Utilization of the Cardiac Phase Space Tomography Analysis (cPSTA) System.

<p>Phase signal data are collected and transferred to cloud. The generated models and analysis are available for physician assessment. cPSTA System = Cardiac Phase Space Tomography Analysis System, CAD = coronary artery disease. Reprinted from presentation materials of A4L under a license, with permission from A4L and W20, original production 2016.</p

Demographics of population.

<p>Demographics of population.</p

Detecting flow-limiting CAD.

<p>Machine-Learned Predictor (cPSTA) Compared to Exercise SPECT [<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0198603#pone.0198603.ref007" target="_blank">7</a>] and Exercise ECG [<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0198603#pone.0198603.ref007" target="_blank">7</a>, <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0198603#pone.0198603.ref026" target="_blank">26</a>].</p

Relationship between pre-test (prior) probability and post-test (posterior) probabilities.

a) the machine-learned predictor, b) BNP when greater than 150pg/ml, or c) BNP when greater than 50pg/ml. A positive test is shown in red, and a negative test in green. The diagonal dashed black line represents no change from the pre-test to post-test probability. The vertical dashed black lines represent intermediate to high pre-test probabilities (vertical dashed lines at 30%, 50% and 70%) from left to right. The post-test probabilities were calculated based on a varying pre-test probability, and constant sensitivity, specificity, and corresponding likelihood ratios.</p

Histogram of study population LVEDP.

Histogram of study population LVEDP.</p

Subgroup analyses of the performance of the machine-learned predictor.

Subgroup analyses of the performance of the machine-learned predictor.</p

Performance of the machine-learned predictor.

Performance of the machine-learned predictor.</p

Inclusion and exclusion criteria.

(DOCX)</p

Performance of the machine-learned model when propensity matching based on age and gender.

Performance of the machine-learned model when propensity matching based on age and gender.</p

Feature values.

(XLSX)</p