AI assisted feedback system for transmit parameter optimization in Cardiac Ultrasound

Annangi, Pavan; Ravishankar, Hariharan; Patil, Rohan; Tore, Bjastaad; Aase, Svein Arne; Steen, Erik

doi:10.1109/ius46767.2020.9251501

Cited by 4 publications

(2 citation statements)

References 4 publications

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“…Overall these limitations in quality could be addressed by creating an ML model that classifies the quality of an apical four chamber view and then filters out inadequate media before predictions. Such models exist for formal echocardiography, 22,23 and could be developed for point of care ultrasound. Another solution would be AI guidance toward optimal image acquisition, such as detection of a foreshortened apical 4-chamber view.…”

Section: Effect Of Video and Image Qualitymentioning

confidence: 99%

A Stress Test of Artificial Intelligence: Can Deep Learning Models Trained From Formal Echocardiography Accurately Interpret Point‐of‐Care Ultrasound?

Crockett

Kelly

Brundage

et al. 2022

J of Ultrasound Medicine

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Objectives To test if a deep learning (DL) model trained on echocardiography images could accurately segment the left ventricle (LV) and predict ejection fraction on apical 4‐chamber images acquired by point‐of‐care ultrasound (POCUS). Methods We created a dataset of 333 videos from cardiac POCUS exams acquired in the emergency department. For each video we derived two ground‐truth labels. First, we segmented the LV from one image frame and second, we classified the EF as normal, reduced, or severely reduced. We then classified the media's quality as optimal, adequate, or inadequate. With this dataset we tested the accuracy of automated LV segmentation and EF classification by the best‐in‐class echocardiography trained DL model EchoNet‐Dynamic. Results The mean Dice similarity coefficient for LV segmentation was 0.72 (N = 333; 95% CI 0.70–0.74). Cohen's kappa coefficient for agreement between predicted and ground‐truth EF classification was 0.16 (N = 333). The area under the receiver‐operating curve for the diagnosis of heart failure was 0.74 (N = 333). Model performance improved with video quality for the tasks of LV segmentation and diagnosis of heart failure, but was unchanged with EF classification. For all tasks the model was less accurate than the published benchmarks for EchoNet‐Dynamic. Conclusions Performance of a DL model trained on formal echocardiography worsened when challenged with images captured during resuscitations. DL models intended for assessing bedside ultrasound should be trained on datasets composed of POCUS images. Such datasets have yet to be made publicly available.

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Section: Effect Of Video and Image Qualitymentioning

confidence: 99%

A Stress Test of Artificial Intelligence: Can Deep Learning Models Trained From Formal Echocardiography Accurately Interpret Point‐of‐Care Ultrasound?

Crockett

Kelly

Brundage

et al. 2022

J of Ultrasound Medicine

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“…This issue of acquisition heterogeneity is especially troublesome in AI as the impact on image quality and the ensuing lack of homogeneity is detrimental in AI-model training if not accounted for ( 22 ). For this purpose, AI-assisted feedback systems have been proposed to facilitate optimization of parameters including depth, gain and frequency ( 23 ). Additionally, AI-based denoising and artifact removal tools have been recently developed for transthoracic echocardiographic imaging in congenital heart disease that can further standardize image quality ( 7 ).…”

Section: Part I: Optimizing the Pediatric Echocardiogrammentioning

confidence: 99%

Artificial intelligence in the pediatric echocardiography laboratory: Automation, physiology, and outcomes

et al. 2022

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Artificial intelligence (AI) is frequently used in non-medical fields to assist with automation and decision-making. The potential for AI in pediatric cardiology, especially in the echocardiography laboratory, is very high. There are multiple tasks AI is designed to do that could improve the quality, interpretation, and clinical application of echocardiographic data at the level of the sonographer, echocardiographer, and clinician. In this state-of-the-art review, we highlight the pertinent literature on machine learning in echocardiography and discuss its applications in the pediatric echocardiography lab with a focus on automation of the pediatric echocardiogram and the use of echo data to better understand physiology and outcomes in pediatric cardiology. We also discuss next steps in utilizing AI in pediatric echocardiography.

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Predictive Analysis in Health Care System Using AI

Agarwal

2021

Artificial Intelligence in Healthcare

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AI assisted feedback system for transmit parameter optimization in Cardiac Ultrasound

Cited by 4 publications

References 4 publications

A Stress Test of Artificial Intelligence: Can Deep Learning Models Trained From Formal Echocardiography Accurately Interpret Point‐of‐Care Ultrasound?

A Stress Test of Artificial Intelligence: Can Deep Learning Models Trained From Formal Echocardiography Accurately Interpret Point‐of‐Care Ultrasound?

Artificial intelligence in the pediatric echocardiography laboratory: Automation, physiology, and outcomes

Predictive Analysis in Health Care System Using AI

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