Application of Patient-Specific Computational Fluid Dynamics in Coronary and Intra-Cardiac Flow Simulations: Challenges and Opportunities

The emergence of new cardiac diagnostics and therapeutics of the heart has given rise to the challenging field of virtual design and testing of technologies in a patient-specific environment. Given the recent advances in medical imaging, computational power and mathematical algorithms, patient-specific cardiac models can be produced from cardiac images faster, and more efficiently than ever before. The emergence of patient-specific computational fluid dynamics (CFD) has paved the way for the new field of computer-aided diagnostics. This article provides a review of CFD methods, challenges and opportunities in coronary and intra-cardiac flow simulations. It includes a review of market products and clinical trials. Key components of patient-specific CFD are covered briefly which include image segmentation, geometry reconstruction, mesh generation, fluid-structure interaction, and solver techniques

Artificial Intelligence in Assessing Cardiovascular Diseases and Risk Factors via Retinal Fundus Images: A Review of the Last Decade

Background: Cardiovascular diseases (CVDs) continue to be the leading cause of mortality on a global scale. In recent years, the application of artificial intelligence (AI) techniques, particularly deep learning (DL), has gained considerable popularity for evaluating the various aspects of CVDs. Moreover, using fundus images and optical coherence tomography angiography (OCTA) to diagnose retinal diseases has been extensively studied. To better understand heart function and anticipate changes based on microvascular characteristics and function, researchers are currently exploring the integration of AI with non-invasive retinal scanning. Leveraging AI-assisted early detection and prediction of cardiovascular diseases on a large scale holds excellent potential to mitigate cardiovascular events and alleviate the economic burden on healthcare systems. Method: A comprehensive search was conducted across various databases, including PubMed, Medline, Google Scholar, Scopus, Web of Sciences, IEEE Xplore, and ACM Digital Library, using specific keywords related to cardiovascular diseases and artificial intelligence. Results: A total of 87 English-language publications, selected for relevance were included in the study, and additional references were considered. This study presents an overview of the current advancements and challenges in employing retinal imaging and artificial intelligence to identify cardiovascular disorders and provides insights for further exploration in this field. Conclusion: Researchers aim to develop precise disease prognosis patterns as the aging population and global CVD burden increase. AI and deep learning are transforming healthcare, offering the potential for single retinal image-based diagnosis of various CVDs, albeit with the need for accelerated adoption in healthcare systems.Comment: 40 pages, 5 figures, 2 tables, 91 reference

Novel Hypertrophic Cardiomyopathy Diagnosis Index Using Deep Features and Local Directional Pattern Techniques

Hypertrophic cardiomyopathy (HCM) is a genetic disorder that exhibits a wide spectrum of clinical presentations, including sudden death. Early diagnosis and intervention may avert the latter. Left ventricular hypertrophy on heart imaging is an important diagnostic criterion for HCM, and the most common imaging modality is heart ultrasound (US). The US is operator-dependent, and its interpretation is subject to human error and variability. We proposed an automated computer-aided diagnostic tool to discriminate HCM from healthy subjects on US images. We used a local directional pattern and the ResNet-50 pretrained network to classify heart US images acquired from 62 known HCM patients and 101 healthy subjects. Deep features were ranked using Student's t-test, and the most significant feature (SigFea) was identified. An integrated index derived from the simulation was defined as 100.log(10 )(SigFea /root 2) in each subject, and a diagnostic threshold value was empirically calculated as the mean of the minimum and maximum integrated indices among HCM and healthy subjects, respectively. An integrated index above a threshold of 0.5 separated HCM from healthy subjects with 100% accuracy in our test dataset

Quantification of Biventricular Strains in Heart Failure With Preserved Ejection Fraction Patient Using Hyperelastic Warping Method

Heart failure (HF) imposes a major global health care burden on society and suffering on the individual. About 50% of HF patients have preserved ejection fraction (HFpEF). More intricate and comprehensive measurement-focused imaging of multiple strain components may aid in the diagnosis and elucidation of this disease. Here, we describe the development of a semi-automated hyperelastic warping method for rapid comprehensive assessment of biventricular circumferential, longitudinal, and radial strains that is physiological meaningful and reproducible. We recruited and performed cardiac magnetic resonance (CMR) imaging on 30 subjects [10 HFpEF, 10 HF with reduced ejection fraction patients (HFrEF) and 10 healthy controls]. In each subject, a three-dimensional heart model including left ventricle (LV), right ventricle (RV), and septum was reconstructed from CMR images. The hyperelastic warping method was used to reference the segmented model with the target images and biventricular circumferential, longitudinal, and radial strain–time curves were obtained. The peak systolic strains are then measured and analyzed in this study. Intra- and inter-observer reproducibility of the biventricular peak systolic strains was excellent with all ICCs > 0.92. LV peak systolic circumferential, longitudinal, and radial strain, respectively, exhibited a progressive decrease in magnitude from healthy control→HFpEF→HFrEF: control (-15.5 ± 1.90, -15.6 ± 2.06, 41.4 ± 12.2%); HFpEF (-9.37 ± 3.23, -11.3 ± 1.76, 22.8 ± 13.1%); HFrEF (-4.75 ± 2.74, -7.55 ± 1.75, 10.8 ± 4.61%). A similar progressive decrease in magnitude was observed for RV peak systolic circumferential, longitudinal and radial strain: control (-9.91 ± 2.25, -14.5 ± 2.63, 26.8 ± 7.16%); HFpEF (-7.38 ± 3.17, -12.0 ± 2.45, 21.5 ± 10.0%); HFrEF (-5.92 ± 3.13, -8.63 ± 2.79, 15.2 ± 6.33%). Furthermore, septum peak systolic circumferential, longitudinal, and radial strain magnitude decreased gradually from healthy control to HFrEF: control (-7.11 ± 1.81, 16.3 ± 3.23, 18.5 ± 8.64%); HFpEF (-6.11 ± 3.98, -13.4 ± 3.02, 12.5 ± 6.38%); HFrEF (-1.42 ± 1.36, -8.99 ± 2.96, 3.35 ± 2.95%). The ROC analysis indicated LV peak systolic circumferential strain to be the most sensitive marker for differentiating HFpEF from healthy controls. Our results suggest that the hyperelastic warping method with the CMR-derived strains may reveal subtle impairment in HF biventricular mechanics, in particular despite a “normal” ventricular ejection fraction in HFpEF

