Table 3.

Overview of machine learning applications in various cardiology-focused areas, highlighting performance metrics and future considerations.

Study	Focus Area	Machine Learning Application	Performance Metrics	Future Considerations
[73]	Risk prediction in resource-limited countries	STEMI	Improved mortality prediction following STEMI Extra Tree ML model demonstrated best predictive ability (sensitivity: 85%, AUC: 79.7%, and accuracy: 75%)	Clinical applicability Generalizability across diverse patient populations Reducing biases in training data
[75]	Automated volume-derived cardiac functional evaluation	CMR imaging and automated strain assessment	GLS and GCS best predicted MACE with high accuracy	Time-consuming post-processing Validation in broader populations
[77]	(Semi)Automatic CAC identification in cardiac CT	Cardiac CT and automated CAC scoring	1. Detection of 52% to 94% of CAC lesions. Positive predictive values between 65% and 96%. 2. Linearly weighted Cohen’s kappa for patient CVD risk categorization ranged from 0.80 to 1.00.	Missed lesions in distal coronary arteries False positive errors near coronary ostia Challenges in ambiguous locations