Automated Left Ventricular Dimension Assessment Using Artificial Intelligence Developed and Validated by a UK-Wide Collaborative

Howard, James P.; Stowell, Catherine C.; Cole, Graham D.; Ananthan, Kajaluxy; Demetrescu, Camelia; Pearce, Keith; Rajani, Ronak; Sehmi, Jobanpreet; Vimalesvaran, Kavitha; Kanaganayagam, Gajen; McPhail, E. Fred; Ghosh, Ashoke Kumar; Chambers, John; Singh, Amar; Zolgharni, Massoud; Rana, Bushra S.; Francis, Dárrel P.; Shun-Shin, Matthew

doi:10.1161/circimaging.120.011951

Cited by 20 publications

(26 citation statements)

References 18 publications

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“… 32 , 33 With fine-tuning on site-specific data, our model compares favorably with prior state-of-the-art approaches to assessing ventricular wall thickness and hypertrophy on open benchmarks. 16 Expanding on previous work, 21 we collaborated with stakeholders across Stanford Medicine to release our data set of 12 000 deidentified PLAX echocardiogram videos as a resource for the medical machine learning community for future comparison and validation of deep learning models. This expands the prior data set of 10 030 apical 4-chamber videos 21 to a total of 22 030 echocardiogram videos made publicly available, which to our knowledge, is the largest data set release of labeled medical videos with matched clinician annotations.…”

Section: Discussionmentioning

confidence: 99%

“…Nevertheless, quantification of ventricular thickness remains subject to significant intra-and inter-provider variability across imaging modalities 8,9 . Even with the high image resolution and signal-to-noise ratio of cardiac magnetic resonance imaging, there is significant test-retest variability due to the laborious, manual nature of wall thickness measurement 10,11 .…”

Section: Introductionmentioning

confidence: 99%

“…13,14 Even with the high image resolution and signal-to-noise ratio of cardiac magnetic resonance imaging, there is marked test-retest variability owing to the laborious, manual nature of wall thickness measurement. 15,16 Although abundant, 17,18 low cost, and without ionizing radiation, echocardiography relies on expert interpretation, and its accuracy is dependent on careful application of measurement techinques. 19,20 Recent work [21][22][23][24] has shown that deep learning applied to medical imaging can identify clinical phenotypes beyond conventional image interpretation and with higher accuracy than interpretation by human experts.…”

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High-Throughput Precision Phenotyping of Left Ventricular Hypertrophy With Cardiovascular Deep Learning

