Erik Smistad

1.8k total citations
53 papers, 1.1k citations indexed

About

Erik Smistad is a scholar working on Radiology, Nuclear Medicine and Imaging, Cardiology and Cardiovascular Medicine and Computer Vision and Pattern Recognition. According to data from OpenAlex, Erik Smistad has authored 53 papers receiving a total of 1.1k indexed citations (citations by other indexed papers that have themselves been cited), including 36 papers in Radiology, Nuclear Medicine and Imaging, 30 papers in Cardiology and Cardiovascular Medicine and 13 papers in Computer Vision and Pattern Recognition. Recurrent topics in Erik Smistad's work include Cardiovascular Function and Risk Factors (28 papers), Cardiac Imaging and Diagnostics (26 papers) and Cardiac Valve Diseases and Treatments (17 papers). Erik Smistad is often cited by papers focused on Cardiovascular Function and Risk Factors (28 papers), Cardiac Imaging and Diagnostics (26 papers) and Cardiac Valve Diseases and Treatments (17 papers). Erik Smistad collaborates with scholars based in Norway, France and United States. Erik Smistad's co-authors include Frank Lindseth, Lasse Løvstakken, Andreas Østvik, Anne C. Elster, Bjørn Olav Haugen, Ivar Mjåland Salte, Bjørnar Grenne, Thor Edvardsen, Harald Brunvand and Kristina H. Haugaa and has published in prestigious journals such as PLoS ONE, European Heart Journal and IEEE Access.

In The Last Decade

Erik Smistad

45 papers receiving 1.1k citations

Peers — A (Enhanced Table)

Peers by citation overlap · career bar shows stage (early→late) cites · hero ref

Name h Career Trend Papers Cites
Erik Smistad Norway 19 583 449 284 205 155 53 1.1k
Timothy J. W. Dawes United Kingdom 17 615 1.1× 531 1.2× 534 1.9× 227 1.1× 168 1.1× 39 1.5k
Nicolás Duchateau Spain 17 482 0.8× 702 1.6× 171 0.6× 211 1.0× 160 1.0× 48 1.1k
Bryan He United States 14 672 1.2× 610 1.4× 119 0.4× 178 0.9× 145 0.9× 31 1.6k
Kristen M. Meiburger Italy 24 582 1.0× 400 0.9× 274 1.0× 339 1.7× 443 2.9× 90 1.5k
Rhodri Davies United Kingdom 19 428 0.7× 246 0.5× 706 2.5× 341 1.7× 61 0.4× 65 1.5k
Alberto Gómez United Kingdom 17 232 0.4× 282 0.6× 101 0.4× 244 1.2× 213 1.4× 55 779
Bob D. de Vos Netherlands 15 1.1k 1.9× 318 0.7× 435 1.5× 679 3.3× 185 1.2× 38 1.7k
Mathieu De Craene Spain 21 852 1.5× 786 1.8× 361 1.3× 351 1.7× 175 1.1× 82 1.6k
Avan Suinesiaputra New Zealand 19 773 1.3× 703 1.6× 264 0.9× 242 1.2× 99 0.6× 60 1.3k
Saša Grbić United States 16 496 0.9× 124 0.3× 183 0.6× 177 0.9× 205 1.3× 31 857

Countries citing papers authored by Erik Smistad

Since Specialization
Citations

This map shows the geographic impact of Erik Smistad's research. It shows the number of citations coming from papers published by authors working in each country. You can also color the map by specialization and compare the number of citations received by Erik Smistad with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Erik Smistad more than expected).

Fields of papers citing papers by Erik Smistad

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Erik Smistad. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the papers produced by Erik Smistad. The network helps show where Erik Smistad may publish in the future.

Co-authorship network of co-authors of Erik Smistad

This figure shows the co-authorship network connecting the top 25 collaborators of Erik Smistad. A scholar is included among the top collaborators of Erik Smistad based on the total number of citations received by their joint publications. Widths of edges represent the number of papers authors have co-authored together. Node borders signify the number of papers an author published with Erik Smistad. Erik Smistad is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

