Inter-center cross-validation and finetuning without patient data sharing for predicting transcatheter aortic valve implantation outcome

Transcatheter aortic valve implantation (TAVI) is the routine treatment worldwide for aortic valve stenosis in low-to high-risk patients. Assessing patient risk is essential to identify the most suitable candidates that could benefit from the procedure. Despite the broad use of statistical predictors in patient selection, current machine learning predictors have only been validated on retrospective data collected in single centers. Further, external validation is needed to assess the improvement in accuracy, which is offered by machine learning and deep learning techniques. In this study, we propose a finetuning approach for deep learning models by performing an inter-center cross-validation and finetuning technique, in order to improve the cross-validation accuracy results. We aimed to overcome data exchange and policy-related issues of two medical centers with a dedicated protocol, exploiting the exchange of deep learning models, data processing and validation steps which does not require any patient data sharing. The finetuning is based on the other center's data for further training of the initial model. After finetuning the model, we obtain an average AUC improvement of 13% and 7% with respect to the initial models. This research demonstrates that the predicting capabilities of deep learning models can be extended to and cross-validated with other centers, independent of limitations in data-sharing policies. Moreover, the study shows that finetuning can be exploited to considerably improve the accuracy of the prediction models.