Artificial intelligence-powered software outperforms interventional cardiologists in assessment of IVUS-based stent
Pablo M Rubio1, Hector M Garcia-Garcia1, Jason Galo1
1Interventional Cardiology, MedStar Washington Hospital Center, Washington, DC, USA.
Background:
Optimal stent deployment assessed by intravascular ultrasound (IVUS) is associated with improved outcomes after percutaneous coronary intervention (PCI). However, IVUS remains underutilized due to its time-consuming analysis and reliance on operator expertise. AVVIGO™+, an FDA-approved artificial intelligence (AI) software, offers automated lesion assessment, but its performance for stent evaluation has not been thoroughly investigated.
Aim:
To assess whether an artificial intelligence-powered software (AVVIGO™+) provides a superior evaluation of IVUS-based stent expansion index (%Stent expansion = Minimum Stent Area (MSA) / Distal reference lumen area) and geographic miss (i.e. >50 % plaque burden - PB - at stent edges) compared to the current gold standard method performed by interventional cardiologists (IC), defined as frame-by-frame visual assessment by interventional cardiologists, selecting the MSA and the reference frame with the largest lumen area within 5 mm of the stent edge, following expert consensus.
Methods:
This retrospective study included 60 patients (47,997 IVUS frames) who underwent IVUS guided PCI, independently analyzed by IC and AVVIGO™+. Assessments included minimum stent area (MSA), stent expansion index, and PB at proximal and distal reference segments. For expansion, a threshold of 80 % was used to define suboptimal results. The time required for expansion analysis was recorded for both methods. Concordance, absolute and relative differences were evaluated.
Results:
AVVIGO™ + consistently identified lower mean expansion (70.3 %) vs. IC (91.2 %), (p < 0.0001), primarily due to detecting frames with smaller MSA values (5.94 vs. 7.19 mm2, p = 0.0053). This led to 25 discordant cases in which AVVIGO™ + reported suboptimal expansion while IC classified the result as adequate. The analysis time was significantly shorter with AVVIGO™ + (0.76 ± 0.39 min) vs IC (1.89 ± 0.62 min) (p < 0.0001), representing a 59.7 % reduction. For geographic miss, AVVIGO™ + reported higher PB than IC at both distal (51.8 % vs. 43.0 %, p < 0.0001) and proximal (50.0 % vs. 43.0 %, p = 0.0083) segments. When applying the 50 % PB threshold, AVVIGO™ + identified PB ≥50 % not seen by IC in 12 cases (6 distal, 6 proximal).
Conclusion:
AVVIGO™ + demonstrated improved detection of suboptimal stent expansion and geographic miss compared to interventional cardiologists, while also significantly reducing analysis time. These findings suggest that AI-based platforms may offer a more reliable and efficient approach to IVUS-guided stent optimization, with potential to enhance consistency in clinical practice.
