Non-Invasive Point-of-care Diagnosis Using Machine Learning and Signal Analytics to Transform Early Detection of Heart Disease
Non-Invasive Point-of-care Diagnosis Using Machine Learning and Signal Analytics to Transform Early Detection of Heart Disease
This study is designed as a repository study to collect resting cardiac phase signals and subject meta data from eligible subjects using the Phase Signal Recorder (PSR) prior to coronary angiography. The repository data will be used for the purposes of research, development, optimization and testing of machine-learning algorithms developed by Analytics 4 Life.
Male and Female subjects will be uniquely and consecutively enrolled into one of two groups to support populating a repository of phase signals: Group 1: Subjects with known prior coronary artery disease or previous percutaneous coronary intervention (PCI), and Group 2: Subjects with new onset symptoms suggestive of obstructive coronary artery disease at current presentation with no known coronary artery disease.
Enrollment into Group 1 and Group 2 will occur simultaneously until up to 500 male subjects and up to 1000 female subjects with paired data (phase signal and coronary angiography outcomes data) are enrolled. Once the desired number of subjects have been enrolled from both groups, then only Group 2 will continue to be enrolled.
Resting phase signals will be collected in all patients who meet inclusion/exclusion criteria and have signed an informed consent form. This study consists of a screening visit, resting phase signal collection (study procedure), and coronary angiography. In this study, resting phase signals will be acquired in subjects prior to coronary angiography.
Inclusion Criteria:
Exclusion Criteria:
horace.gillins@analytics4life.com(919) 444-2843