20 of 20 papers shown
1.
Ytterhus, Borgny, Cecilia Lindskog, Elisabeth Wik, et al.. (2025). Immunohistochemistry guided segmentation of benign epithelial cells, in situ lesions, and invasive epithelial cells in breast cancer slides. PLoS ONE. 20(7). e0328033–e0328033.
2.
Østvik, Andreas, Erik Smistad, Bjørnar Grenne, et al.. (2025). Effect of apical foreshortening and transducer angulation on strain measurements: a quantitative investigation. European Heart Journal - Cardiovascular Imaging. 26(Supplement_1).
3.
Smistad, Erik, Andreas Østvik, Espen Holte, et al.. (2025). Deep learning in echocardiography: real-time measurements of left ventricular wall thickness and chamber dimensions in the parasternal long-axis view. European Heart Journal - Cardiovascular Imaging. 26(Supplement_1).
4.
Dalen, Håvard, et al.. (2024). Toward Robust Cardiac Segmentation Using Graph Convolutional Networks. IEEE Access. 12. 33876–33888. 4 indexed citations
5.
Østvik, Andreas, Ivar Mjåland Salte, Sigve Karlsen, et al.. (2024). Deep learning improves test–retest reproducibility of regional strain in echocardiography. PubMed. 2(4). qyae092–qyae092. 1 indexed citations
6.
Smistad, Erik, Jieyu Hu, Andreas Østvik, et al.. (2023). Automatic measurements of left ventricular volumes and ejection fraction by artificial intelligence: clinical validation in real time and large databases. European Heart Journal - Cardiovascular Imaging. 25(3). 383–395. 22 indexed citations
7.
Smistad, Erik, Andreas Østvik, Bjørnar Grenne, et al.. (2023). Real-time guidance by deep learning of experienced operators to improve the standardization of echocardiographic acquisitions. PubMed. 1(2). qyad040–qyad040. 5 indexed citations
8.
Hu, Jieyu, et al.. (2023). Automated 2-D and 3-D Left Atrial Volume Measurements Using Deep Learning. Ultrasound in Medicine & Biology. 50(1). 47–56. 2 indexed citations
9.
Salte, Ivar Mjåland, Andreas Østvik, Sigve Karlsen, et al.. (2023). Deep Learning for Improved Precision and Reproducibility of Left Ventricular Strain in Echocardiography: A Test-Retest Study. Journal of the American Society of Echocardiography. 36(7). 788–799. 23 indexed citations
10.
Smistad, Erik, et al.. (2023). Real-time guiding by deep learning during echocardiography to reduce left ventricular foreshortening and measurement variability. PubMed. 1(1). qyad012–qyad012. 4 indexed citations
11.
12.
Pettersen, Henrik Sahlin, Ilya Belevich, Elin Synnøve Røyset, et al.. (2022). Code-Free Development and Deployment of Deep Segmentation Models for Digital Pathology. Frontiers in Medicine. 8. 816281–816281. 12 indexed citations
13.
Østvik, Andreas, Ivar Mjåland Salte, Erik Smistad, et al.. (2021). Myocardial Function Imaging in Echocardiography Using Deep Learning. IEEE Transactions on Medical Imaging. 40(5). 1340–1351. 47 indexed citations
14.
Smistad, Erik, Ivar Mjåland Salte, Håvard Dalen, & Lasse Løvstakken. (2021). Real-time temporal coherent left ventricle segmentation using convolutional LSTMs. 1–4. 5 indexed citations
15.
Salte, Ivar Mjåland, Andreas Østvik, Erik Smistad, et al.. (2021). Artificial Intelligence for Automatic Measurement of Left Ventricular Strain in Echocardiography. JACC. Cardiovascular imaging. 14(10). 1918–1928. 86 indexed citations
16.
Smistad, Erik, Andreas Østvik, Ivar Mjåland Salte, et al.. (2020). Real-Time Automatic Ejection Fraction and Foreshortening Detection Using Deep Learning. IEEE Transactions on Ultrasonics Ferroelectrics and Frequency Control. 67(12). 2595–2604. 53 indexed citations
17.
Østvik, Andreas, Erik Smistad, Svein Arne Aase, Bjørn Olav Haugen, & Lasse Løvstakken. (2018). Real-Time Standard View Classification in Transthoracic Echocardiography Using Convolutional Neural Networks. Ultrasound in Medicine & Biology. 45(2). 374–384. 82 indexed citations
18.
Smistad, Erik, Andreas Østvik, Bjørn Olav Haugen, & Lasse Løvstakken. (2017). 2D left ventricle segmentation using deep learning. 2017 IEEE International Ultrasonics Symposium (IUS). 1–4. 50 indexed citations
19.
Smistad, Erik, et al.. (2016). Automatic Segmentation and Probe Guidance for Real-Time Assistance of Ultrasound-Guided Femoral Nerve Blocks. Ultrasound in Medicine & Biology. 43(1). 218–226. 17 indexed citations
20.
Smistad, Erik, et al.. (2015). FAST: framework for heterogeneous medical image computing and visualization. International Journal of Computer Assisted Radiology and Surgery. 10(11). 1811–1822. 31 indexed citations

Rankless uses publication and citation data sourced from OpenAlex, an open and comprehensive bibliographic database. While OpenAlex provides broad and valuable coverage of the global research landscape, it—like all bibliographic datasets—has inherent limitations. These include incomplete records, variations in author disambiguation, differences in journal indexing, and delays in data updates. As a result, some metrics and network relationships displayed in Rankless may not fully capture the entirety of a scholar's output or impact.

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