Table 6.

Comparative analysis of AI-based studies with CVD/stroke risk stratification of PD patients in the COVID-19 framework.

SN	Citations	Year	Input Covariates						GT	PS	AI	FE	CLS	ACC %	AUC
			OBBM	LBBM	CUSIP	MedUSE	PD	COV
1	Yan et. al. [268]	2019	✓	✓	✕	✓	✕	✕	CVD	NA	NA	NA	NA	NA	NA
2	Park et al. [249]	2017	✓	✓	✕	✕	✓	✕	Stroke	18	ML	RF	SVM	88.00	NR
3	Suri et al. [248]	2022	✓	✓	✓	✕	✓	✕	CVD/stroke	NR	ML	NR	NR	NR	NR
4	Zimmerman et al. [252]	2020	✓	✓	✕	✕	✕	✓	CVD	32	DL	LDA	CNN	87.23	NR
5	Aljameel et al. [269]	2021	✓	✓	✕	✕	✕	✓	CVD/stroke	287	ML	KNN	SVM	95.00	0/99
6	Suri et al. [54]	2020	✓	✓	✓	✕	✕	✓	CVD/stroke	NR	ML/DL	NR	NR	NR	NR
7	Handy et al. [253]	2021	✓	✓	✓	✕	✕	✓	CVD/stroke	NR	ML/DL	LSTM	SVM	84.00	NR
8	Unnikrishnan et al. [245]	2016	✓	✓	✕	✕	✕	✕	CVD	3654	ML	LR	SVM	83.00	NR
9	Mouridsen et al. [270]	2020	✓	✓	✕	✕	✕	✕	Stroke, MRI	16	DL	NR	KNN	74.00	0.74
10	Bergamaschi et al. [254]	2021	✓	✓	✕	✕	✕	✕	CVD	237	NA	NA	NA	NA	NA
11	Reva et al. [244]	2021	✓	✓	✕	✕	✕	✕	Stroke, CT	200	ML	NB	DT, RF, SVM	85.32	NR
12	Kakadiaris et al. [243]	2022	✓	✓	✕	✕	✕	✕	CVD	6459	ML	DT, RF	SVM	86.00	0.92
13	Proposed study	2022	✓	✓	✓	✕	✓	✓	CVD/stroke	NA	NA	NA	NA	NA	NA

IC: Input covariate, COV: COVID-19, PD: Parkinson’s disease, CVD: Cardiovascular disease, AI: Artificial Intelligence, OBBM: Office-based, LBBM: Laboratory-based, CUSIP: Carotid ultrasound image phenotype, MedUse: Medication, GT: Ground truth, PS: Patient size, FE: Feature extraction, CLS: Type of classifier, ACC: Accuracy, AUC: Area under the curve, NA: Not applicable, NR: Not reported, ✓: Yes, ✕: No.