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

Ricardo R. Lopes, Marco Mamprin, Jo M. Zelis, Pim A.L. Tonino, Martijn S. Van Mourik, Marije M. Vis, Sveta Zinger, Bas A.J.M. de Mol, Peter H.N. de With, Henk A. Marquering

Onderzoeksoutput: Hoofdstuk in Boek/Rapport/CongresprocedureConferentiebijdrageAcademicpeer review

Samenvatting

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.

Originele taal-2Engels
TitelProceedings - 2020 IEEE 33rd International Symposium on Computer-Based Medical Systems, CBMS 2020
RedacteurenAlba Garcia Seco de Herrera, Alejandro Rodriguez Gonzalez, KC Santosh, Zelalem Temesgen, Bridget Kane, Paolo Soda
UitgeverijInstitute of Electrical and Electronics Engineers
Pagina's591-596
Aantal pagina's6
ISBN van elektronische versie978-1-7281-9429-5
DOI's
StatusGepubliceerd - jul 2020
Evenement33rd IEEE International Symposium on Computer-Based Medical Systems, CBMS 2020 - Virtual, Online, Verenigde Staten van Amerika
Duur: 28 jul 202030 jul 2020

Congres

Congres33rd IEEE International Symposium on Computer-Based Medical Systems, CBMS 2020
LandVerenigde Staten van Amerika
StadVirtual, Online
Periode28/07/2030/07/20

Vingerafdruk Duik in de onderzoeksthema's van 'Inter-center cross-validation and finetuning without patient data sharing for predicting transcatheter aortic valve implantation outcome'. Samen vormen ze een unieke vingerafdruk.

Citeer dit