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The AngioCloud Service enables visualization and measurement of cerebral blood vessels for preoperational planning and sizing for neurovascular interventions and surgery.

General functionalities are provided such as:

• Segmentation of neurovascular structures
• · Centerline calculation
• · Visualization of 3D vascular images
• · Measurement and annotation tools
• · Case sharing and 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 AngioCloud Service is a standalone web-application which intends to receive a 3D rotational angiography (3D-RA) DICOM dataset and provide interactive views to physicians for the visualization and measurement of cerebral vasculatures during preoperational planning. Physicians can query a 3D-RA dataset directly via the AngioCloud Service provided that they have an internet browser with access to the internet. General functionalities are provided such as:

• Segmentation of neurovascular structures ●
• . Centerline calculation
• Visualization of 3D vascular images
• . Measurement and annotation tools
• Case sharing and reporting tools

The AngioCloud Service runs as a web application on a standard Windows or Mac OS X based computer and can also be accessed on mobile web browsers, but with limited software functionalities enabled. The AngioCloud Service does not use any artificial intelligence or machine learning functionality and the main segmentation algorithm is based on level-set methods. The Visualization Toolkit (VTK) and Vascular Model Toolkit (VMTK) serve as important software libraries that the underlying algorithm of AngioCloud Service leverages for a number of computational operations such as 3D segmentation, geometric analysis, mesh generation, and surface data analysis for image-based modeling of blood vessels. The device does not contact the patient nor does it control any life-sustaining devices. Information provided by the AngioCloud Service 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 AngioCloud Service is a web application designed for the visualization and measurement of cerebral blood vessels for preoperational planning and sizing for neurovascular interventions and surgery. It includes functionalities such as segmentation of neurovascular structures, centerline calculation, visualization of 3D vascular images, measurement and annotation tools, and case sharing and reporting tools. The device does not use artificial intelligence or machine learning. The following describes the acceptance criteria and the study proving the device meets these criteria.

Acceptance Criteria and Reported Device Performance

The performance testing was conducted primarily to evaluate the AngioCloud Service's segmentation module. The acceptance criteria were based on two metrics: Hausdorff Distance (dH) and Dice Coefficient (DC). These metrics were used to compare the segmentations performed by the AngioCloud Service against a reference standard. While specific numeric acceptance thresholds for dH and DC are not provided in the document, the conclusion states that "Both the mean DC and mean dH for each group met the acceptance criteria."

Study Details

1. Sample Size and Data Provenance:

   • Test Set Sample Size: The document states that "Anatomically relevant phantom models derived from clinical scans were acquired using Siemens (Artis Q with PURE) and Phillips (Allura Xper FD 20/20) Angio suites." The exact number of phantom models or clinical scans used to derive them is not specified.
   • Data Provenance: The data used for the test set were "phantom models derived from clinical scans" acquired using Siemens and Phillips Angio suites. The country of origin and whether the data was retrospective or prospective are not explicitly stated. However, the use of "phantom models" implies a controlled, non-patient-specific testing environment, likely based on retrospective clinical data used for model creation.

2. 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 or their qualifications.

3. Adjudication Method for the Test Set:

   • The document does not specify any adjudication method used for the test set.

4. Multi-Reader Multi-Case (MRMC) Comparative Effectiveness Study:

   • No MRMC comparative effectiveness study was mentioned. The device does not utilize AI/ML, and the testing focused on the performance of its segmentation module against a reference standard, not on human reader improvement with AI assistance.

5. Standalone Performance:

   • Yes, a standalone performance evaluation was conducted for the segmentation module. The Hausdorff distance (dH) and DICE coefficient (DC) were used to evaluate the algorithm's segmentation performance against a reference standard. The study assesses the device's inherent functionality without human-in-the-loop performance being a primary measure. Human interaction is for adjusting the segmentation threshold ("manual adjust the segmentation threshold" or "Auto" option), but the core performance evaluation metrics (dH, DC) are for the algorithm's output relative to the ground truth.

6. Type of Ground Truth Used:

   • The ground truth for the segmentation performance evaluation was based on a "reference standard" from "anatomically relevant phantom models derived from clinical scans." This implies a highly accurate or 'gold standard' segmentation for these phantom models, likely established through precise measurements or expert consensus on the phantom data, rather than direct pathology or outcomes data from live patients.

7. Training Set Sample Size:

   • The document states that the AngioCloud Service "does not use any artificial intelligence or machine learning functionality." Therefore, there is no "training set" in the context of machine learning model development. The segmentation algorithm is based on "level-set methods" and leverages "The Visualization Toolkit (VTK) and Vascular Model Toolkit (VMTK)."

8. Ground Truth Establishment for Training Set:

   • As there is no AI/ML component, there is no "training set" with ground truth established in the machine learning sense. The underlying algorithms (level-set methods, VTK, VMTK) are established scientific and engineering tools, not trained models requiring annotated ground truth data for learning.

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