicated for any medical condition that may benefit from the use of stereotactic surgery and which provides a reference to rigid anatomical structures such as sinus, skull, long bone, or vertebra, visible on medical images such as CT, MR, or X-ray.
The InstaTrak system with Multiple Dataset Navigation provides the same capability as the existing system, with the additional functionality of utilizing two sets of medical images instead of one. By providing information from multiple datasets, the user can locate and visualize anatomical structures using different imaging modalities. The InstaTrak 3500 Plus System allows the user to view the medical images of the patient's anatomy in response to the mouse or the tracked surgical instrument. Alignment of the patient and medical images is accomplished through the registration process. In all types of surgery the goal is the same, to display to the surgeon based on the medical images, where the position of a tracked surgical tool is with regard to the patient's anatomy. With the additional capability of multiple dataset navigation, the surgeon can now view the position of the tracked instrument using two sets of medical images instead of one. The Multiple Dataset Navigation will provide the user with the ability to co-reqister (fuse) images from multiple datasets such as CT and MR. Using the existing InstaTrak 3500 Plus System software, the user will register one of the datasets, referred to as the Reference Dataset, to the patient. Navigation is then possible on the fused images, with secondary (registered) dataset(s) acting as a visualization enhancement for both surgical planning and intra-operative quidance. The sensors and instruments used for navigation are identical to those utilized by the existing InstaTrak system. Navigation will be disabled until the datasets have been successfully co-registered. Patient registration is the process by which the coordinate systems of the medical images and the pointing instrument are aligned. This is performed on the primary (reference) dataset. Both the method of registration on the primary (reference) dataset and the resulting accuracy are identical to that described in K983529. The current system provides displays for a single set of medical images. The Multiple Dataset Navigation option will provide displays for multiple sets of medical images. The addition of the Multiple Dataset Navigation operating mode does not change any of the major components of the InstaTrak System. There are no new receivers, transmitters, or instrument attachment configurations associated with this operational mode. Addition of the Multiple Dataset Navigation mode is a software change only.
The provided 510(k) summary for the GE Medical Systems Navigation and Visualization InstaTrak 3500 Plus with Multiple Dataset Navigation does not contain the specific information requested about acceptance criteria, device performance tables, sample sizes, ground truth establishment, or study designs (MRMC, standalone).
The document is a 510(k) summary, which focuses on demonstrating substantial equivalence to a predicate device rather than providing a detailed technical description of performance validation studies.
Here's what can be extracted based on the limitations of the provided text:
1. Table of Acceptance Criteria and Reported Device Performance:
The document does not explicitly state acceptance criteria or provide a table of reported device performance metrics in the way typically seen for diagnostic or AI algorithms (e.g., sensitivity, specificity, accuracy). Instead, it focuses on the equivalence of its registration process and navigation capabilities to existing systems.
2. Sample Size Used for the Test Set and Data Provenance:
This information is not provided in the 510(k) summary. The document states that the patient registration method and resulting accuracy for the primary (reference) dataset are "identical to that described in K983529." To find any details about testing or sample sizes, one would need to refer to the K983529 submission, which is not included here.
3. Number of Experts Used to Establish the Ground Truth for the Test Set and Qualifications:
This information is not provided in the 510(k) summary. Given the device's function as an image-guided surgical system for localization, ground truth would likely involve physical measurements or intraoperative verification, rather than expert interpretation of images for diagnosis.
4. Adjudication Method for the Test Set:
This information is not provided.
5. If a Multi-Reader Multi-Case (MRMC) Comparative Effectiveness Study Was Done, and Effect Size:
This type of study is typically conducted for diagnostic devices where human readers interpret images. For an image-guided surgical navigation system that aids in locating anatomical structures, an MRMC study in the traditional sense would likely not be relevant or performed. The document focuses on the technical capability of fusing multiple datasets and the accuracy of registration as being equivalent to a predicate. It does not assess the diagnostic performance of human readers with or without AI assistance.
6. If a Standalone (Algorithm Only Without Human-in-the-Loop Performance) Was Done:
The document describes the "Multiple Dataset Navigation" as a software change that provides the "ability to co-register (fuse) images from multiple datasets." The navigation is then "possible on the fused images." The core function described relates to the accuracy of registration. The document states: "Both the method of registration on the primary (reference) dataset and the resulting accuracy are identical to that described in K983529." This implies that the accuracy of the underlying registration algorithm (a standalone component) would have been assessed as part of the original K983529 submission. However, specific details of that assessment are not provided in this summary.
7. The Type of Ground Truth Used:
For the core function of image registration, ground truth would typically involve physical phantoms with known fiducial markers, where the true alignment is precisely measurable. For clinical application, ground truth for actual navigation accuracy is often derived from intraoperative verification using physical measurements or comparison to anatomical landmarks during surgery. The document itself does not specify the type of ground truth used for the claim of "identical accuracy" to K983529.
8. The Sample Size for the Training Set:
This information is not provided. As this device predates the widespread use of deep learning, the concept of a "training set" in the modern AI sense might not apply. The software change focuses on a new capability (multiple dataset navigation) building upon an existing, validated system.
9. How the Ground Truth for the Training Set Was Established:
This information is not provided.
Summary of Device and Evidence Presented in Document:
The 510(k) summary describes the "InstaTrak 3500 Plus with Multiple Dataset Navigation" as an upgrade (software change only) to an existing image-guided surgical system (InstaTrak 3500 Plus, K983529). The primary new functionality is the ability to co-register (fuse) and navigate using two sets of medical images (e.g., CT and MR) instead of one.
The document claims substantial equivalence to predicate devices (BrainLAB's Vectorvision iPlan and Medtronic's StealthStation with StealthMerge) that also offer multiple dataset fusion.
The key statement regarding performance is:
"Patient registration is the process by which the coordinate systems of the medical images and the pointing instrument are aligned. This is performed on the primary (reference) dataset. Both the method of registration on the primary (reference) dataset and the resulting accuracy are identical to that described in K983529."
This statement implicitly argues that because the underlying patient registration method and its accuracy are unchanged from the previously cleared device (K983529), and the new multiple dataset navigation builds upon this existing, validated platform, no new safety or effectiveness concerns are raised. The validation for the core accuracy would therefore refer back to the K983529 submission, which is not detailed here.
Therefore, this document does not present a standalone study with new acceptance criteria and performance data for this particular software upgrade, but rather leverages the established performance of the base system and the equivalence to predicate devices for the new functionality.
§ 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).