TABLE 4.

Flow or hemodynamics prediction accuracy reported by other machine or deep learning studies.

Method	Output object	Data set size	Input data format	Error function or accuracy
The proposed deep learning method	3D cerebral aneurysm hemodynamics	500	Flexible point cloud	NMAE < 6.5%, MRE < 13%
Itu’s machine-learning model	Fractional flow reserve (FFR) value	12,000	Geometric parameter	Error = 0.03%
Lee’s CNNs	2D unsteady flow field	500,000	Fixed meshes	32.8% < Error < 1%
Guo’s DCNNs	2D/3D steady flow	400,000	Fixed pixels	MRE < 3%
Liang’s DNNs	3D thoracic aorta hemodynamics	729	Fixed meshes	NMAE < 6.5%