Decoding the Genetic Architecture of Acute Coronary Syndrome: A Review on Integrated Medical Modeling.

UNLABELLED: Genetic mutations contribute significantly to the complexity of understanding Acute Coronary Syndrome (ACS). Artificial Intelligence (AI) models, including Artificial Neural Networks (ANN), Convolutional Neural Networks (CNN), and Recurrent Neural Networks (RNN), have been integrated as an advanced approach to uncover hidden mutation clues for early detection and prevention of life-threatening diseases, including ACS.

INTRODUCTION

Genetic mutations play a crucial role in the pathogenesis of ACS, complicating efforts to fully characterize its underlying biological mechanisms. These mutations contribute to atherosclerosis, leading to ACS, along with other genetic disorders, making it essential to explore them using advanced AI models.

AIM OF THE STUDY: The study focuses on the impact of genetic mutations on ACS risk and on understanding these mutations using AI tools, such as ANN, CNN, and RNN.

METHODS

An innovative Artificial Intelligence (AI)-based approach was used to investigate the key loopholes associated with ACS. AlphaFold and AI models, including ANN, CNN, and RNN, were utilized to analyze genetic data and biological processes related to ACS and other genetic diseases.

RESULTS AND DISCUSSION: AI models have simplified the interpretation of complex genetic data, revealing how ACS-related mutations contribute to atherosclerosis by affecting molecular pathways and mechanisms.

CONCLUSION

Although challenges remain, the integration of AI in cardiac genetics offers promise for personalized medicine, providing timely solutions while maintaining ethical standards.