Technical Name Artificial Intelligence enabled electrocardiogram interpretation system
Project Operator National Defense Medical Center
Project Host 林嶔
Summary
Our AI-enabled ECG interpretation system combines the large scale annotation databasethe innovative deep learning algorithm, which can accurately assist in the diagnosis of more than 50 diseases using only one simple ECG. This system will be active by ECG uploadconduct real-time analysis, which provides physicians to identify severe, asymptotic,unaware cardiovascular diseases at the first time. It can also be widely used in remote communities to conduct cheapsimple chronic cardiovascular disease examinations,reduce the related disease burden through early diagnosistreatment.
Scientific Breakthrough
Our team has the world number two ECG database with the most comprehensive patient characteristicsdiagnoses. Besides, we have innovative deep learning algorithms to enhance model performance, so our AI-enabled ECG interpretation system can assist extensive disease diagnoses with high accuracy. Our system has been deployed in many fields to change local clinical process,the relevant results have also been recognized by several domestic awardspublished more than 10 clinical application studies in international journals.
Industrial Applicability
Our AI-ECG interpretation system hopes to monitor the user's physiological condition in a non-invasive way through ECG examination. It provides simple disease screening toolsassists clinicians in making real-time decision-making in places where medical resources are lacking. The system can effectivelyquickly diagnose acute cardiopulmonary diseasessimply screen potential diseases. In addition to being used in hospitals, it can also be used in ambulances, telemedicinewearable devices in the future to reduce the possibility of sudden death. Therefore, it is of great help in improving the health of the Taiwanese peopleeven all mankind.
Keyword Electrocardiogram Deep learning Epidemiology Statistics Unsupervised learning Transfer learning Internet of Things and Cloud Computing Cardiovascular disease Telemedicine
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