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Innova EPVision 2.0 software application is 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.

Innova EPVision 2.0 software application is intended to enable users to load, overlay and register in real time 3D datasets with radioscopic or radiographic images of the same anatomy. Electrophysiological signal information is imported and used to color-code these 3D datasets in order to support catheter/device during cardiac electrophysiology guidance interventional procedures.

Innova EPVision 2.0 is the new version of Innova EPVision software, which is part of the Innova Vision Applications [K092639] software. Innova EPVision 2.0, as all Innova Vision Applications image processing algorithms, is executed on a hardware called Advantage platform Workstation (AW) [K110834].
It can perform the following functions:

• Superimpose the segmented DICOM 3D XA, CT, MR dataset on radioscopic or radiographic image of the same anatomy, obtained on an Innova Fluoroscopic X-ray system [K113034].
• . Register the segmented DICOM 3D XA. CT. MR dataset with radioscopic or radiographic images obtained on an Innova Fluoroscopic X-ray system for interventional procedures.
• . Image stabilization features such as ECG gated display or motion tracking in the image.
• . Capability to load planning data, deposited on the 3D model in Volume Viewer [K041521], such as 3D landmarks, ablations lines, and to display them on the 3D-2D fused image to support the physician during procedures.
• . Marking points of interest of different size and color during the procedures.
• . The frequently used functions are also available from tableside on the Innova Central Touch Screen to provide efficient workflow during the interventional procedures.

Innova EPVision 2.0 can perform additionally the following functions:

• Import electrophysiology (EP) data digitized and processed . on the CardioLab system [K111200] and use them to colorcode EP recording points on 3D model of the visualized anatomy in order to support catheter/device guidance during cardiac electrophysiology interventional procedures.
• . Catheter tip detection to help locate the catheter tip on the 2D X-ray image. The user can modify or correct the automatically proposed tip location anytime.
• . 3D side viewer allowing the user to freely rotate the 3D model independently from the Fluoro image and the gantry angulation.

Innova EPVision 2.0, as Innova EPVision targets clinical indication for interventional cardiology procedures and in particular cardiac electrophysiology procedures.

Here's an analysis of the provided text regarding the acceptance criteria and study for the Innova EPVision 2.0 device:

1. Table of Acceptance Criteria and Reported Device Performance

The provided 510(k) summary does not explicitly state specific quantitative acceptance criteria or detailed performance metrics for the Innova EPVision 2.0. Instead, it relies on demonstrating substantial equivalence to a predicate device (K092639, Innova Vision Applications) and adherence to various standards (NEMA PS 3.1 - 3.20 (2011) DICOM Set, IEC 60601-1-4 (2000), IEC 62304 (2006), IEC 62366 (2007)).

The "reported device performance" is described in terms of verification and validation activities designed to ensure the device works as required and meets user needs and intended use.

Summary of Device Performance (as described):

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

The document does not specify a "test set" in the context of clinical data for performance evaluation. Instead, it discusses verification and validation tests as part of the software development lifecycle. These tests would involve a variety of inputs and scenarios, but the exact sample sizes (e.g., number of test cases, number of images) for these engineering tests are not provided.

There is no mention of data provenance (e.g., country of origin, retrospective/prospective) because no clinical studies were deemed necessary or performed to support substantial equivalence.

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

This information is not applicable or provided as no clinical studies were performed that would require expert-established ground truth on a test set. The ground truth for the engineering verification and validation tests would be the expected behavior of the software as defined by its requirements and design specifications, not clinical expert consensus.

4. Adjudication method for the test set

This information is not applicable or provided since no clinical validation study involving adjudication of a test set was performed.

5. If a multi-reader multi-case (MRMC) comparative effectiveness study was done, 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. The submission explicitly states: "The subject of this premarket submission, Innova EPVision 2.0, did not require clinical studies to support substantial equivalence." Therefore, there is no reported effect size of human readers improving with or without AI assistance.

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

The document describes the device as a software application that "enables users to load, overlay and register... in order to support catheter/device guidance." It also mentions "Catheter tip detection to help locate the catheter tip on the 2D X-ray image. The user can modify or correct the automatically proposed tip location anytime." This clearly indicates a human-in-the-loop design.

While the "Catheter tip detection" component could have an internal standalone performance evaluation during development, the submission does not report a standalone (algorithm only) performance study in the context of a regulatory submission outcome. The overall device is intended to be used with a human interventionalist.

7. The type of ground truth used (expert consensus, pathology, outcomes data, etc.)

For the engineering verification and validation tests, the ground truth would be the expected software behavior and functionality based on design input (system requirements and specifications). For example, a test for "Superimpose the segmented DICOM 3D XA, CT, MR dataset on radioscopic or radiographic image" would have a ground truth defined by the correctness of the overlay as per the design specifications. There is no mention of clinical ground truth (expert consensus, pathology, or outcomes data) being used for the regulatory submission's performance evaluation because no clinical studies were conducted.

8. The sample size for the training set

This information is not provided. The document makes no mention of machine learning model training or a training set. The device is described as inheriting functions and employing the "same fundamental scientific technology" as its predicate, implying a rule-based or traditional image processing approach rather than a machine learning approach requiring distinct training data that would be relevant to this 510(k) submission.

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

This information is not provided as no training set is mentioned in the context of this 510(k) submission.

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