Detection of Heart Disease
Radionics, an alternative form of medicine, asserts that disease can be diagnosed using frequency energies to balance discordant frequencies. When applied to heart disease, such energy could help heal physical structures while also restoring healthy energy flow. A device transmitting various frequencies over the body may be used, with each device purporting to detect imbalances among these frequencies; conversely a person exhibiting healthy frequencies would likely exhibit positive energy that promote health.
Traditional cardiac imaging techniques focused on structural abnormalities to diagnose and guide treatment decisions, with cardiac CT capable of detecting coronary artery stenosis, echocardiography assessing valve disease and pericardial effusion evaluation, magnetic resonance imaging (MRI) providing assessments on ventricular function as well as differentiating the various causes. Hybrid systems that combine PET or SPECT imaging with CT or MRI provide perfusion and metabolic mapping data along with anatomical information for functional imaging purposes.
However, these technologies can be costly and require medical facilities for diagnosis. Furthermore, they only detect limited conditions. Enter Rhythmi: an innovative mobile ECG diagnostic technology powered by deep learning algorithms to detect cardiovascular diseases without the need to visit medical facilities – using CNNs it can identify arrhythmias and other common cardiovascular ailments without having to visit 12 electrodes like traditional ECG tests do – rather it captures data using one sensor, making the experience more comfortable for patients than traditional tests which use 12 electrodes; its accuracy, user friendliness, portability and portability contribute to its success while limitations include limited database of arrhythmias as well as generic medical report that doesn’t provide medical details specifics about each individual case.
Detection of Blood Vessel Disease
Blood vessel blockages are one of the main contributors to cardiovascular diseases, resulting in ischemia, myocardial infarction, stroke and other serious consequences. Utilizing non-invasive sensing technologies, cloud computing platforms, and deep learning techniques, the Smart Diagnostic System can detect arterial artery blockages without resorting to costly procedures or invasive surgery. This system utilizes Photoplethysmography (PPG), infrared thermal imaging and Doppler ultrasound sensors to continuously acquire pulse waveforms, temperature distribution patterns and blood ow characteristics related to vascular health. A microcontroller module based on an ESP32 microcontroller handles signal acquisition, analog-to-digital conversion and wireless communication while additionally offering data backup/recovery mechanisms that ensure its scalability, maintainability and reliability.
