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AV Vascular is indicated to assist users in the visualization, assessment and quantification of vascular anatomy on CTA and/or MRA datasets, in order to assess patients with suspected or diagnosed vascular pathology and to assist with pre-procedural planning of endovascular interventions.

AV Vascular is a post-processing software application intended for visualization, assessment, and quantification of vessels in computed tomography angiography (CTA) and magnetic resonance angiography (MRA) data with a unified workflow for both modalities.

AV Vascular includes the following functions:

• Advanced visualization: the application provides all relevant views and interactions for CTA and MRA image review: 2D slides, MIP, MPR, curved MPR (cMPR), stretched MPR (sMPR), path-aligned views (cross-sectional and longitudinal MPRs), 3D volume rendering (VR).

• Vessel segmentation: automatic bone removal and vessel segmentation for head/neck and body CTA data, automatic vessel centerline, lumen and outer wall extraction and labeling for the main branches of the vascular anatomy in head/neck and body CTA data, semi-automatic and manual creation of vessel centerline and lumen for CTA and MRA data, interactive two-point vessel centerline extraction and single-point centerline extension.

• Vessel inspection: enable inspection of an entire vessel using the cMPR or sMPR views as well as inspection of a vessel locally using vessel-aligned views (cross-sectional and longitudinal MPRs) by selecting a position along a vessel of interest.

• Measurements: ability to create and save measurements of vessel and lumen inner and outer diameters and area, as well as vessel length and angle measurements.

• Measurements and tools that specifically support pre-procedural planning: manual and automatic ring marker placement for specific anatomical locations, length measurements of the longest and shortest curve along the aortic lumen contour, angle measurements of aortic branches in clock position style, saving viewing angles in C-arm notation, and configurable templated

• Saving and export: saving and export of batch series and customizable reports.

This summarization is based on the provided 510(k) clearance letter for Philips Medical Systems' AV Vascular device.

Acceptance Criteria and Device Performance for Aorto-iliac Outer Wall Segmentation

The reported device performance for all primary and secondary metrics meets the predefined acceptance criteria.

Study Details for Aorto-iliac Outer Wall Segmentation Validation

1. Sample Size used for the Test Set and Data Provenance:

   • Sample Size: 80 patients
   • Data Provenance: Retrospectively collected from 7 clinical sites in the US, 3 European hospitals, and one hospital in Asia.
   • Independence from Training Data: All performance testing datasets were acquired from clinical sites distinct from those which provided the algorithm training data. The algorithm developers had no access to the testing data, ensuring complete independence.
   • Patient Characteristics: At least 80% of patients had thoracic and/or abdominal aortic diseases and/or iliac artery diseases (e.g., thoracic/abdominal aortic aneurysm, ectasia, dissection, and stenosis). At least 20% had been treated with stents.
   • Demographics:
     • Geographics: North America: 58 (72.5%), Europe: 3 (3.75%), Asia: 19 (23.75%)
     • Sex: Male: 59 (73.75%), Female: 21 (26.25%)
     • Age (years): 21-50: 2 (2.50%), 51-70: 31 (38.75%), >71: 45 (56.25%), Not available: 2 (2.5%)

2. Number of Experts Used to Establish Ground Truth for the Test Set and Qualifications:

   • Number of Experts: Three
   • Qualifications: US-board certified radiologists.

3. Adjudication Method for the Test Set:

   • The three US-board certified radiologists independently performed manual contouring of the outer wall along the aorta and iliac arteries on cross-sectional planes for each CT angiographic image.
   • After quality control, these three aortic and iliac arterial outer wall contours were averaged to serve as the reference standard contour. This can be considered a form of consensus/averaging after independent readings.

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

   • The provided document does not indicate that a Multi-Reader Multi-Case (MRMC) comparative effectiveness study was done to measure human reader improvement with AI assistance. The study focused on the standalone performance of the AI algorithm compared to an expert-derived ground truth.

5. Standalone (Algorithm Only Without Human-in-the-Loop Performance):

   • Yes, the performance data provided specifically describes the standalone performance of the AI-based algorithm for aorto-iliac outer wall segmentation. The algorithm's output was compared directly against the reference standard without human intervention in the segmentation process.

6. Type of Ground Truth Used:

   • Expert Consensus/Averaging: The ground truth was established by averaging the independent manual contouring performed by three US-board certified radiologists.

7. Sample Size for the Training Set:

   • The document states that the testing data were independent of the training data and that developers had no access to the testing data. However, the exact sample size for the training set is not specified in the provided text.

8. How the Ground Truth for the Training Set Was Established:

   • The document implies that training data were used, but it does not describe how the ground truth for the training set was established. It only ensures that the testing data did not come from the same clinical sites as the training data and that algorithm developers had no access to the testing data.

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