Erik Smistad
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View article: Real-time guidance and automated measurements to improve echocardiographic assessment of left ventricular size and function
Real-time guidance and automated measurements to improve echocardiographic assessment of left ventricular size and function Open
Background The low reproducibility of echocardiographic measurements limits the ability to detect subtle changes in left ventricular (LV) function. Deep learning (DL) methods enable real-time analysis of acquisitions and may improve standa…
View article: Generative augmentations for improved cardiac ultrasound segmentation using diffusion models
Generative augmentations for improved cardiac ultrasound segmentation using diffusion models Open
One of the main challenges in current research on segmentation in cardiac ultrasound is the lack of large and varied labeled datasets and the differences in annotation conventions between datasets. This makes it difficult to design robust …
View article: Real-Time Global Longitudinal Strain During Echocardiography: A Deep Learning Platform for Improved Workflow
Real-Time Global Longitudinal Strain During Echocardiography: A Deep Learning Platform for Improved Workflow Open
The DL platform for fully automated real-time GLS measurements was feasible, precise, and time efficient. Real-time DL-based feedback allows operators to optimize images during acquisition, thus improving quality metrics relevant to GLS an…
View article: Immunohistochemistry guided segmentation of benign epithelial cells, in situ lesions, and invasive epithelial cells in breast cancer slides
Immunohistochemistry guided segmentation of benign epithelial cells, in situ lesions, and invasive epithelial cells in breast cancer slides Open
Digital pathology enables automatic analysis of histopathological sections using artificial intelligence. Automatic evaluation could improve diagnostic efficiency and find associations between morphological features and clinical outcome. F…
View article: Real-time guidance and automated measurements using deep learning to improve echocardiographic assessment of left ventricular size and function
Real-time guidance and automated measurements using deep learning to improve echocardiographic assessment of left ventricular size and function Open
Aims The low reproducibility of echocardiographic measurements challenges the identification of subtle changes in left ventricular (LV) function. Deep learning (DL) methods enable real-time analysis of acquisitions and may improve echocard…
View article: Real-time deep learning-based image guiding and automated left ventricular measurements to reduce test-retest variability
Real-time deep learning-based image guiding and automated left ventricular measurements to reduce test-retest variability Open
Aims To evaluate the effect of combining real-time deep learning (DL)-based guiding and automated measurements of left ventricular (LV) volumetric measurements and strain. Methods and results Patients (n=47) with mixed cardiac pathology we…
View article: Predicting estrogen receptor status from HE-stained breast cancer slides using artificial intelligence
Predicting estrogen receptor status from HE-stained breast cancer slides using artificial intelligence Open
Introduction The estrogen receptor (ER) is routinely assessed by immunohistochemistry (IHC) in breast cancer to stratify patients into therapeutic and prognostic groups. Pathology laboratories are burdened by an increased number of biopsie…
View article: Generative augmentations for improved cardiac ultrasound segmentation using diffusion models
Generative augmentations for improved cardiac ultrasound segmentation using diffusion models Open
One of the main challenges in current research on segmentation in cardiac ultrasound is the lack of large and varied labeled datasets and the differences in annotation conventions between datasets. This makes it difficult to design robust …
View article: Novel automated quality indicators for echocardiography and their importance for left ventricular strain
Novel automated quality indicators for echocardiography and their importance for left ventricular strain Open
Background Until now, no method is available for automatic extraction of indicators of image quality and view standardization from echocardiographic recordings. Thus, description of data quality has been indirect, often by description of o…
View article: Deep learning improves test–retest reproducibility of regional strain in echocardiography
Deep learning improves test–retest reproducibility of regional strain in echocardiography Open
Aims The clinical utility of regional strain measurements in echocardiography is challenged by suboptimal reproducibility. In this study, we aimed to evaluate the test–retest reproducibility of regional longitudinal strain (RLS) per corona…
View article: Improving time-efficiency and accuracy in echocardiography: real-time automated measurements of left ventricular global longitudinal strain using deep learning
Improving time-efficiency and accuracy in echocardiography: real-time automated measurements of left ventricular global longitudinal strain using deep learning Open
Background Accurate quantification of left ventricular (LV) systolic function is fundamental in echocardiography. LV Global Longitudinal Strain (GLS) offers advantages over LV ejection fraction, being more sensitive, reproducible and offer…
View article: Deep learning for fully automated echocardiographic measurements of left ventricular wall thickness and chamber dimensions in the parasternal long-axis view
Deep learning for fully automated echocardiographic measurements of left ventricular wall thickness and chamber dimensions in the parasternal long-axis view Open
Background Accurate quantification of left ventricular (LV) wall thickness and chamber dimensions in the echocardiographic parasternal long-axis view (PLAX) is crucial for clinical decisions in patients with heart disease. However, there i…
View article: Toward Robust Cardiac Segmentation Using Graph Convolutional Networks
Toward Robust Cardiac Segmentation Using Graph Convolutional Networks Open
Fully automatic cardiac segmentation can be a fast and reproducible method to extract clinical measurements from an echocardiography examination. The U-Net architecture is the current state-of-the-art deep learning architecture for medical…
View article: Immunohistochemistry guided segmentation of benign epithelial cells, in situ lesions, and invasive epithelial cells in breast cancer slides
Immunohistochemistry guided segmentation of benign epithelial cells, in situ lesions, and invasive epithelial cells in breast cancer slides Open
Digital pathology enables automatic analysis of histopathological sections using artificial intelligence (AI). Automatic evaluation could improve diagnostic efficiency and help find associations between morphological features and clinical …
