Interpretable AI for beat-to-beat cardiac function assessment

Ouyang, David; He, Bryan; Ghorbani, Amirata; Langlotz, Curt P.; Heidenreich, Paul A.; Harrington, Robert A.; Liang, David; Ashley, Euan A.; Zou, James

doi:10.1101/19012419

Cited by 15 publications

(29 citation statements)

References 35 publications

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“…Two publicly available datasets in echocardiography -Cardiac Acquisitions for Multi-structure Ultrasound Segmentation (CAMUS) [15] and Dynamic-Echonet [19] are used for the experiments. The former dataset has 2D apical four-chamber and two-chamber view sequences of 500 patients.…”

Section: Datasetmentioning

confidence: 99%

“…The US images for ED and ES stages are extracted from the video and frame information, and the ground truth is created from the expert tracings of the left ventricle. The dataset is split into 14956 training, 2552 validation and 2552 testing images with the same split as [19].…”

Section: Datasetmentioning

confidence: 99%

“…The Dynamic-Echonet dataset is trained with EchoNet-Dynamic architecture [19] with the author's open source implementation 2 . It uses a DeepLabv3 [5] model with ResNet50 [8] as the main feature extractor.…”

Section: Semantic Segmentationmentioning

confidence: 99%

“…The recent success of Deep Learning (DL) has shown great promise in developing automated methods in 2D echocardiography. DL based methods have been used for automated cardiac structure and function assessment, and view classification [24,17,15,19]. Left ventricle (LV) volume is one of the essential measures in the cardiac US, which can help accurately estimate ejection fraction [15] and would be an integral part of automated echocardiography.…”

Section: Introductionmentioning

confidence: 99%

“…Left ventricle (LV) volume is one of the essential measures in the cardiac US, which can help accurately estimate ejection fraction [15] and would be an integral part of automated echocardiography. Although several methods have been developed for the automated segmentation of LV [15,19] in 2D echo images, most of these methods do not include uncertainty estimation. Uncertainty estimation could be beneficial during the image acquisition phase (by providing feedback to the operator on image's quality) [4] and during interpretation, providing confidence on the automated measurements obtained from the model to support clinical decision making [18].…”

Section: Introductionmentioning

confidence: 99%

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Uncertainty Estimation in Deep 2D Echocardiography Segmentation

Dahal¹,

Kafle²,

Khanal³

2020

Preprint

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2D echocardiography is the most common imaging modality for cardiovascular diseases. The portability and relatively low-cost nature of Ultrasound (US) enable the US devices needed for performing echocardiography to be made widely available. However, acquiring and interpreting cardiac US images is operator dependent, limiting its use to only places where experts are present. Recently, Deep Learning (DL) has been used in 2D echocardiography for automated view classification, and structure and function assessment. Although these recent works show promise in developing computer-guided acquisition and automated interpretation of echocardiograms, most of these methods do not model and estimate uncertainty which can be important when testing on data coming from a distribution further away from that of the training data. Uncertainty estimates can be beneficial both during the image acquisition phase (by providing real-time feedback to the operator on acquired image's quality), and during automated measurement and interpretation. The performance of uncertainty models and quantification metric may depend on the prediction task and the models being compared. Hence, to gain insight of uncertainty modelling for left ventricular segmentation from US images, we compare three ensembling based uncertainty models quantified using four different metrics (one newly proposed) on state-of-the-art baseline networks using two publicly available echocardiogram datasets. We further demonstrate how uncertainty estimation can be used to automatically reject poor quality images and improve state-of-the-art segmentation results.

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Section: Datasetmentioning

confidence: 99%

Section: Datasetmentioning

confidence: 99%

Section: Semantic Segmentationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Uncertainty Estimation in Deep 2D Echocardiography Segmentation

Dahal¹,

Kafle²,

Khanal³

2020

Preprint

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Deep Learning for Cardiovascular Imaging

Wehbe,

Katsaggelos,

Hammond

et al. 2023

JAMA Cardiol

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ImportanceArtificial intelligence (AI), driven by advances in deep learning (DL), has the potential to reshape the field of cardiovascular imaging (CVI). While DL for CVI is still in its infancy, research is accelerating to aid in the acquisition, processing, and/or interpretation of CVI across various modalities, with several commercial products already in clinical use. It is imperative that cardiovascular imagers are familiar with DL systems, including a basic understanding of how they work, their relative strengths compared with other automated systems, and possible pitfalls in their implementation. The goal of this article is to review the methodology and application of DL to CVI in a simple, digestible fashion toward demystifying this emerging technology.ObservationsAt its core, DL is simply the application of a series of tunable mathematical operations that translate input data into a desired output. Based on artificial neural networks that are inspired by the human nervous system, there are several types of DL architectures suited to different tasks; convolutional neural networks are particularly adept at extracting valuable information from CVI data. We survey some of the notable applications of DL to tasks across the spectrum of CVI modalities. We also discuss challenges in the development and implementation of DL systems, including avoiding overfitting, preventing systematic bias, improving explainability, and fostering a human-machine partnership. Finally, we conclude with a vision of the future of DL for CVI.Conclusions and RelevanceDeep learning has the potential to meaningfully affect the field of CVI. Rather than a threat, DL could be seen as a partner to cardiovascular imagers in reducing technical burden and improving efficiency and quality of care. High-quality prospective evidence is still needed to demonstrate how the benefits of DL CVI systems may outweigh the risks.

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