Making Preventive Care a Reality in Cardiovascular Disease Treatment with Deep learning Technology
Intuition is the result of the collective intelligence of human experience. It can be developed to the point where an individual can sense or predict even the slightest threat and danger in any given situation. It is a highly advanced form of understanding that can be acquired through the process of self-growth and mastery which requires sincere dedication to the self. It can be time-consuming, sometimes painful and our day-to-day commitments and responsibilities often make it impossible to dedicate ourselves fully to the process.
Humans, therefore, develop it slowly, in a phased manner, over several years of experience in the areas of their choice and interest. Every individual’s intuitive abilities possess certain characteristics that can never be replicated due to the permutations and combinations of experiences that are unique to them. Intuition allows us to predict and act on situations according to the best of our knowledge.
Imagine the precision of this ability if it was possible to access everyone’s experience and apply the common and distinctive knowledge to the plan of action in critical situations and its result. This is the power of Machine Learning (ML) and Deep Learning(DL) in Artificial intelligence(AI). Deep learning technology aims to replicate this aspect of the human mind and develop software for the sole purpose of analyzing large amounts of data to deepen the understanding of a subject and make intelligent decisions based on current information and heuristic knowledge.
Deep Learning technology can be smartly incorporated where human energy, ethics, and resources limit the use of our abilities. In healthcare, for instance, it is impossible to humanly meet the demands of certain cases due to high costs of human assistance, lack of medical staff in high-demand situations, or the unpredictability of an illness. A good example to understand the relevance of this technology in healthcare would be cardiovascular disease. It was responsible for 31% of the worldwide deaths in 2016 and the reason behind most fatalities was the inability to detect the arrhythmia on time.
Deep learning (DL) methods today have successfully automated the detection of arrhythmias and assist in completely avoiding a cardiac event by alerting care teams of the possible event on time. ECG (electrocardiogram), the most widely used test to detect and predict cardiovascular diseases, captures the rhythmic irregularities playing a crucial role in diagnosing a patient’s acute and chronic heart conditions. ECG technology equipped with ML provides long-term analysis of this data to automate the detection of heart rhythm abnormalities.
Our heartbeats are just as unique as our lives making it impossible to draw conclusions based on a strictly defined formula or algorithm. Heart rhythms constantly change throughout the day and several factors determine our cardiac rhythms. Depending on whether an individual is walking, sleeping, sitting, feeling relaxed or excited a heartbeat can slow down or speed up. Lifestyle factors such as diet, mental health issues, and addictions can have a long-term impact on heart health. Heart conditions may be inherited based on the genetic make-up of an individual. These elements make ECG predictions highly complicated and not as accurate as they should be.
Also, it is humanly impossible to track individual beats and their highs and lows. These differences, although subtle, are crucial and hard to detect. Such minute abnormalities can only be detected through ML techniques. Cardiologists accurately predict and identify the right kind of abnormal heartbeat detected by the ECG to recommend the right treatment. In the traditional sense, signals obtained from an ECG are analyzed over a long period generating the big data needed to build the ML knowledge bank. Deep learning methods can be used to detect even the minutest aberrations in the heartbeats and individual models can be developed based on individual cases.
At Techindia, these predictive systems are updated regularly with millions of cases related to cardiac events sharpening the identification process of the system with every update. The data acts as experience in the deep learning technique enabling the computers to learn and acquire new skills without human intervention. Like humans, these computers are programmed to associate events and retain knowledge. The artificial neural networks and algorithms are thus inspired by the human brain. Deep learning models are already in use today in technologies such as driverless cars, image processing software and devices with voice control features.
Similarly, AI products today are being launched to make the ECG analysis as accurate and relevant as possible. Infuse Rhythm AI, a powerful software developed at TechIndia, classifies 21 categories of cardiac arrhythmias and noise filters using machine learning algorithms. The modular platform is user-friendly and its effective monitoring capabilities significantly reduce expensive medical events and allow physicians to focus on preventive care. The AI application simplifies and automates the complexities of detection in Holters, MCT, Events, 12 Lead ECG and Wearable biosensors at the same time increasing the reliability.
The Infuse RHYTHM AI engine uses a two-dimensional Deep Convolution Neural Network comprising of 14 layers to create 21 categorizations of cardiac rhythms. It is further enhanced by a collection of 315,488 single-lead 10-second ECG strips from close to 120,063 patients and other comprehensive diverse test sets of ECGs. This massive amount of data is sourced from FDA-approved monitoring devices such as Lifesignals, Bittium Faros, Apple Watch and 12 vanguard ECG machines from renowned brands such as Philips, BPL and GE to name a few.
Some of the prominent learning models that power Infuse RHYTHM AI are specifically recommended for ECG analysis such as Multilayer Perceptron (MLP), Convolution Neural Network (CNN), Recurrent Neural Network (RNN), Long Short-Term Memory (LSTM) and Deep Belief Network (DBN). Each model is designed to perform an analytical function to assist learning and develop its extraordinary predictive skills from the regular data updates.
Some of the main highlights of Infuse RHYTHM AI engine are:
AI is set to revolutionize the traditional approaches towards care globally. Its adoption is inevitable considering the time, cost and effort it saves in the treatment of chronic and acute illnesses. A transformation geared to be a lifesaver in more than just the healthcare setting, Artificial Intelligence is a ripple effect that will be felt far and wide.
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