et al. 2022

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IMPORTANCEEarly detection and characterization of increased left ventricular (LV) wall thickness can markedly impact patient care but is limited by under-recognition of hypertrophy, measurement error and variability, and difficulty differentiating causes of increased wall thickness, such as hypertrophy, cardiomyopathy, and cardiac amyloidosis.OBJECTIVE To assess the accuracy of a deep learning workflow in quantifying ventricular hypertrophy and predicting the cause of increased LV wall thickness. DESIGN, SETTINGS, AND PARTICIPANTSThis cohort study included physician-curated cohorts from the Stanford Amyloid Center and Cedars-Sinai Medical Center (CSMC) Advanced Heart Disease Clinic for cardiac amyloidosis and the Stanford Center for Inherited Cardiovascular Disease and the CSMC Hypertrophic Cardiomyopathy Clinic for hypertrophic cardiomyopathy from January 1, 2008, to December 31, 2020. The deep learning algorithm was trained and tested on retrospectively obtained independent echocardiogram videos from Stanford Healthcare, CSMC, and the Unity Imaging Collaborative. MAIN OUTCOMES AND MEASURES The main outcome was the accuracy of the deep learning algorithm in measuring left ventricular dimensions and identifying patients with increased LV wall thickness diagnosed with hypertrophic cardiomyopathy and cardiac amyloidosis. RESULTS The study included 23 745 patients: 12 001 from Stanford Health Care (6509 [54.2%] female; mean [SD] age, 61.6 [17.4] years) and 1309 from CSMC (808 [61.7%] female; mean [SD] age, 62.8 [17.2] years) with parasternal long-axis videos and 8084 from Stanford Health Care (4201 [54.0%] female; mean [SD] age, 69.1 [16.8] years) and 2351 from CSMS (6509 [54.2%] female; mean [SD] age, 69.6 [14.7] years) with apical 4-chamber videos. The deep learning algorithm accurately measured intraventricular wall thickness (mean absolute error [MAE], 1.2 mm; 95% CI, 1.1-1.3 mm), LV diameter (MAE, 2.4 mm; 95% CI, 2.2-2.6 mm),and posterior wall thickness (MAE, 1.4 mm; 95% CI, 1.2-1.5 mm) and classified cardiac amyloidosis (area under the curve [AUC], 0.83) and hypertrophic cardiomyopathy (AUC, 0.98) separately from other causes of LV hypertrophy. In external data sets from independent domestic and international health care systems, the deep learning algorithm accurately quantified ventricular parameters (domestic: R 2 , 0.96; international: R 2 , 0.90). For the domestic data set, the MAE was 1.7 mm (95% CI, 1.6-1.8 mm) for intraventricular septum thickness, 3.8 mm (95% CI, 3.5-4.0 mm) for LV internal dimension, and 1.8 mm (95% CI, 1.7-2.0 mm) for LV posterior wall thickness. For the international data set, the MAE was 1.7 mm (95% CI, 1.5-2.0 mm) for intraventricular septum thickness, 2.9 mm (95% CI, 2.4-3.3 mm) for LV internal dimension, and 2.3 mm (95% CI, 1.9-2.7 mm) for LV posterior wall thickness. The deep learning algorithm accurately detected cardiac amyloidosis (AUC, 0.79) and hypertrophic cardiomyopathy (AUC, 0.89) in the domestic external validation site. CONCLUSIONS AND RELEVANCEIn this cohort study, t...

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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confidence: 99%

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High-Throughput Precision Phenotyping of Left Ventricular Hypertrophy With Cardiovascular Deep Learning

et al. 2022

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“…Moreover, with an expanding data set we will be able to train quality control CNNs to recognize specific types of quality and planning errors. In order to decrease subjectivity of this task, a collaborative initiative to build a consensus across a vast number of operators, similar to a recent one developed in the field of echo and AI ( 25 ), would be of great value. Lastly, our method now requires post-processing and is separated from the CMR scanner.…”

Section: Discussionmentioning

confidence: 99%

Deep Learning for Classification and Selection of Cine CMR Images to Achieve Fully Automated Quality-Controlled CMR Analysis From Scanner to Report

Vergani

Razavi

Puyol‐Antón

et al. 2021

Front. Cardiovasc. Med.

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Introduction: Deep learning demonstrates great promise for automated analysis of CMR. However, existing limitations, such as insufficient quality control and selection of target acquisitions from the full CMR exam, are holding back the introduction of deep learning tools in the clinical environment. This study aimed to develop a framework for automated detection and quality-controlled selection of standard cine sequences images from clinical CMR exams, prior to analysis of cardiac function.Materials and Methods: Retrospective study of 3,827 subjects that underwent CMR imaging. We used a total of 119,285 CMR acquisitions, acquired with scanners of different magnetic field strengths and from different vendors (1.5T Siemens and 1.5T and 3.0T Phillips). We developed a framework to select one good acquisition for each conventional cine class. The framework consisted of a first pre-processing step to exclude still acquisitions; two sequential convolutional neural networks (CNN), the first (CNNclass) to classify acquisitions in standard cine views (2/3/4-chamber and short axis), the second (CNNQC) to classify acquisitions according to image quality and orientation; a final algorithm to select one good acquisition of each class. For each CNN component, 7 state-of-the-art architectures were trained for 200 epochs, with cross entropy loss and data augmentation. Data were divided into 80% for training, 10% for validation, and 10% for testing.Results: CNNclass selected cine CMR acquisitions with accuracy ranging from 0.989 to 0.998. Accuracy of CNNQC reached 0.861 for 2-chamber, 0.806 for 3-chamber, and 0.859 for 4-chamber. The complete framework was presented with 379 new full CMR studies, not used for CNN training/validation/testing, and selected one good 2-, 3-, and 4-chamber acquisition from each study with sensitivity to detect erroneous cases of 89.7, 93.2, and 93.9%, respectively.Conclusions: We developed an accurate quality-controlled framework for automated selection of cine acquisitions prior to image analysis. This framework is robust and generalizable as it was developed on multivendor data and could be used at the beginning of a pipeline for automated cine CMR analysis to obtain full automatization from scanner to report.

