Quantitative Echocardiography: Real-Time Quality Estimation and View Classification Implemented on a Mobile Android Device

Woudenberg, Nathan Van; Liao, Zhibin; Abdi, Ali A.; Girgis, Hani Z.; Luong, Christina; Vaseli, Hooman; Behnami, Delaram; Zhang, Haotian; Gin, Kenneth; Rohling, Robert; Tsang, Teresa S.M.; Abolmaesumi, Purang

doi:10.1007/978-3-030-01045-4_9

Cited by 12 publications

(6 citation statements)

References 4 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…With the rise of Point- of- Care Ultrasound (POCUS) technology, mobile compatible solutions for quality estimation are more present than ever. In this context, Van Woudenberg et al ( 17 ) proposed an Android application to provide the user with real-time feedback of both CVC and image quality. The authors used a single DL network with a DenseNet model and Long Short Term Memory (LSTM) features, trained on a dataset of over 16,000 echocardiogram cines distributed across the 14 cardiac views.…”

Section: Artificial Intelligence Applications For Echocardiography Ac...mentioning

confidence: 99%

“…On the other hand, Luong et al ( 16 ) aimed to automatically assess the quality score of TTEs in hospitalized patients across 3 clinical groups: mechanically ventilated patients and two matched spontaneously breathing controls. For this purpose, the authors used the model previously published in ( 17 ), and used 16,772 2D TTE videos. The overall estimated maximum quality score was significantly poorer for mechanically ventilated TTEs (0.55) compared with either control group (Control 1: 0.64, Control 2: 0.61).…”

Section: Artificial Intelligence Applications For Echocardiography Ac...mentioning

confidence: 99%

See 1 more Smart Citation

Assisted probe guidance in cardiac ultrasound: A review

Ferraz

Coimbra²,

Pedrosa³

2023

Front. Cardiovasc. Med.

View full text Add to dashboard Cite

Echocardiography is the most frequently used imaging modality in cardiology. However, its acquisition is affected by inter-observer variability and largely dependent on the operator’s experience. In this context, artificial intelligence techniques could reduce these variabilities and provide a user independent system. In recent years, machine learning (ML) algorithms have been used in echocardiography to automate echocardiographic acquisition. This review focuses on the state-of-the-art studies that use ML to automate tasks regarding the acquisition of echocardiograms, including quality assessment (QA), recognition of cardiac views and assisted probe guidance during the scanning process. The results indicate that performance of automated acquisition was overall good, but most studies lack variability in their datasets. From our comprehensive review, we believe automated acquisition has the potential not only to improve accuracy of diagnosis, but also help novice operators build expertise and facilitate point of care healthcare in medically underserved areas.

show abstract

Section: Artificial Intelligence Applications For Echocardiography Ac...mentioning

confidence: 99%

Section: Artificial Intelligence Applications For Echocardiography Ac...mentioning

confidence: 99%

Assisted probe guidance in cardiac ultrasound: A review

Ferraz

Coimbra²,

Pedrosa³

2023

Front. Cardiovasc. Med.

View full text Add to dashboard Cite

show abstract

“…Of the 488 accepted studies, AI analysis was performed with biplane method in 300 (61.5%) and single plane apical 4…”

Section: Feasibility Of Ai Analysismentioning

confidence: 99%

“…The use of artificial intelligence (AI) as a method for automating medical image analysis has the potential to transform patient care (1). In echocardiography, AI applications have demonstrated significant value at numerous stages of the analysis pipeline, including automatic view classification (2)(3)(4), quantitative assessment of image quality (5,6), automated contouring (7)(8)(9), assessment of regional wall motion (10), and disease classification (11)(12)(13). Notwithstanding the advantages of automated, high-throughput analysis, the benefits of AI driven analysis include savings of time (9,14), improved prognostication (11,15), reduced variability (16), and greater precision (6,13).…”

Section: Introductionmentioning

confidence: 99%

Automated analysis of limited echocardiograms: Feasibility and relationship to outcomes in COVID-19

Pellikka

Strom²,

Pajares-Hurtado³

et al. 2022

Front. Cardiovasc. Med.

