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Literature DB >> 35132500

Automated algorithms in diastology: how to move forward?

Mihai Strachinaru^1,2, Johan G Bosch³.

Abstract

Entities: Chemical

Year: 2022 PMID： 35132500 DOI： 10.1007/s10554-021-02505-8

Source DB: PubMed Journal: Int J Cardiovasc Imaging ISSN： 1569-5794 Impact factor: 2.357

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References

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21 in total

1. Artificial Intelligence-Based Assessment of Left Ventricular Filling Pressures From 2-Dimensional Cardiac Ultrasound Images.

Authors: Alaa Mabrouk Salem Omar; Khader Shameer; Sukrit Narula; Mohamed Ahmed Abdel Rahman; Osama Rifaie; Jagat Narula; Joel T Dudley; Partho P Sengupta
Journal: JACC Cardiovasc Imaging Date: 2017-07-19

2. Recommendations for the Evaluation of Left Ventricular Diastolic Function by Echocardiography: An Update from the American Society of Echocardiography and the European Association of Cardiovascular Imaging.

Authors: Sherif F Nagueh; Otto A Smiseth; Christopher P Appleton; Benjamin F Byrd; Hisham Dokainish; Thor Edvardsen; Frank A Flachskampf; Thierry C Gillebert; Allan L Klein; Patrizio Lancellotti; Paolo Marino; Jae K Oh; Bogdan Alexandru Popescu; Alan D Waggoner
Journal: J Am Soc Echocardiogr Date: 2016-04 Impact factor: 5.251

3. Machine Learning Analysis of Left Ventricular Function to Characterize Heart Failure With Preserved Ejection Fraction.

Authors: Sergio Sanchez-Martinez; Nicolas Duchateau; Tamas Erdei; Gabor Kunszt; Svend Aakhus; Anna Degiovanni; Paolo Marino; Erberto Carluccio; Gemma Piella; Alan G Fraser; Bart H Bijnens
Journal: Circ Cardiovasc Imaging Date: 2018-04 Impact factor: 7.792

4. Phenomapping of patients with heart failure with preserved ejection fraction using machine learning-based unsupervised cluster analysis.

Authors: Matthew W Segar; Kershaw V Patel; Colby Ayers; Mujeeb Basit; W H Wilson Tang; Duwayne Willett; Jarett Berry; Justin L Grodin; Ambarish Pandey
Journal: Eur J Heart Fail Date: 2019-10-21 Impact factor: 15.534

5. Impact of the 2016 ASE/EACVI recommendations on the prevalence of diastolic dysfunction in the general population.

Authors: João G Almeida; Ricardo Fontes-Carvalho; Francisco Sampaio; José Ribeiro; Paulo Bettencourt; Frank A Flachskampf; Adelino Leite-Moreira; Ana Azevedo
Journal: Eur Heart J Cardiovasc Imaging Date: 2018-04-01 Impact factor: 6.875

6. Echocardiographic Features of Patients With Heart Failure and Preserved Left Ventricular Ejection Fraction.

Authors: Amil M Shah; Maja Cikes; Narayana Prasad; Guichu Li; Stoyan Getchevski; Brian Claggett; Adel Rizkala; Ilya Lukashevich; Eileen O'Meara; John J Ryan; Sanjiv J Shah; Wilfred Mullens; Michael R Zile; Carolyn S P Lam; John J V McMurray; Scott D Solomon
Journal: J Am Coll Cardiol Date: 2019-12-10 Impact factor: 24.094

Automated algorithms in diastology: how to move forward?

1. Artificial Intelligence-Based Assessment of Left Ventricular Filling Pressures From 2-Dimensional Cardiac Ultrasound Images.

2. Recommendations for the Evaluation of Left Ventricular Diastolic Function by Echocardiography: An Update from the American Society of Echocardiography and the European Association of Cardiovascular Imaging.

3. Machine Learning Analysis of Left Ventricular Function to Characterize Heart Failure With Preserved Ejection Fraction.

4. Phenomapping of patients with heart failure with preserved ejection fraction using machine learning-based unsupervised cluster analysis.

5. Impact of the 2016 ASE/EACVI recommendations on the prevalence of diastolic dysfunction in the general population.

6. Echocardiographic Features of Patients With Heart Failure and Preserved Left Ventricular Ejection Fraction.

7. Interpatient Similarities in Cardiac Function: A Platform for Personalized Cardiovascular Medicine.

8. A Network-Based "Phenomics" Approach for Discovering Patient Subtypes From High-Throughput Cardiac Imaging Data.

9. A simple, fast and reproducible echocardiographic approach to grade left ventricular diastolic function.

10. Artificial intelligence for the diagnosis of heart failure.