Demographic, murmur, and clinical outcome information in the Challenge training, validation, and/or test sets; nan values indicate unknown or missing values. by Matthew A. Reyna (16965038)

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Demographic, murmur, and clinical outcome distributions across the Challenge training, validation, and test data. by Matthew A. Reyna (16965038)

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Weighted accuracy metric (1) for the murmur detection task (<i>x</i>-axis) and the cost metric (8) scores for the clinical outcome identification task (<i>y</i>-axis) of the final... by Matthew A. Reyna (16965038)

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Scores of the officially ranked methods on the test set for the clinical outcome identification task. by Matthew A. Reyna (16965038)

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Auscultation locations for the CirCor DigiScope dataset [6], which was used for the Challenge: Pulmonary valve (PV), aortic valve (AV), mitral valve (MV), and tricuspid valve (TV). by Matthew A. Reyna (16965038)

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Traditional evaluation metric (<i>x</i>-axis) and cost metric (<i>y</i>-axis) scores for the final Challenge entries with the clinical outcome identification task on the hidden tes... by Matthew A. Reyna (16965038)

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Traditional evaluation metric (<i>x</i>-axis) and weighted accuracy metric (<i>y</i>-axis) scores for the final Challenge entries with the murmur detection task on the hidden test... by Matthew A. Reyna (16965038)

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Confusion matrix <i>N</i> for clinical outcome detection with two classes: Clinical outcome abnormal and clinical outcome normal. by Matthew A. Reyna (16965038)

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Screening and diagnosis pipeline for the Challenge. by Matthew A. Reyna (16965038)

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Confusion matrix <i>M</i> for murmur detection with three classes: Murmur present, murmur unknown, and murmur absent. by Matthew A. Reyna (16965038)

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Scores of the officially ranked methods on the test set for the murmur detection task. by Matthew A. Reyna (16965038)

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The expert screening cost <i>g</i>_expert(<i>x</i>) defined for the Challenge: Mean cost for screening a fraction <i>x</i> of a patient cohort for cardiac abnormalities. by Matthew A. Reyna (16965038)

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Comparison between simulations for all algorithms (success rate <i>versus</i> number of swarm particles). by Rutinaldo Aguiar Nascimento (14259594)

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Summary of the contrasts obtained from the normalized images for both inclusions as a function of the host type and the wavelength. by María Victoria Waks-Serra (21578611)

Fraction of stripes considered as a function of aspect ratio cut-off value for individual stripe measurements. by Pratik Mullick (12851011)

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Overall concept and specific components for developing the cloud based solution. by Vlad Ploscaru (13767257)

Subjects: “…artificial intelligence algorithms…”

Cycle GAN approach for performing style transfer between healthy and pathological coronary angiography images slices. by Vlad Ploscaru (13767257)

Subjects: “…artificial intelligence algorithms…”

Sample envisaged detection of the distal point (white circle) for a right coronary artery image. by Vlad Ploscaru (13767257)

Subjects: “…artificial intelligence algorithms…”

Approach for artificial intelligence based virtual post-percutaneous coronary intervention fractional flow reserve (FFR) computation. by Vlad Ploscaru (13767257)

Subjects: “…artificial intelligence algorithms…”

Fig 1 - by Vlad Ploscaru (13767257)

Subjects: “…artificial intelligence algorithms…”