View article: Automatic measurements of left ventricular volumes and ejection fraction by artificial intelligence: clinical validation in real time and large databases
Automatic measurements of left ventricular volumes and ejection fraction by artificial intelligence: clinical validation in real time and large databases Open
Aims Echocardiography is a cornerstone in cardiac imaging, and left ventricular (LV) ejection fraction (EF) is a key parameter for patient management. Recent advances in artificial intelligence (AI) have enabled fully automatic measurement…
View article: Automated 2-D and 3-D Left Atrial Volume Measurements Using Deep Learning
Automated 2-D and 3-D Left Atrial Volume Measurements Using Deep Learning Open
Our results highlight the potential of automated LA volume estimation in clinical practice. The proposed prototype application, capable of processing real-time data from a clinical ultrasound scanner, provides valuable temporal volume curv…
View article: Real-time guidance by deep learning of experienced operators to improve the standardization of echocardiographic acquisitions
Real-time guidance by deep learning of experienced operators to improve the standardization of echocardiographic acquisitions Open
Aims Impaired standardization of echocardiograms may increase inter-operator variability. This study aimed to determine whether the real-time guidance of experienced sonographers by deep learning (DL) could improve the standardization of a…
View article: The impact of real-time guiding by deep learning on test-retest variability of automated left ventricular systolic function measurements
The impact of real-time guiding by deep learning on test-retest variability of automated left ventricular systolic function measurements Open
Funding Acknowledgements Type of funding sources: Public grant(s) – National budget only. Main funding source(s): Norwegian University of Science and Technology, St. Olavs University Hospital, Central-Norway Health Authority. Background Le…
View article: Automatic measurements of left ventricular volumes and ejection fraction by artificial intelligence reduces time-consumption and inter-observer variability
Automatic measurements of left ventricular volumes and ejection fraction by artificial intelligence reduces time-consumption and inter-observer variability Open
Funding Acknowledgements Type of funding sources: Public Institution(s). Main funding source(s): Norwegian University of Science and Technology. Background Left ventricular (LV) volumes and ejection fraction (EF) are the most used and stud…
View article: Real-time guiding by deep learning during echocardiography to reduce left ventricular foreshortening and measurement variability
Real-time guiding by deep learning during echocardiography to reduce left ventricular foreshortening and measurement variability Open
Aims Apical foreshortening leads to an underestimation of left ventricular (LV) volumes and an overestimation of LV ejection fraction and global longitudinal strain. Real-time guiding using deep learning (DL) during echocardiography to red…
View article: Noninvasive intracranial pressure assessment by optic nerve sheath diameter: Automated measurements as an alternative to clinician-performed measurements
Noninvasive intracranial pressure assessment by optic nerve sheath diameter: Automated measurements as an alternative to clinician-performed measurements Open
Introduction Optic nerve sheath diameter (ONSD) has shown promise as a noninvasive parameter for estimating intracranial pressure (ICP). In this study, we evaluated a novel automated method of measuring the ONSD in transorbital ultrasound …
View article: Real-Time Echocardiography Guidance for Optimized Apical Standard Views
Real-Time Echocardiography Guidance for Optimized Apical Standard Views Open
Measurements of cardiac function such as left ventricular ejection fraction and myocardial strain are typically based on 2-D ultrasound imaging. The reliability of these measurements depends on the correct pose of the transducer such that …
View article: Automated analyses and real-time guiding by deep learning to reduce test-retest variability of global longitudinal strain
Automated analyses and real-time guiding by deep learning to reduce test-retest variability of global longitudinal strain Open
Background Global longitudinal strain (GLS) is recommended for assessment of left ventricular (LV) function. Test-retest variability of GLS rely on recordings and analyses. Foreshortened LV recordings are shown to reduce length measurement…
View article: H2G-Net: A multi-resolution refinement approach for segmentation of breast cancer region in gigapixel histopathological images
H2G-Net: A multi-resolution refinement approach for segmentation of breast cancer region in gigapixel histopathological images Open
Over the past decades, histopathological cancer diagnostics has become more complex, and the increasing number of biopsies is a challenge for most pathology laboratories. Thus, development of automatic methods for evaluation of histopathol…
View article: Real-Time Echocardiography Guidance for Optimized Apical Standard Views
Real-Time Echocardiography Guidance for Optimized Apical Standard Views Open
Measurements of cardiac function such as left ventricular ejection fraction and myocardial strain are typically based on 2D ultrasound imaging. The reliability of these measurements strongly depends on the correct pose of the transducer su…
View article: Real-Time Echocardiography Guidance for Optimized Apical Standard Views
Real-Time Echocardiography Guidance for Optimized Apical Standard Views Open
Measurements of cardiac function such as left ventricular ejection fraction and myocardial strain are typically based on 2D ultrasound imaging. The reliability of these measurements strongly depends on the correct pose of the transducer su…
View article: The impact of real-time feedback by deep learning during echocardiographic scanning on test-retest variability of left ventricular systolic function measurements
The impact of real-time feedback by deep learning during echocardiographic scanning on test-retest variability of left ventricular systolic function measurements Open
Funding Acknowledgements Type of funding sources: Public grant(s) – National budget only. Main funding source(s): Norwegian University of Science and Technology, St. Olavs University Hospital, Central-Norway Health Authority OnBehalf Depar…
View article: Automatic measurement of LV wall thickness from 2D cardiac echocardiography
Automatic measurement of LV wall thickness from 2D cardiac echocardiography Open
Funding Acknowledgements Type of funding sources: Public grant(s) – EU funding. Main funding source(s): PIC from European Union"s Horizon 2020 Marie Skłodowska-Curie Actions ITN Background The wall thickness of the left ventricle (LV) is a…