View full text Add to dashboard Cite

BackgroundAs automated echocardiographic analysis is increasingly utilized, continued evaluation within hospital settings is important to further understand its potential value. The importance of cardiac involvement in patients hospitalized with COVID-19 provides an opportunity to evaluate the feasibility and clinical relevance of automated analysis applied to limited echocardiograms.MethodsIn this multisite US cohort, the feasibility of automated AI analysis was evaluated on 558 limited echocardiograms in patients hospitalized with COVID-19. Reliability of automated assessment of left ventricular (LV) volumes, ejection fraction (EF), and LV longitudinal strain (LS) was assessed against clinically obtained measures and echocardiographic findings. Automated measures were evaluated against patient outcomes using ROC analysis, survival modeling, and logistic regression for the outcomes of 30-day mortality and in-hospital sequelae.ResultsFeasibility of automated analysis for both LVEF and LS was 87.5% (488/558 patients). AI analysis was performed with biplane method in 300 (61.5%) and single plane apical 4- or 2-chamber analysis in 136 (27.9%) and 52 (10.7%) studies, respectively. Clinical LVEF was assessed using visual estimation in 192 (39.3%), biplane in 163 (33.4%), and single plane or linear methods in 104 (21.2%) of the 488 studies; 29 (5.9%) studies did not have clinically reported LVEF. LV LS was clinically reported in 80 (16.4%). Consistency between automated and clinical values demonstrated Pearson's R, root mean square error (RMSE) and intraclass correlation coefficient (ICC) of 0.61, 11.3% and 0.72, respectively, for LVEF; 0.73, 3.9% and 0.74, respectively for LS; 0.76, 24.4ml and 0.87, respectively, for end-diastolic volume; and 0.82, 12.8 ml, and 0.91, respectively, for end-systolic volume. Abnormal automated measures of LVEF and LS were associated with LV wall motion abnormalities, left atrial enlargement, and right ventricular dysfunction. Automated analysis was associated with outcomes, including survival.ConclusionAutomated analysis was highly feasible on limited echocardiograms using abbreviated protocols, consistent with equivalent clinically obtained metrics, and associated with echocardiographic abnormalities and patient outcomes.

show abstract

“…Two real-time point of care applications have been published in the relevant literature, one uses a binary classification network to divide images into a "high" or "low" quality group [20] while the other provides real-time operator feedback on view classification (14 views) as well as an indication of quality using an application on an Android mobile phone [172]. Both of these approaches utilise the point-based human assessment of image quality as previously described.…”

Section: Clinical Deployabilitymentioning

confidence: 99%

A computer vision pipeline for fully automated echocardiogram interpretation

Lane¹

View full text Add to dashboard Cite

Cardiovascular disease is the leading cause of global mortality and continues to place a significant burden, in economic and resource terms, upon health services. A 2-dimensional transthoracic echocardiogram captures high spatial and temporal images and videos of the heart and is the modality of choice for the rapid assessment of heart function and structure due to it’s non-invasive nature and lack of ionising radiation. The challenging process of analysing echocardiographic images is currently manually performed by trained experts, though this process is vulnerable to intra- and inter-observer variability and is highly time-consuming. Additionally, echocardiographic images suffer from varying degrees of noise and vary drastically in terms of image quality. Exponential advancements in the fields of artificial intelligence, deep learning and computer vision have enabled the rapid development of automated systems capable of high-precision tasks, often out-performing human experts. This thesis aims to investigate the applicability of applying deep learning methods to automate key processes in the modern echocardiographic laboratory. Namely, view classification, quality assessment, cardiac phase detection, segmentation of the left ventricle and keypoint detection on tissue Doppler imaging strips. State-of-the-art deep learning architectures were applied to each task, and evaluated against ground-truth annotations provided by trained experts. The datasets used throughout each Chapter are diverse and, in some cases, have been made public for the benefit of the research community. To encourage transparency and openness, all code and model weights have been published. Should automated deep learning systems, both online (in terms of providing real-time feedback) and offline (behind the scenes), become integrated within clinical practice, there is great potential for improved accuracy and efficiency, thus improving patient outcomes. Furthermore, health services could save valuable resources such as time and money.

show abstract

Quantitative Echocardiography: Real-Time Quality Estimation and View Classification Implemented on a Mobile Android Device

Cited by 12 publications

References 4 publications

Assisted probe guidance in cardiac ultrasound: A review

Assisted probe guidance in cardiac ultrasound: A review

Automated analysis of limited echocardiograms: Feasibility and relationship to outcomes in COVID-19

A computer vision pipeline for fully automated echocardiogram interpretation

Contact Info

Product

Resources

About