Table 1.

Summary of the studies involving Artificial Intelligence in cardiology.

Cardiology Domain	Diagnostic modality/type of data used	Author/Study name	Study application	Study conclusion
Preventive Cardiology	MERC model in NORIN-STEMI patients	Shetty et al4/NORIN-STEMI	Risk prediction following STEMI	ML models - improved mortality prediction following STEMI compared to traditional logistic regression (Extra Tree ML model best predictive ability -sensitivity: 85%, AUC: 79.7%, and Accuracy: 75%)
	Praise score in ACS patients	D'Ascenzo et al.5	ML model to predict all-cause death, recurrent acute myocardial infarction, and major bleeding after ACS	PRAISE score - accurate discriminative capabilities for prediction of all-cause death, myocardial infarction and major bleeding
Diagnostic CV Imaging	Echocardiography	Madani et al.8	Identification of echocardiographic views	CNN model distinguished between 15 standard echocardiographic views – accuracy: 97.8%
	Cardiac CT- CCTA	Zhang et al.9	Fully automated echocardiogram interpretation and detection of selected clinical condition	CNN model detected multiple clinical conditions (cardiomyopathy, cardiac amyloidosis and PAH - C statistics of 0.93, 0.87 and 0.85, respectively)
	Cardiac CT-FFR	Lin A et al.13	Develop and validate a deep learning system for CCTA-derived measures of plaque volume and stenosis severity	Deep learning system -rapid measurements of plaque volume and stenosis severity from CCTA
	CMR	Morais et al.14	Evaluate diagnostic performance of CT-FFR for detection of significant CAD in contrast to invasive FFR	ML-based CT-FFR: good diagnostic performance for detection of CAD
		Bai et al.15	Segmentation of heart structures, automatic measurement of LV end-diastolic volume and other values	CNN model - able to perform highly accurate automatic measurements and delineation of heart structures
		Wang et al.16	AI Based CMR Assessment of Biventricular Function	Good agreement between automated and expert-derived LVEF
Electrocardiography	Arrhythmia classification	Hannun et al.19	Automated arrhythmia classification	Classified 12 different arrhythmias with an average AUC of 0.97 and an F1 score of 0.84, exceeding that of an average cardiologist (0.78)
	AF detection	Attia ZI et al.20	Detection of paroxysmal AF based on 10 s recording of 12-lead ECG taken in sinus rhythm	AI-enabled ECG acquired during normal sinus rhythm permits AF detection
	Asymptomatic LV dysfunction	Attia et al.21	Detection of asymptomatic LV dysfunction from ECG	Detected LV dysfunction- AUC of 0.93
	HRV	Shah B et al.22	Evaluate an AI model to identify time domain HRV measures in COVID-19 recovered subjects	AI model was able to distinguish between COVID-19 recovered patients and healthy controls based on HRV
	AF detection	Perez et al.23	Smartwatch identification of AF	Participants receiving notification of an irregular pulse: 34% had AF on subsequent ECG patch readings and 84% of notifications were concordant with AF
Heart Failure	Artificial Intelligence-Clinical Decision Support System for HF diagnosis	Choi et al.25	Evaluate the diagnostic accuracy of an AI-CDSS for heart failure	AI-CDSS - high diagnostic accuracy for HF: concordance rate between AI-CDSS and heart failure specialists - 98%
	AI algorithm applied to a single-lead ECG recorded during ECG-enabled stethoscope examination	Bachtiger P et al,26	Validate a potential point-of-care screening tool (ECG-enabled stethoscope) for LVEF of 40% or lower	AI-ECG-enabled stethoscope can detect LVEF of ≤40% with good accuracy- AUROC: 0·91, sensitivity: 91·9% and specificity: 80·2%
	Predicting response to therapy (CRT)	AI-ECG,	Predicting CRT outcomes	Predicted death or HF hospitalization within 12 months - AUC of 0.74
	Novel characterization of HF phenogroups	Kalscheur et al.27	Predicting CRT outcomes	Predicted echocardiographic CRT response better than current guidelines (AUC: 0.70 vs. 0.65) - greater discrimination of long-term survival (c-index: 0.61 vs. 0.56)
		Feeny et al.28	Heart failure phenogroups in CRT	Four phenogroups identified with significantly different clinical and echocardiographic characteristics - two phenogroups substantially better treatment response to CRT therapy
		Cikes et al.29
Interventional Cardiology	IFR	Davies J.30 CEREBRIA-1	Comparison of AI with human intelligence for interpretation of IFR pullback data in stable CAD	ML algorithms were non-inferior to expert consensus opinion in determining both appropriateness for PCI as well as optimal PCI strategy
	FFR	Rougin A et al.31	Feasibility of AI based FFR (Autocath FFR) for prediction of hemodynamically significant lesions based on cineangiography images	Autocath FFR has excellent accuracy in prediction of wire based FFR

Abbreviations: ACS: acute coronary syndrome; AF: atrial fibrillation; AI: artificial intelligence; AI-CDSS: Artificial Intelligence-Clinical Decision Support System; AUC: area under the curve; CAD: coronary artery disease; CNN: convoluted neural network; CMR: cardiac magnetic resonance imaging; CRT: cardiac resynchronization therapy; CV: cardiovascular; CT: computed tomography; CCTA: Coronary computed tomography angiography; ECG: electrovcardiogram; FFR: fractional flow reserve; HF: heart failure; HRV: heart rate variability; IFR: instantaneous wave free ratio; LV: left ventricle; LVEF: left ventricular ejection fraction; ML: machine learning; North India ST-Elevation Myocardial Infarction (NORIN-STEMI); STEMI: ST-Elevation Myocardial Infarction.