Normal Values of Myocardial Deformation Assessed by Cardiovascular Magnetic Resonance Feature Tracking in a Healthy Chinese Population: A Multicenter Study

Reference values on atrial and ventricular strain from cardiovascular magnetic resonance (CMR) are essential in identifying patients with impaired atrial and ventricular function. However, reference values have not been established for Chinese subjects. One hundred and fifty healthy volunteers (75 Males/75 Females; 18–82 years) were recruited. All underwent CMR scans with images acceptable for further strain analysis. Subjects were stratified by age: Group 1, 18–44 years; Group 2, 45–59 years; Group 3, ≥60 years. Feature tracking of CMR cine imaging was used to obtain left atrial global longitudinal (LA Ell) and circumferential strains (LA Ecc) and respective systolic strain rates, left ventricular longitudinal (LV Ell), circumferential (LV Ecc) and radial strains (LV Err) and their respective strain rates, and right ventricular longitudinal strain (RV Ell) and strain rate. LA Ell and LA Ecc were 32.8 ± 9.2% and 40.3 ± 13.4%, respectively, and RV Ell was −29.3 ± 6.0%. LV Ell, LV Ecc and LV Err were −22.4 ± 2.9%, −24.3 ± 3.1%, and 79.0 ± 19.4%, respectively. LV Ell and LV Ecc were higher in females than males (P < 0.05). LA Ell, LA Ecc, and LV Ecc decreased, while LV Err increased with age (P < 0.05). LV Ell and RV Ell were not shown to be associated with age. Normal ranges for atrial and ventricular strain and strain rates are provided using CMR feature tracking in Chinese subjects

Comparison of Image Acquisition Techniques in Four-Dimensional Flow Cardiovascular MR on 3 Tesla in Volunteers and Tetralogy of Fallot Patients

Four-dimensional phase-contrast (PC) velocity-encoded flow magnetic resonance imaging (4D flow MRI) is a potentially valuable tool for studying cardiovascular hemodynamics for disease monitoring and/or treatment planning. In this study we compared the performance of two 4D flow MRI pulse sequences - echo-planar imaging (EPI) and segmented gradient-echo (turbo-field-echo or TFE on vendor's platform) - on a clinical 3T system in 6 human subjects including 3 patients with Tetralogy of Fallot (TOF). For aortic flow rate, the coefficients of variation (COV) between 2D and 4D EPI were 7.0% and 7.7% for controls and patients respectively. The corresponding COV between 2D and 4D TFE were 19.0% and 18.3% for controls and patients respectively. The COV between 4D TFE and 4D EPI were larger than 18.7% in kinetic energy analysis. 4D EPI demonstrated acceptable accuracy of intra-cardiac flow quantification, which was also shown in the ex-vivo phantom measurements

Role of Four-Chamber Heart Ultrasound Images in Automatic Assessment of Fetal Heart: A Systematic Understanding

The fetal echocardiogram is useful for monitoring and diagnosing cardiovascular diseases in the fetus in utero. Importantly, it can be used for assessing prenatal congenital heart disease, for which timely intervention can improve the unborn child's outcomes. In this regard, artificial intelligence (AI) can be used for the automatic analysis of fetal heart ultrasound images. This study reviews nondeep and deep learning approaches for assessing the fetal heart using standard four-chamber ultrasound images. The state-of-the-art techniques in the field are described and discussed. The compendium demonstrates the capability of automatic assessment of the fetal heart using AI technology. This work can serve as a resource for research in the field

Cardiac magnetic resonance T1 and extracellular volume mapping with motion correction and co-registration based on fast elastic image registration

10.1007/s10334-017-0668-2Magnetic Resonance Materials in Physics, Biology and Medicine311115-12

Comprehensive electrocardiographic diagnosis based on deep learning

Cardiovascular disease (CVD) is the leading cause of death worldwide, and coronary artery disease (CAD) is a major contributor. Early-stage CAD can progress if undiagnosed and left untreated, leading to myocardial infarction (MI) that may induce irreversible heart muscle damage, resulting in heart chamber remodeling and eventual congestive heart failure (CHF). Electrocardiography (ECG) signals can be useful to detect established MI, and may also be helpful for early diagnosis of CAD. For the latter especially, the ECG perturbations can be subtle and potentially misclassified on manual interpretation and/or when analyzed by traditional algorithms found in ECG instrumentation. For automated diagnostic systems (ADS), deep learning techniques are favored over conventional machine learning techniques, due to the automatic feature extraction and selection processes involved. This paper highlights various deep learning algorithms exploited for the classification of ECG signals into CAD, MI, and CHF conditions. The Convolutional Neural Network (CNN), followed by combined CNN and Long Short-Term Memory (LSTM) models, appear to be the most useful architectures for classification. A 16-layer LSTM model was developed in our study and validated using 10-fold cross validation. A classification accuracy of 98.5% was achieved. Our proposed model has the potential to be a useful diagnostic tool in hospitals for the classification of abnormal ECG signals