Table 4.

Summary of studies that utilized convolutional neural networks to quantify parameters of kidney disease

Study	Parameter of Interest	Modality	Number of Subjects	Sensitivity	Specificity	Positive Predictive Value	Negative Predictive Value	Accuracy	Area Under the Curve
2D global slice-by-slice model in current report	Kidney fibrosis	CT	92	0.817	0.852	0.886	0.767	0.831	0.917
2D U-Net voxel model in current report	Kidney fibrosis	CT	92	0.853	0.916	0.935	0.816	0.879	0.922
Abdeltawab (2019) (7)	Early transplant kidney dysfunction	MRI	56	0.933	0.923	NR	NR	0.929	0.93
Kuo (2019) (25)	eGFR	US	1297	0.607	0.921	NR	NR	0.856	0.904
Sabanayagam (2020) (26)	eGFR	RP	6485	0.83	0.83	0.54	0.96	NR	0.911
Chen (2020) (27)	Kidney tumors	CT	100	0.77	0.93	NR	NR	0.9714	NR

2D, two-dimensional; CT, computed tomography; MRI, magnetic resonance imaging; NR, not reported; US, ultrasound; RP, two-field retinal photography.