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Mashup Score: 1
From exploring AI in recommender systems to taking a plunge into healthcare, my journey into AI has been both exhilarating and eye-opening…
Source: anya-chauhan.medium.comCategories: General Medicine News, CardiologistsTweet
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Mashup Score: 36
Use of artificial intelligence (AI) in cardiovascular imaging may potentially augment clinical decision-making in disease management, but no prospective randomized controlled trials have assessed t…
Source: ai.nejm.orgCategories: General Medicine News, CardiologistsTweet-
Congratulations to @DrRossUpton @GarywoodwardPHD and the rest of the @ultromics team for publishing the first multiple center RCT of stress echocardiography in @NEJM_AI! https://t.co/IEjYIWwlvQ @ASE360 @PaulLeeson @NIHRresearch
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Mashup Score: 6Paper page - EchoPrime: A Multi-Video View-Informed Vision-Language Model for Comprehensive Echocardiography Interpretation - 2 month(s) ago
Join the discussion on this paper page
Source: huggingface.coCategories: General Medicine News, Cardiologists1Tweet-
RT @milos_ai: EchoPrime is on Hugging Face daily papers 🚀 https://t.co/MzQXgAM4tF https://t.co/Wfk3xCqJiW
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Mashup Score: 51Clinical and genetic associations of asymmetric apical and septal left ventricular hypertrophy - 3 month(s) ago
AbstractAims. Increased left ventricular mass has been associated with adverse cardiovascular outcomes including incident cardiomyopathy and atrial fibrill
Source: academic.oup.comCategories: General Medicine News, CardiologistsTweet-
Now published in European Heart Journal Digital Health! https://t.co/6pFdZWZPKJ https://t.co/1ELmUFYjIG
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Mashup Score: 50Clinical and genetic associations of asymmetric apical and septal left ventricular hypertrophy - 3 month(s) ago
AbstractAims. Increased left ventricular mass has been associated with adverse cardiovascular outcomes including incident cardiomyopathy and atrial fibrill
Source: academic.oup.comCategories: General Medicine News, CardiologistsTweet-
Now published in European Heart Journal Digital Health! https://t.co/6pFdZWZPKJ https://t.co/1ELmUFYjIG
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Mashup Score: 29Circulation September 17, 2024 Issue - 3 month(s) ago
This week, please join a panel session with authors Timothy Poterucha and David Ouyang, Editorialist Anna Sannino, and Senior Associate Editor Victoria Delgado
Source: podcasts.apple.comCategories: General Medicine News, CardiologistsTweet-
Excited to share this work lead by @StanfordMedRes and former @SarnoffCardio fellow @amey_vrudhula! Discussed in this week’s @CircAHA podcast, with @timpotsMD @VDelgadoGarcia @AnnaSannino1985 and Peder Myhre! https://t.co/Q9cuE72F2m https://t.co/sE8okj6UAZ
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Mashup Score: 68
BACKGROUND: Diagnosis of mitral regurgitation (MR) requires careful evaluation by echocardiography with Doppler imaging. This study presents the development and validation of a fully automated deep learning pipeline for identifying apical 4-chamber view videos with color Doppler echocardiography and detecting clinically significant (moderate or severe) MR from transthoracic echocardiograms. METHODS: A total of 58 614 transthoracic echocardiograms (2 587 538 videos) from Cedars-Sinai Medical Center were used to develop and test an automated pipeline to identify apical 4-chamber view videos with color Doppler across the mitral valve and then assess MR severity. The model was tested internally on a test set of 1800 studies (80 833 videos) from Cedars-Sinai Medical Center and externally evaluated in a geographically distinct cohort of 915 studies (46 890 videos) from Stanford Healthcare. RESULTS: In the held-out Cedars-Sinai Medical Center test set, the view classifier demonstrated an area
Source: www.ahajournals.orgCategories: General Medicine News, CardiologistsTweet-
Congrats to @SarnoffCardio fellow and @StanfordMedRes @amey_vrudhula for publishing in @CircAHA! Using deep learning, we view classify and quantify MR severity in #echofirst. Currently the only such project with open code and model weights. Paper: https://t.co/ZtqQQy7x2C Code:… https://t.co/LUXkgQZhgc https://t.co/g1KpjJqtQZ https://t.co/XdrBrxQeAz
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Mashup Score: 6Clinical and genetic associations of asymmetric apical and septal left ventricular hypertrophy - 4 month(s) ago
AbstractBackground. Increased left ventricular mass has been associated with adverse cardiovascular outcomes including incident cardiomyopathy and atrial f
Source: academic.oup.comCategories: General Medicine News, CardiologistsTweet-
@jpirruccello @shaan_khurshid @UKBIOBANK Most hits were associated with total LV mass and replicated in prior studies, however there were interesting though provoking associations for disproportionate phenotypes. Read more at: https://t.co/Ql2RphxKaf https://t.co/6nsTELfv8F
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Mashup Score: 6Clinical and genetic associations of asymmetric apical and septal left ventricular hypertrophy - 4 month(s) ago
AbstractBackground. Increased left ventricular mass has been associated with adverse cardiovascular outcomes including incident cardiomyopathy and atrial f
Source: academic.oup.comCategories: General Medicine News, CardiologistsTweet-
@jpirruccello @shaan_khurshid @UKBIOBANK Most hits were associated with total LV mass and replicated in prior studies, however there were interesting though provoking associations for disproportionate phenotypes. Read more at: https://t.co/Ql2RphxKaf https://t.co/6nsTELfv8F
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Mashup Score: 65
BACKGROUND: Diagnosis of mitral regurgitation (MR) requires careful evaluation by echocardiography with Doppler imaging. This study presents the development and validation of a fully automated deep learning pipeline for identifying apical 4-chamber view videos with color Doppler echocardiography and detecting clinically significant (moderate or severe) MR from transthoracic echocardiograms. METHODS: A total of 58 614 transthoracic echocardiograms (2 587 538 videos) from Cedars-Sinai Medical Center were used to develop and test an automated pipeline to identify apical 4-chamber view videos with color Doppler across the mitral valve and then assess MR severity. The model was tested internally on a test set of 1800 studies (80 833 videos) from Cedars-Sinai Medical Center and externally evaluated in a geographically distinct cohort of 915 studies (46 890 videos) from Stanford Healthcare. RESULTS: In the held-out Cedars-Sinai Medical Center test set, the view classifier demonstrated an area
Source: www.ahajournals.orgCategories: General Medicine News, CardiologistsTweet-
Congrats to @SarnoffCardio fellow and @StanfordMedRes @amey_vrudhula for publishing in @CircAHA! Using deep learning, we view classify and quantify MR severity in #echofirst. Currently the only such project with open code and model weights. Paper: https://t.co/ZtqQQy7x2C Code:… https://t.co/LUXkgQZhgc https://t.co/g1KpjJqtQZ https://t.co/XdrBrxQeAz
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RT @milos_ai: Check out Anya's medium post about our summer project "Recruiting Digital Patients". https://t.co/FXmHtgUMBs