Development and validation of artificial intelligence-based algorithms for predicting the segments debulked by rotational atherectomy using intravascular ultrasound

Background Although rotation atherectomy (RA) is a useful technique for severely calcified lesions, patients undergoing RA show a greater incidence of catastrophic complications, such as coronary perforation. Therefore, prior to the RA procedure, it is important to predict which regions of the coronary plaque will be debulked by RA. Objectives We develop and evaluate an artificial intelligence–based algorithm that uses pre-RA intravascular ultrasound (IVUS) images to automatically predict regions debulked by RA Methods A total of 2106 IVUS cross-sections from 60 patients with de novo severely calcified coronary lesions who underwent IVUS-guided RA were consecutively collected. The two identical IVUS images of pre-and post-RA were merged, and the orientations of the debulked segments identified in the merged images are marked on the outer circle of each IVUS image. The artificial intelligence model was developed based on ResNet (deep residual learning for image recognition). The architecture connected 36 fully connected layers, each corresponding to one of the 36 orientations segmented every 10°, to a single feature extractor. Results In each cross-sectional analysis, our artificial intelligence model achieved an average sensitivity, specificity, positive predictive value, negative predictive value, and accuracy of 81%, 72%, 46%, 90%, and 75%, respectively. Conclusions The artificial intelligence–based algorithm can use information from pre-RA IVUS images to accurately predict regions debulked by RA. The proposed method will assist interventional cardiologists in determining the treatment strategies for severely calcified coronary lesions.