Table 3. Characteristics of deep learning systems for CVD risk predictions (I).

SN	Studies	Country	TP	TI	FH	RF	BMI	Eth	#TD	HT	SM	MC	MRI	ECG	CUSIP	IST	Tech.	Organ
1	Azzorpardi et al. (78)	England	15	×	×	CUSIP	×	√	PD	√	√	√	×	×	√	Image	Segm.	Carotid Artery
2	Biswas et al. (79)	USA	203	396	√	OBBM, LBBM, cIMT	√	√	ID	√	√	√	×	×	√	Image	Segm.	Carotid Artery
3	Biswas et al. (80)	USA	203	×	×	cIMT, PA	√	√	ID	√	√	×	×	×	√	Image	Segm.	Carotid Artery
4	Biswas et al. (81)	USA	204	×	×	OBBM, LBBM, cIMT, TPA	√	√	ID	√	√	√	×	×	√	Image	Segm.	Carotid Artery
5	Jain et al. (98)	USA	50	300	√	OBBM, LBBM, CUSIP	√	√	ID	√	√	√	×	×	√	Image	Segm.	Carotid Artery
6	Jain et al. (99)	USA	99	970	×	OBBM, LBBM, CUSIP	×	√	ID	√	√	√	×	×	√	Image	Segm.	Carotid Artery
7	Jamthikar et al. (13)	USA	120	×	√	OBBM, LBBM, CUSIP, RA	√	√	PD	√	√	×	×	×	√	Image	Segm.	Carotid Artery
8	Lakadir et al. (82)	Spain	56	×	√	OBBM, LBBM, cIMT	√	×	ID	√	√	×	×	×	√	Image	Segm.	Carotid Plaque
9	Meshram et al. (83)	Columbia	101	×	√	OBBM, LBBM, Plaque	√	×	ID	√	√	×	×	×	√	Image	Segm.	Carotid Plaque
10	Wu et al. (84)	China	1,057	×	√	OBBM, LBBM, MRI	√	×	ID	√	√	×	√	×	×	Image	Segm.	Carotid
11	Zhou et al. (85)	China	600	5,000	×	OBBM, LBBM, TPA	×	√	ID	×	×	√	×	×	√	Image	Segm.	Carotid Plaque
12	Zhou et al. (86)	China	77,497	×	×	OBBM, LBBM, TPA	×	√	ID	√	√	√	×	×	√	Image	Segm.	Carotid Plaque
13	Ganitidis et al. (87)	Greece	84	×	×	OBBM, LBBM, TPA	×	√	ID	√	√	×	×	×	√	Image	Segm.	Carotid
14	Mohannadi et al. (100)	Qutar	NA	100	×	OBBM, cIMT	×	×	ID	×	×	×	×	×	√	Image	Segm.	Carotid
15	Latha et al. (101)	Malaysia	NA	361	√	OBBM, LBBM, cIMT	√	×	ID	√	√	×	×	×	√	Image	Segm.	Carotid
16	Otgonbaatar et al. (102)	Korea	43	×	×	OBBM, Brain CT	×	×	ID	√	√	×	×	×	√	Image	Segm.	Brain
17	Jain et al. (103)	USA	190	379	√	OBBM, LBBM, CT	√	√	ID	√	√	×	×	×	√	Image	Segm.	Carotid
18	Ziegler et al. (88)	Sweden	NA	482	√	OBBM, LBBM, MRI	√	×	ID	√	√	×	√	×	×	Image	Segm.	Carotid
19	Bortsova et al. (89)	The Netherlands	2,319	×	√	OBBM, LBBM, volume	√	√	ID	√	√	×	×	×	√	Image	Segm.	Carotid
20	Zhu et al. (104)	China	NA	×	×	OBBM, LBBM, cIMT	×	×	ID	×	×	×	√	×	×	Image	Segm.	Carotid
21	Park et al. (105)	Korea	NA	316	×	US Imaging parameter	×	√	ID	×	×	×	×	×	√	Image	Segm.	Carotid
22	Jain et al. (90)	USA	NA	433	√	cIMT, LD	×	×	ID	×	×	×	×	×	√	Image	Segm.	Carotid
23	Savaş et al. (91)	Turkey	153	501	×	OBBM, IMT	×	×	ID	√	×	×	×	×	√	Image	Segm.	Carotid
24	Washim et al. (92)	USA	NA	20	×	cIMT	×	×	ID	×	×	×	×	×	√	Image	Segm.	Carotid Artery
25	Sudha et al. (93)	India	110	220	×	cIMT	×	×	ID	√	×	√	×	×	√	Image	Segm.	Carotid
26	Groves et al. (106)	Canada	160	360	×	OBBM, LBBM, TPA	×	×	ID	√	×	√	×	×	√	Image	Segm.	Carotid
27	Saba et al. (94)	Italy	75	×	×	cIMT	×	×	ID	×	×	×	×	×	√	Image	Segm.	Carotid
28	Tsakanikas et al. (107)	Greece	30	×	√	cIMT	×	×	ID	×	×	×	√	×	×	Image	Segm.	Carotid
29	Koktzoglou et al. (95)	Illinois	12	×	×	cIMT	×	×	ID	×	×	×	×	×	√	Image	Segm.	Carotid
30	Flores et al. (108)	USA	NA	×	×	cIMT	×	×	ID	×	×	×	×	×	√	Image	Segm.	Carotid
31	Luo et al. (96)	Indianapolis	5700	×	×	cIMT	×	×	ID	×	×	×	×	×	√	Image	Segm.	Carotid
32	Xiao et al. (97)	China	NA	×	×	RF Signals	×	×	ID	×	×	×	×	×	×	RFsignal	Segm.	Carotid

CVD, cardiovascular disease; SN, serial number; TP, total patients; TI, total image; FH, family history; RF, risk factor; BMI, body mass index; Eth, ethnicity; #TD, #type of data; HT, hypertension; SM, smoking; MC, multicentre; MRI, magnetic resonance imaging; ECG, electrocardiogram; CUSIP, carotid ultrasound imaging phenotype; IST, input single type; Tech., technique; CUSIP, carotid ultrasound image phenotypes; OBBM, office-based biomarkers; LBBM, laboratory-based biomarkers; cIMT, carotid intima-media thickness; PA, power analysis; TPA, total plaque area; RA, rheumatoid arteries; CT, computed tomography; US, ultrasound; MRI, magnetic resonance imaging; LD, lumen diameter; RF, random forest.