ct-ffr research software (Canon inc)
90
Structured Review
Canon inc
ct-ffr research software
Ct Ffr Research Software, supplied by Canon inc, used in various techniques. Bioz Stars score: 90/100, based on 1 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
https://www.bioz.com/product/ct-ffr+research+software/ct+ffr+research+software/10__1016_slash_j__jcct__2019__06__004-1019-7-10
Average 90 stars, based on 1 article reviews
Ct Ffr Research Software, supplied by Canon inc, used in various techniques. Bioz Stars score: 90/100, based on 1 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
https://www.bioz.com/product/ct-ffr+research+software/ct+ffr+research+software/10__1016_slash_j__jcct__2019__06__004-1019-7-10
Average 90 stars, based on 1 article reviews
ct-ffr research software - by Bioz Stars,
2026-09
90/100 stars
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Related Articles
Software:Article Title: Initial evaluation of three-dimensionally printed patient-specific coronary phantoms for CT-FFR software validation Article Snippet: Image data from the phantoms were input to a Article Title: 3D Printed Cardiovascular Patient Specific Phantoms Used for Clinical Validation of a CT-derived FFR Diagnostic Software Article Snippet: This project uses a Article Title: Abstracts of the 14th Annual Scientific Meeting of the Society of Cardiovascular Computed Tomography Article Snippet: s of the 14th Annual Scientific Meeting of the Society of Cardiovascular Computed Tomography Posters Friday: Poster Session 1: Artificial Intelligence/ Machine Learning Abstracts 36-43 36 Machine Learning To Predict The Long-term Risk Of Myocardial Infarction And Cardiac Death Based On Clinical Risk, Coronary Calcium And Epicardial Adipose Tissue: Difference Between Men And Women Commandeur F, Slomka P, Göller M, Chen X, Cadet S, Razipour A,1 Gransar H,1 Cantu S,1 Miller R,1 Rozanski A,1 Achenbach S,2 Tamarappoo B, Berman D, Dey D. 1Cedars-Sinai Medical Center, Los Angeles, CA, United States University hospital Erlangen, Erlangen, Germany Introduction Machine learning (ML) allows objective integration of clinical and imaging data for the prediction of events.. Our aim was to evaluate the performance of ML for the prediction of myocardial infarction (MI) and cardiac death (CD) in asymptomatic subjects, as well as in men and women separately.. Methods We assessed 1912 consecutive subjects [1117 (58.4%) male, age: 55.8±9.1] from the EISNER (Early Identification of Subclinical Atherosclerosis by Noninvasive Imaging Research) trial, with long-term follow-up after non-contrast CAC CT. CAC score, age-and-gender-adjusted CAC percentile, and aortic calcium scores were obtained. Derivative Assay:Article Title: Initial evaluation of three-dimensionally printed patient-specific coronary phantoms for CT-FFR software validation Article Snippet: Image data from the phantoms were input to a Article Title: 3D Printed Cardiovascular Patient Specific Phantoms Used for Clinical Validation of a CT-derived FFR Diagnostic Software Article Snippet: This project uses a Article Title: Abstracts of the 14th Annual Scientific Meeting of the Society of Cardiovascular Computed Tomography Article Snippet: s of the 14th Annual Scientific Meeting of the Society of Cardiovascular Computed Tomography Posters Friday: Poster Session 1: Artificial Intelligence/ Machine Learning Abstracts 36-43 36 Machine Learning To Predict The Long-term Risk Of Myocardial Infarction And Cardiac Death Based On Clinical Risk, Coronary Calcium And Epicardial Adipose Tissue: Difference Between Men And Women Commandeur F, Slomka P, Göller M, Chen X, Cadet S, Razipour A,1 Gransar H,1 Cantu S,1 Miller R,1 Rozanski A,1 Achenbach S,2 Tamarappoo B, Berman D, Dey D. 1Cedars-Sinai Medical Center, Los Angeles, CA, United States University hospital Erlangen, Erlangen, Germany Introduction Machine learning (ML) allows objective integration of clinical and imaging data for the prediction of events.. Our aim was to evaluate the performance of ML for the prediction of myocardial infarction (MI) and cardiac death (CD) in asymptomatic subjects, as well as in men and women separately.. Methods We assessed 1912 consecutive subjects [1117 (58.4%) male, age: 55.8±9.1] from the EISNER (Early Identification of Subclinical Atherosclerosis by Noninvasive Imaging Research) trial, with long-term follow-up after non-contrast CAC CT. CAC score, age-and-gender-adjusted CAC percentile, and aortic calcium scores were obtained. Comparison:Article Title: Initial evaluation of three-dimensionally printed patient-specific coronary phantoms for CT-FFR software validation Article Snippet: Image data from the phantoms were input to a Article Title: 3D Printed Cardiovascular Patient Specific Phantoms Used for Clinical Validation of a CT-derived FFR Diagnostic Software Article Snippet: This project uses a Article Title: Abstracts of the 14th Annual Scientific Meeting of the Society of Cardiovascular Computed Tomography Article Snippet: s of the 14th Annual Scientific Meeting of the Society of Cardiovascular Computed Tomography Posters Friday: Poster Session 1: Artificial Intelligence/ Machine Learning Abstracts 36-43 36 Machine Learning To Predict The Long-term Risk Of Myocardial Infarction And Cardiac Death Based On Clinical Risk, Coronary Calcium And Epicardial Adipose Tissue: Difference Between Men And Women Commandeur F, Slomka P, Göller M, Chen X, Cadet S, Razipour A,1 Gransar H,1 Cantu S,1 Miller R,1 Rozanski A,1 Achenbach S,2 Tamarappoo B, Berman D, Dey D. 1Cedars-Sinai Medical Center, Los Angeles, CA, United States University hospital Erlangen, Erlangen, Germany Introduction Machine learning (ML) allows objective integration of clinical and imaging data for the prediction of events.. Our aim was to evaluate the performance of ML for the prediction of myocardial infarction (MI) and cardiac death (CD) in asymptomatic subjects, as well as in men and women separately.. Methods We assessed 1912 consecutive subjects [1117 (58.4%) male, age: 55.8±9.1] from the EISNER (Early Identification of Subclinical Atherosclerosis by Noninvasive Imaging Research) trial, with long-term follow-up after non-contrast CAC CT. CAC score, age-and-gender-adjusted CAC percentile, and aortic calcium scores were obtained. |