Table 2.

Current challenges and future perspectives of computational modelling in AF management

No.	Challenge
1	Lack of personalization details (e.g. incorporation of genetic and acquired risk factors)
2	Limited availability of experimental data used to validate computational models (e.g. limited access to atrial tissues other than atrial appendage and to patient cohorts without an indication for cardiac surgery)
3	Limited pre-procedural availability of patient-specific electrophysiological information
4	Inability to image patient-specific fibre orientations
5	Limited spatial resolution of traditional MRI makes resolving the complex fibrosis patterns in the thin atrial walls challenging
6	Intra-individual heterogeneities are not fully characterized
7	Lack of cellular details in organ-level models that may be required to simulate realistic AAD effects due to high computational cost
8	Issues regarding simulation of intervening gaps, PV reconnection, focal ectopic firing and progression of the underlying substrates due to continued atrial remodelling remain unresolved
9	Complex integration with existing workflows and systems (e.g. requirement for LGE-MRI and its time-consuming segmentation, integration with electro-anatomical mapping systems)
10	‘Black box’ characteristic of deep-learning based machine learning models

No.	Future perspective
1	Advances in experimental methodologies as well as clinical imaging modalities may provide new opportunities for model development and personalization
2	Technological innovations in combination with new approaches for model simplification are expected to provide additional computational performance, enabling simulation of more detailed mechanistic-models
3	Increased standardization and improved attention to re-usability will likely facilitate the exchange of modelling approaches and their integration into existing workflows
4	An increasing availability of large data sets and modern (explainable) machine learning models
5	Hard evidence for the clinical benefit of using computational models (e.g. RCT) will be needed to motivate their routine use