Automatic assessment of collaterals physiology in chronic total occlusions by means of artificial intelligence.

ABSTRACT

BACKGROUND: Assessment of collaterals physiology in chronic total occlusions (CTO) currently requires dedicated devices, adds complexity, and increases the cost of the intervention. This study sought to derive collaterals physiology from flow velocity changes (ΔV) in donor arteries, calculated with artificial intelligence- aided angiography. METHODS: Angiographies with successful percutaneous coronary intervention (PCI) in 2 centers were retro- spectively analyzed. CTO collaterals were angiographically evaluated according to Rentrop and collateral connections (CC) classifications. Flow velocities in the primary and secondary collateral donor arteries (PCDA, SCDA) were automatically computed pre and post PCI, based on a novel deep-learning model to extract the length/time curve of the coronary filling in angiography. Parameters of collaterals physiology, Δcollateral-flow (Δfcoll) and Δcollateral-flow-index (ΔCFI), were derived from the ΔV pre-post. RESULTS: The analysis was feasible in 105 out of 130 patients. Flow velocity in the PCDA significantly decreased after CTO-PCI, proportionally to the angiographic collateral grading (Rentrop 1 0.02 ± 0.01 m/s; Rentrop 2 0.04 ± 0.01 m/s; Rentrop 3 0.07 ± 0.02 m/s; p < 0.001; CC0 0.01 ± 0.01 m/s; CC1 0.04 ± ± 0.02 m/s; CC2 0.06 ± 0.02 m/s; p < 0.001). Δfcoll and ΔCFI paralleled ΔV. SCDA also showed a greater reduction in flow velocity if its collateral channels were CC1 vs. CC0 (0.03 ± 0.01 vs. 0.01 ± 0.01 m/s; p < 0.001). For each individual patient, ΔV was more pronounced in the PCDA than in the SCDA. CONCLUSIONS: Automatic assessment of collaterals physiology in CTO is feasible, based on a deeplearning model analyzing the filling of the donor vessels in angiography. The changes in collateral flow with this novel method are quantitatively proportional to the angiographic grading of the collaterals.