Harnessing Artificial Intelligence and Machine Learning for Enhanced Diagnosis of Takotsubo Cardiomyopathy

Abstract

Cardiologists often view Takotsubo cardiomyopathy (TTC) which doctors call stress-induced cardiomyopathy as a short-term heart condition that can look like acute coronary syndrome (ACS). Medical professionals struggle to achieve accurate and timely diagnosis of TTC because regular diagnostic techniques depend strongly on imaging procedures and subjective assessments. The application of artificial intelligence (AI) combined with machine learning (ML) delivers advanced verification tools to process intricate clinical datasets that include imaging outcomes along with electrocardiogram (ECG) results and medical record assessments. This paper demonstrates how AI and ML systems can examine patterns to maximize diagnostic accuracy while decreasing inaccurate diagnoses and enhance the results for patients who are being evaluated for TTC. This study reveals both integration difficulties and roadmap details regarding the deployment and advancement of these technologies for medical use.