Search Results

Vision 2, EVARVision, TrackVision 2 and HeartVision 2 software applications are intended to enable users to load 3D datasets and overlay and register in real time these 3D datasets with radioscopic or radiographic images of the same anatomy in order to support catheter/device guidance during interventional procedures.

Structures of interest and estimated dimensions can be overlaid on the radioscopic or radiographic images. Image processing can be applied to enhance the display of such images. This information is intended to assist the physician during interventional procedures.

The Stereo 3D option enables physicians to visualize and localize needles, points, and segments on a 3D model/space using a stereotactic reconstruction of radioscopic or radiographic images at a significantly lower dose than use of a full cone beam CT acquisition. This information is intended to assist the physician during interventional procedures.

Vision Applications are a group of software applications called Vision 2, EVARVision, TrackVision 2 and HeartVision 2 that share the same core functionalities to target different clinical procedures.

Vision Applications load 3D datasets previously acquired from an acquisition modality (CT, MR or CBCT) and prepared with Volume Viewer application [K041521]. They overlay and register in real-time these 3D datasets with the 2D X-ray live images acquired from the GE Interventional X-ray system [K181403] (called IGS X-ray system in the rest of the document) to help support localization and guidance of catheters / devices during interventional procedures, in conjunction with primary images, native live 2D X-ray images.

Vision Applications help physicians to perform interventional procedures by providing enhanced image quality and additional 3D information instead of 2D X-ray live images alone.

Vision Applications operate on GE's Advantage Workstation (AW) [K110834] platform and communicates with the IGS X-ray system [K181403] for receiving the live X-ray images.

The subject device, Vision Applications were developed from modifications to the primary predicate device Innova Vision Applications [K092639], including the addition of new optional feature "Digital Pen". The Digital Pen option is what triggered this 510k and was modified from the reference device, GE's IGS X-ray systems [K181403] under the name Stenosis Analysis. The Vision Applications include also Stereo 3D option feature [K152352, secondary predicate].

The primary features/functionalities of the Vision Applications are:

• . Digital Pen.
• Overlay of 2D/3D images.
• Reception and display of live 2D images and related information. ●
• Loading of 3D datasets.
• Review mode.
• Film/Sequence/photo store.
• . Display controls for Visualization of images: including Zoom/Roam, Rendering, Planning data display, Annotation display, Virtual Collimation, ECG Display, Calcification Visualization Enhancement, display adjustment tools.
• Automatic Registration: including A priori registration and Registration based on Augmented Calibration.
• Manual Registration.
• Bi-view registration.
• User Interface: control from AW and from Tableside.
• . 2D Modes.
• Send Angles: including EVAR Angles, Progress View/Bull's eye.
• . Stereo 3D.

Here's a breakdown of the acceptance criteria and the study that proves the device meets them, based on your provided text:

1. Table of Acceptance Criteria and Reported Device Performance

2. Sample Size for Test Set and Data Provenance

• Sample Size: The text states "a phantom with series of known dimension." It does not provide a specific numerical sample size for the test set.
• Data Provenance: The data was generated through "Engineering bench testing" using a "phantom." This indicates the data is prospective and simulated in a controlled environment, rather than from actual patient clinical data. The country of origin is not explicitly stated for this specific test, but the submitter is GE Medical Systems SCS, located in Buc, France.

3. Number of Experts and Qualifications for Ground Truth

• Number of Experts: The document does not mention the use of experts to establish ground truth for this engineering bench testing. The ground truth for the dimension estimation was based on a "phantom with series of known dimension," implying a pre-defined, objective standard rather than expert consensus on images.

4. Adjudication Method for the Test Set

• Adjudication Method: Not applicable. The ground truth was based on a phantom with known dimensions, not on human interpretation requiring adjudication.

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

• MRMC Study: No, an MRMC comparative effectiveness study was not performed. The text explicitly states, "The subject of this premarket submission, Vision Applications, did not require clinical studies to support substantial equivalence."

6. Standalone (Algorithm Only) Performance Study

• Standalone Study: Yes, the testing described appears to be a standalone performance study of the "Digital Pen option" specifically focusing on its "dimension estimation accuracy." This was performed using "engineering bench testing" with a "phantom," meaning the algorithm's performance was evaluated against known physical dimensions without human-in-the-loop assistance during the measurement evaluation. The overall device, however, is intended to assist human operators.

7. Type of Ground Truth Used

• Type of Ground Truth: For the "Digital Pen option" dimension estimation testing, the ground truth was objective, pre-defined physical dimensions of a phantom ("a phantom with series of known dimension").

8. Sample Size for Training Set

• Sample Size: The document does not provide information about the sample size used for the training set. This is common for submissions if the device is a modification of a predicate and primarily relies on engineering changes and validation rather than a completely new AI model.

9. How Ground Truth for Training Set was Established

• How Ground Truth was Established: The document does not provide information on how ground truth was established for a training set. This suggests that the development likely leveraged existing algorithms or established principles from the predicate devices and the reference device's "Stenosis Analysis" functionality, rather than requiring a new, extensive labeled training dataset for novel AI model development.

Ask a specific question about this device