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AneuGuide enables visualization of intracranial vessels for preoperational planning and sizing for neurovascular interventions. AneuGuide also allows for the ability to computationally model the placement of neurointerventional devices. General functionalities are provided such as:

• Segmentation of neurovascular structures .
• Automatic centerline detection
• . Visualization of X-ray based images for 2D review and 3D reconstruction
• . Placing and sizing tools
• Reporting tools

Information provided by the software is not intended in any way to eliminate, replace or substitute for, in whole or in part, the healthcare provider's judgment and analysis of the patient's condition.

The AneuGuide software is a medical device intended to provide a 3D view of the final placement of implants. It uses an image of the patient produced by 3D rotational angiography. It offers clinicians the possibility of computationally modeling the flow diverters (FD) in the artery to be treated through endovascular surgery.

AneuGuide is intended to import DICOM images and to provide a 3D reconstruction of the vascular tree in the surgical area. Also, it allows to pre-operationally estimate the size of flow diverter devices.

AneuGuide is composed of the following analysis workflows: image loading, selection of the volume of interest (VOI), segmentation threshold adjustment, reconstruction, selection of the region of interest (ROI), selection of the vessel inlet, generation of centerline, initializing the flow diverter, and sizing the flow diverter.

The flow diverter supported by the software is the Pipeline Flex Embolization Device (Micro Therapeutics, Inc. d/b/a ev3 Neurovascular, PMA: P100018/S015), which is an FDA-approved neurointerventional device. AneuGuide software has a "moderate" level of concern. It is intended only for preoperational planning. It is not intended for diagnosis.

The ArteryFlow Technology AneuGuide software is a medical device intended for preoperational planning of neurovascular interventions, specifically for visualizing intracranial vessels and computationally modeling the placement of neurointerventional devices like flow diverters.

Here's a breakdown of the acceptance criteria and the study that proves the device meets them:

1. Acceptance Criteria and Reported Device Performance

The document does not explicitly present a table of "acceptance criteria" with specific quantitative thresholds. Instead, it describes general performance tests and a validation study for a key functionality: calculating the deployed length of a flow diverter. The overall acceptance criterion is implied to be that the software functions as intended, and for the flow diverter deployment, that its simulated length is sufficiently accurate compared to real-world implantation.

2. Sample Size and Data Provenance

• Test Set Sample Size: The document mentions two performance tests for computational modeling:
  • Silicone Phantoms: "Using silicone phantoms representative of patients presenting with intracranial aneurysms." The exact number of phantoms is not specified.
  • Patient Implantation Data: "Validation study of the AneuGuide performance comparing the simulated deployed length of the Pipeline Flex Embolization Device with its implanted length in patients with intracranial aneurysms." The exact number of patients or cases is not specified.
• Data Provenance: The document does not specify the country of origin for the patient data used in the validation study. It also does not explicitly state whether the patient data was collected retrospectively or prospectively. Given the context of comparing simulated vs. implanted lengths, it strongly implies retrospective analysis of existing patient implant data.

3. Number of Experts and Qualifications for Ground Truth

The document does not specify the number of experts used to establish the ground truth for the test set, nor does it detail their specific qualifications (e.g., radiologist with X years of experience). The general description implies that the "implanted length" from patients would serve as a real-world ground truth, presumably measured by clinical professionals.

4. Adjudication Method for the Test Set

The document does not mention any formal adjudication method (e.g., 2+1, 3+1) for establishing ground truth for the test set.

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

An MRMC study was not explicitly done or reported. The performance tests described focus on the software's capability to accurately model physical parameters (flow diverter length) rather than evaluating human reader performance with or without AI assistance. The device is for "preoperational planning" and "not intended in any way to eliminate, replace or substitute for...the healthcare provider's judgment." This suggests it's a tool for the human, not an AI to be compared against human readers for diagnostic accuracy.

6. Standalone (Algorithm Only) Performance

Yes, the described "Performance Testing - Bench" section primarily focuses on the standalone (algorithm only) performance of AneuGuide in calculating the deployed length of the Pipeline Flex Embolization Device. The tests involved comparing the software's simulated output against physical measurements (from silicone phantoms and implanted patient data).

7. Type of Ground Truth Used

The type of ground truth used for the computational modeling performance evaluation appears to be:

• Physical Measurements/Knowns: For the silicone phantom study, the "in vitro" placement is likely based on precise physical measurements of the actual flow diverter deployment in the phantoms.
• Outcomes Data/Clinical Measurement: For the patient study, the "implanted length" of the flow diverter is derived from real-world patient data, presumably measured from post-implantation imaging or surgical records. This leans towards clinical outcomes/measurements as ground truth.

8. Sample Size for the Training Set

The document does not provide any information regarding the sample size used for the training set of the AneuGuide software.

9. How Ground Truth for Training Set was Established

The document does not provide any information on how ground truth was established for the training set. Given that the software is for "computational modeling" of device placement and not explicit diagnostic AI, it's possible that the "training" (if it involved machine learning) might have used simulated data, or that "training" in this context refers more to the development and calibration of the underlying physical models rather than a typical supervised learning approach with human-labeled ground truth images.

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