Enhancing Myocardial Disease Prediction with DOC-NET+ Architecture: A Custom Data Analysis Approach for the EMIDEC Challenge.

Public health experts are deeply concerned about cardiovascular diseases, including numerous heart-related ailments that can prove fatal. Distinguishing between Myocarditis and myocardial infarction (MI) is difficult due to comparable symptoms and diagnostic complications. While endomyocardial biopsy (EMB), cardiac troponin indicators, electrocardiography (ECG), and echocardiography are useful in making preliminary diagnoses, their effectiveness is limited. The EMIDEC challenge initiative stands out as an important development in this context. It demonstrates how clinical physiological parameters, combined with delayed enhancement magnetic resonance imaging (DE-MRI), can significantly enhance classification reliability. The aim of this research is to provide a thorough assessment of the present state of the art in EMIDEC-related research. Subsequently, our intention is to establish a new database that will serve as the framework for comprehensive testing and evaluation of the architectural models drawn from the literature. We established a constructive collaboration with the Military Hospital of Tunis, which was crucial for patient data collection and access to a diverse group of patients diagnosed with Myocarditis and MI. We proceeded to select the appropriate architectural models for our study. We chose the DOC-NET and DOC-NET+ models based on prior research that demonstrated their effectiveness in similar classification contexts. DOC-NET and DOC-NET+ demonstrated accuracy rates of 95% and 100% in previous studies with the EMIDEC dataset. When these models were applied to our newly produced custom dataset, DOC-NET maintained its impressive performance with an accuracy score of 97%, while DOC-NET+ had an accuracy score of 98%. However, as we increased both the number of patients and the complexity of scenarios in our new dataset, the accuracy of DOC-NET+ dropped significantly to 91%. The CMR with AI combination allows for more customized and efficient evaluation, ultimately increasing patient outcomes and altering the cardiovascular healthcare environment. However, problems connected to the algorithm's generalizability to complicated data continue to be a crucial concern in further refining these improvements. [ABSTRACT FROM AUTHOR]