Executive Summary : | Cardiac troponin (cTn) is a protein molecule which is present in the skeletal cardiac muscle. Increased levels of cardiac troponins in blood are significant factors for diagnosing Acute Myocardial Infarction (AMI). Various advancements in cancer treatment have increased the number of cancer survivors tremendously. However, the late effect of cancer therapies increases adverse cardiovascular complications like cardio-toxicity, which is a major factor in death in these patients. Though cardio-oncology has gained momentum rapidly in various healthcare systems, it remains a fledgling discipline within the context of clinical cardiology or oncology. Heart Failure (HF) can be measured through Left Ventricular Ejection Fraction (LVEF). A decrease in LVEF reflects myocardial injury, which indicates cardiac failure. Also, it is observed that predicting HF is challenging even for cardiac specialists. The HF can be divided into three types known as reduced Ejection Fraction (HFrEF), mid-range Ejection Fraction (HFmrEF), and Preserved Ejection Fraction (HFpEF). The worsening of Cardio Vascular Disease (CVD) has turned into a huge public health issue, and it is noted that a higher level of Troponin-I is associated with lower LVEF. The researcher's focus is on identifying biosensors for HF and employing an Artificial Intelligence (AI) methodology known as Machine Learning (ML) and Deep Learning (DL) and an Internet of Things (IoT) concept. Various modalities are available to assess the heart's function, namely Cardiac Magnetic Resonance Imaging (MRI), 2D/3D Echocardiogram, Electrocardiogram (ECG), and Angiogram images to find the blocked/occluded arteries. Amongst them, the preliminary and easily available technique is the ECG which is useful in monitoring cardiac activity in the medical field. ECG signals are useful in screening and diagnosing CVDs such as cardiac arrhythmia, stenosis, etc. The Holter ECG records the electrical activity for several hours. Whereas a standard 12-lead ECG provides the cardiac activity in 12 different Leads. Thus ECG is a faster, more powerful, and non-invasive way to diagnose CVD. The goal of developing IoT-based biosensor types is, to make it possible for them to be converted into easily-used portable, handheld devices that provide laboratory-grade sensing capabilities. A recent study has shown the beats of current cardiovascular IoT device options for ambulance applications. Another recent 2022 study has shown the intelligent IoT-based framework, which keeps track of patients based on real-time data and offers heart failure patients prompt, efficient, and high-quality healthcare services. In the suggested effort, we are attempting to create an IoT-based DL platform for HF measurement using the cTn levels (IDC) tool that can assist us in locating the biomarkers for CVD-related complications. |