Cardiovascular Diseases Clinical Trial
Official title:
Application of Artificial Intelligence Deep Learning to the Correlation Between Cardiovascular Disease and Individualized Differences
An association study with large database from electronic medical record system, images, outcome analysis and genetic single nucleotide polymorphism variations by machine learning and artificial intelligence methods in a Taiwanese and Chinese medical center based population
In recent years, the analysis of big data database combined with computer deep learning has
gradually played an important role in biomedical technology. For a large number of medical
record data analysis, image analysis, single nucleotide polymorphism difference analysis,
etc., all relevant research on the development and application of artificial intelligence can
be observed extensively. For clinical indication, patients may receive a variety of
cardiovascular routine examination and treatments, such as: cardiac ultrasound, multi-path
ECG, cardiovascular and peripheral angiography, intravascular ultrasound and optical
coherence tomography, electrical physiology, etc... The current study is for the
investigative cardiovascular team to take the advantage that in addition to the examination
and treatment the participants should appropriately receive, the investigators can also
analyze the individual differences and using the "deep learning methodology" to analyze the
difference in physical fitness, therapeutic effectiveness and the consideration in the safety
of the treatment. The additional goal of this study is to improve the quality of health care,
the realization of cardiovascular "precise medicine" especially with personal difference on
genetic variation.
This study will analyze the differences in the individualization of cardiovascular disease
between diseases and other subjects to further improve the quality of care for clinical
patients. By using artificial intelligence deep learning system, the investigators hope to
not only improve the diagnostic rate and also gain more accurately predict the patient's
recovery, improve medical quality in the near future.
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