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Automatic assessment of collaterals physiology in chronic total occlusions by means of artificial intelligence
Lili Liu1, Fenghua Ding2, Ying Shen2
1Department of Cardiovascular Medicine, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China. lily_9013@163.com.
Insights
Artificial intelligence-aided angiography can assess collateral physiology in chronic total occlusions (CTO) by analyzing flow velocity changes in donor arteries. This novel method quantitatively correlates with angiographic collateral grading, simplifying intervention assessment.
Area of Science:
- Cardiovascular Medicine
- Medical Imaging
- Artificial Intelligence
Background:
- Assessing collateral physiology in chronic total occlusions (CTO) traditionally requires specialized devices, increasing complexity and cost.
- Current methods for evaluating collateral function in CTO interventions are often invasive and resource-intensive.
Purpose of the Study:
- To develop and validate a novel method for assessing collateral physiology in CTO using artificial intelligence (AI)-aided angiography.
- To derive collateral physiology from flow velocity changes (ΔV) in donor arteries, aiming to simplify and reduce the cost of intervention.
Main Methods:
- Retrospective analysis of angiographies from 105 patients undergoing successful percutaneous coronary intervention (PCI) for CTO.
- Utilized a deep-learning model to automatically compute flow velocities in primary and secondary collateral donor arteries (PCDA, SCDA) pre- and post-PCI.
- Derived collateral physiology parameters, including collateral flow (Δfcoll) and collateral flow index (ΔCFI), from measured flow velocity changes (ΔV).
Main Results:
- The AI-based method was feasible in 105/130 patients.
- Flow velocity in PCDA significantly decreased post-PCI, proportionally to Rentrop and collateral connections (CC) grading (p < 0.001).
- Derived parameters (Δfcoll, ΔCFI) paralleled ΔV, showing quantitative correlation with collateral grading.
Conclusions:
- Automatic assessment of collateral physiology in CTO using AI-aided angiography is feasible.
- The novel deep-learning model provides quantitative insights into collateral flow changes, proportional to angiographic collateral grading.
- This AI-driven approach offers a simplified, potentially less costly alternative for evaluating collateral function in CTO interventions.
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.
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