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“…Deep learning (DL) allows automated contour detection offering the promise of faster and potentially more accurate and reproducible evaluation of LV volumes and EF by echocardiography (3,7,13,14). In this work, we developed a new DL algorithm for manual contouring based on a U-Net convolutional network architecture, using an anonymised database of echocardiographic images.…”

Section: Introductionmentioning

confidence: 99%

Deep Learning vs manual techniques for assessing left ventricular ejection fraction in 2D echocardiography: validation against CMR

Saloux

Popoff

Langet

et al. 2022

Preprint

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Objective: To evaluate accuracy and reproducibility of 2D echocardiography (2DE) left ventricular (LV) volumes and ejection fraction (LVEF) estimates by Deep Learning (DL) vs. manual contouring and against CMR. Background: 2DE LV manual segmentation for LV volumes and LVEF calculation is time consuming and operator dependent. Methods: A DL-based convolutional network (DL1) was trained on 2DE data from centre A, then evaluated on 171 subjects with a wide range of cardiac conditions (49 healthy) – 31 subjects from centre A (18%) and 140 subjects from centre B (82%) – who underwent 2DE and CMR on the same day. Two senior (A 1 and B 1 ) and one junior (A 2 ) cardiologists manually contoured 2DE end-diastolic (ED) and end-systolic (ES) endocardial borders in the cycle and frames of their choice. Selected frames were automatically segmented by DL1 and two DL algorithms from the literature (DL2 and DL3), applied without adaptation to verify their generalizability to unseen data. Interobserver variability of DL was compared to manual contouring. All ESV, EDV and EF values were compared to CMR as reference. Results: 50% of 2DE images were of good quality. Interobserver agreement was better by DL1 and DL2 than by manual contouring for EF (Lin's concordance = 0.9 and 0.91 vs. 0.84), EDV (0.98 and 0.99 vs. 0.82), and ESV (0.99 and 0.99 vs. 0.89). LVEF bias was similar or reduced using DL1 (-0.1) vs. manual contouring (3.0), and worse for DL2 and DL3. Agreement between 2DE and CMR LVEF was similar or higher for DL1 vs. manual contouring (Cohen's kappa = 0.65 vs. 0.61) and degraded for DL2 and DL3 (0.48 and 0.29). Conclusion: DL contouring yielded accurate EF measurements and generalized well to unseen data, while reducing interobserver variability. This suggests that DL contouring may improve accuracy and reproducibility of 2DE LVEF in routine practice.

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Automated Left Ventricular Dimension Assessment Using Artificial Intelligence Developed and Validated by a UK-Wide Collaborative

Cited by 20 publications

References 18 publications

High-Throughput Precision Phenotyping of Left Ventricular Hypertrophy With Cardiovascular Deep Learning

High-Throughput Precision Phenotyping of Left Ventricular Hypertrophy With Cardiovascular Deep Learning

Deep Learning for Classification and Selection of Cine CMR Images to Achieve Fully Automated Quality-Controlled CMR Analysis From Scanner to Report

Deep Learning vs manual techniques for assessing left ventricular ejection fraction in 2D echocardiography: validation against CMR

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