The ECG tests along with Artificial Intelligence (AI) are found to help identify patients’ likely medical cause of death within a year. The researchers from Geisinger Health System have proved the research right by studying 1.77 Million ECGs and other records of approximately 400,000 subjects. The team compared the machine learning-based models with the data collected to understand the patterns of changes that lead to the shortening of life in the long-run. The neural network model was found to be effective in studying the ECG signals and directly predicting the 1-year risk of death. The neural network helps predict the risk of death in individuals declared to have normal ECG by their physicians.
The risk patterns are generally difficult to be recognized but the AI-based model was found to prove efficient in analyzing ECG tests and identifying the patients at a higher risk of death. The model could successfully detect the dangerous irregular heartbeat and help the health officials warn the patients to resort to the necessary treatment. The ECG results helped the team train deep neural networks. The use of computer predictions for appropriate care plans for patients is the bigger challenge. AI could help predict future events rather than the current health problems. This technology could revolutionize medicine by using computers. This technique could help uncover potential atrial fibrillation now or in the future.
Similarly, a California-based startup QT Medical has been able to develop a miniature version of ECG such that people can perform the tests at home itself. The portable ECG device, PCA 500, is found to use just four things to provide accurate readings. The new device was used by QT Medical’s very own Dr. Ruey-Kang Chang at 33,000ft in an economy seat during his flight from California to Taipei. The researchers are also working on QT Pro that can be used in an emergency room as well as an ambulance. The company is also planning for an online service called EKG 360 to function like the ride services.
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