A transformer-based deep learning model for predicting the use of atherectomy devices during PCI from preprocedural cardiac computed tomography angiography

Severe coronary calcification complicates percutaneous coronary intervention (PCI), often necessitating atherectomy-based debulking devices that increase procedural complexity and risk. We aimed to develop and externally validate a Transformer-based deep learning model using preprocedural cardiac computed tomography angiography (CCTA) to predict atherectomy device use during PCI and compare its performance with that of blinded human readers, including general cardiologists and PCI experts.

This retrospective multicentre study included 498 calcified coronary lesions from 419 patients who underwent PCI after preprocedural CCTA. Data from 12 institutions were used for training and validation with four-fold cross-validation, and data from two independent institutions (97 lesions) served as an external test cohort. The model combined a convolutional neural network feature extractor with Transformer-based multiple instance learning, and clinical variables were evaluated for incremental value. In the external test cohort, the CCTA-only model achieved an area under the receiver operating characteristic curve (AUC) of 0.788 (95% confidence interval [CI], 0.691–0.873) for predicting atherectomy device use. Adding clinical variables resulted in an AUC of 0.805 (95% CI, 0.713–0.886), indicating limited incremental value. In the reader comparison, the model showed higher accuracy than general cardiologists (0.742 vs. 0.619; P = 0.029) and numerically similar accuracy to PCI experts (0.742 vs. 0.670; P = 0.230).

A Transformer-based deep learning model using preprocedural CCTA showed clinically relevant performance for predicting atherectomy device use during PCI in an independent external test cohort. These findings suggest that the model may support lesion assessment and procedural planning.