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Vitrea® is a medical diagnostic system that allows the processing, review, analysis, communication and media interchange of multi-dimensional digital images acquired from a variety of imaging devices. Vitrea is not meant for primary image interpretation in mammography. In addition, Vitrea has the following additional indication:

The Endovascular Stent Planning Software application is intended for use with CT (computed tomography) images to assist medical professionals in the analysis, treatment and follow-up of aortic vascular disorders that may require a stent procedure. The software provides 3D segmentation of the aorta and initializes stent measurements based on a template provided by the stent manufacturers. The user can review the 2D and 3D images, verify and correct the results of the segmentation and initialization, and generate a report with the stent measurements.

The Endovascular Stent Planning Software, an application module for the Vitrea software, enables visualization and measurements of the aortic vessel for evaluation, treatment and follow up for aortic vascular disorders that may require a stent procedure. It has the following features:

• Automated segmentation of the aortic vessel, including thrombus .
• Custom and manufacturer-recommended stent-graft worksheets and report . templates for stent sizing
• Automatic initialization of endovascular measurements (diameters, lengths, . angles, volumes) based on the stent template selection, with easy centerline and contour editing tools
• Workflows for pre-treatment (stent, surgery) planning and post-treatment . follow up, including a side-by-side comparative review layout

The provided document does not contain details about specific acceptance criteria, a study proving the device meets those criteria, or quantitative performance metrics typically found in clinical validation studies for medical devices. The document is a 510(k) summary for the "Endovascular Stent Planning Software," primarily focusing on its substantial equivalence to predicate devices.

However, based on the provided text, I can infer the general nature of the "acceptance criteria" and the "study" conducted.

Inferred Acceptance Criteria:

The acceptance criteria appear to be implicitly related to the software functioning as intended, providing accurate segmentation and measurements, and not introducing new safety or effectiveness concerns compared to predicate devices. Specific quantitative metrics are not provided.

Inferred Study:

The "study" described is primarily focused on software verification and validation testing, rather than a clinical performance study with human subjects.

Here's a breakdown of the requested information based on the provided text, with many fields being "Not Applicable" or "Not Provided" due to the nature of the submission:

1. Table of Acceptance Criteria and Reported Device Performance

2. Sample Size Used for the Test Set and Data Provenance

• Sample Size: Not specified. The "test set" likely refers to data used for internal software testing. There's no mention of a patient-specific dataset size.
• Data Provenance: Not specified.

3. Number of Experts Used to Establish Ground Truth for the Test Set and Qualifications of Those Experts

• Number of Experts: Not specified.
• Qualifications of Experts: Not specified. Given this was a software validation, ground truth might have been established by engineering specifications or clinical experts during development, but details are not provided.

4. Adjudication Method for the Test Set

• Adjudication Method: Not specified.

5. If a Multi-Reader Multi-Case (MRMC) Comparative Effectiveness Study Was Done

• MRMC Study: No, an MRMC comparative effectiveness study was not described or referenced. The submission focuses on substantial equivalence through technological comparison and software validation, not clinical efficacy with human readers.
• Effect Size of Human Readers Improve with AI vs. Without AI Assistance: Not applicable, as no such study was performed or reported. The device is an "assistance" tool, but its impact on human reader performance was not quantified in this submission.

6. If a Standalone (i.e., algorithm only without human-in-the-loop performance) Was Done

• Standalone Performance: The document describes the device's capabilities, such as "Automated segmentation of the aortic vessel" and "Automatic initialization of endovascular measurements." The user "can review... verify and correct the results." This implies there is an automated component (standalone algorithm) whose output serves as a starting point for the human user. However, no specific standalone performance metrics (e.g., accuracy of automated segmentation or initial measurements without correction) are provided. The "study" was about software functionality.

7. The Type of Ground Truth Used

• Type of Ground Truth: Not explicitly stated. For software testing, ground truth would likely be defined by:
  • Engineering specifications/requirements: To verify that the software outputs match predefined specifications for segmentation, measurement initialization, and report generation.
  • Potentially, expert review: To confirm that the automated outputs (segmentation, initial measurements) are clinically reasonable and align with expert understanding, before user correction. However, this is inferred, not explicit.

8. The Sample Size for the Training Set

• Sample Size (Training Set): Not applicable or not specified. This document describes a software that performs image processing and measurement initialization. It's not explicitly stated as a machine learning or AI-driven system that requires a distinct "training set" in the modern sense. While it has "automated segmentation," the methodology (e.g., rule-based, traditional image processing, or early AI) and any associated training data are not detailed.

9. How the Ground Truth for the Training Set Was Established

• Ground Truth Establishment (Training Set): Not applicable or not specified, as a distinct training set for an AI model is not mentioned. If the "automated segmentation" involves machine learning, the method for establishing ground truth for any potential training data is not described.

Summary of Device and Evidence:

The "Endovascular Stent Planning Software" provides tools for visualization and measurement of aortic vessels for stent planning. The primary evidence presented in this 510(k) summary for its "acceptance" is that it underwent software verification and validation testing to ensure it functions according to requirements, interacts without negatively impacting existing software functionality, and performs as intended without unacceptable risks. The submission relies heavily on demonstrating substantial equivalence to previously cleared predicate devices, suggesting that the technological characteristics and intended use are similar enough that no new clinical performance studies were deemed necessary to establish safety and effectiveness for a regulatory decision.

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