Table 4.

Comparison with current literature.

Authors	Methoda	AUC	AUC0	Dice Score	Mean volume absolute error (mL)	Number of patients (training/test set)
Scalzo et al. 42	Kernel spectral regression		0.91	–	–	25b
Nielsen et al. 18	Deep learning (SegNet)	–	0.88	–	–	187 (158/29)
McKinley et al. 15	Random forests (FASTER)	–	–	0.34 (±0.22)	30 (±26)	100 (45/55)
Pinto et al. 47	Deep learning (U-Net + Gated recurrent unit)	–	–	0.35	–	75 (43/32)
Winder et al. 43	Random forests	–	–	0.45	–	90b
Grosser et al. 17	Gradient boosting		0.89	0.39	–	99c
Yu et al. 31	Deep learning (U–net)	0.92	–	0.53	–	182d
Our study	Deep learning (U-Net)	0.98	0.94	0.48	28 (±38)	394c
	Gradient boosting	0.98	0.94	0.53	27 (±40)	394c

^aBest performing model for each study is presented.

^bLeave-one-out cross validation.

^c10-fold cross-validation.

^d5-fold cross-validation.