Automated Echocardiographic Image Interpretation Using Artificial Intelligence

In addition to remaining as one of the leading causes of global mortality, cardio vascular disease has a significant impact on overall health, well-being, and life expectancy. Therefore, early detection of anomalies in cardiac function has become essential for early treatment, and therefore reduction in mortalities. Echocardiography is the most commonly used modality for evaluating the structure and function of the heart. Analysis of echocardiographic images has an important role in the clinical practice in assessing the cardiac morphology and function and thereby reaching a diagnosis. The process of interpretation of echocardiographic images is considered challenging for several reasons. The manual annotation is still a daily work in the clinical routine due to the lack of reliable automatic interpretation methods. This can lead to time-consuming tasks that are prone to intra- and inter-observer variability. Echocardiographic images inherently suffer from a high level of noise and poor qualities. Therefore, although several studies have attempted automating the process, this re-mains a challenging task, and improving the accuracy of automatic echocardiography interpretation is an ongoing field. Advances in Artificial Intelligence and Deep Learning can help to construct an auto-mated, scalable pipeline for echocardiographic image interpretation steps, includingview classification, phase-detection, image segmentation with a focus on border detection, quantification of structure, and measurement of the clinical markers. This thesis aims to develop optimised automated methods for the three individual steps forming part of an echocardiographic exam, namely view classification, left ventricle segmentation, quantification, and measurement of left ventricle structure. Various Neural Architecture Search methods were employed to design efficient neural network architectures for the above tasks. Finally, an optimisation-based speckle tracking echocardiography algorithm was proposed to estimate the myocardial tissue velocities and cardiac deformation. The algorithm was adopted to measure cardiac strain which is used for detecting myocardial ischaemia. All proposed techniques were compared with the existing state-of-the-art methods. To this end, publicly available patients datasets, as well as two private datasets provided by the clinical partners to this project, were used for developments and comprehensive performance evaluations of the proposed techniques. Results demonstrated the feasibility of using automated tools for reliable echocardiographic image interpretations, which can be used as assistive tools to clinicians in obtaining clinical measurements

MediViSTA-SAM: Zero-shot Medical Video Analysis with Spatio-temporal SAM Adaptation

In recent years, the Segmentation Anything Model (SAM) has attracted considerable attention as a foundational model well-known for its robust generalization capabilities across various downstream tasks. However, SAM does not exhibit satisfactory performance in the realm of medical image analysis. In this study, we introduce the first study on adapting SAM on video segmentation, called MediViSTA-SAM, a novel approach designed for medical video segmentation. Given video data, MediViSTA, spatio-temporal adapter captures long and short range temporal attention with cross-frame attention mechanism effectively constraining it to consider the immediately preceding video frame as a reference, while also considering spatial information effectively. Additionally, it incorporates multi-scale fusion by employing a U-shaped encoder and a modified mask decoder to handle objects of varying sizes. To evaluate our approach, extensive experiments were conducted using state-of-the-art (SOTA) methods, assessing its generalization abilities on multi-vendor in-house echocardiography datasets. The results highlight the accuracy and effectiveness of our network in medical video segmentation

MDA-Unet: A Multi-Scale Dilated Attention U-Net For Medical Image Segmentation

The advanced development of deep learning methods has recently made significant improvements in medical image segmentation. Encoder–decoder networks, such as U-Net, have addressed some of the challenges in medical image segmentation with an outstanding performance, which has promoted them to be the most dominating deep learning architecture in this domain. Despite their outstanding performance, we argue that they still lack some aspects. First, there is incompatibility in U-Net’s skip connection between the encoder and decoder features due to the semantic gap between low-processed encoder features and highly processed decoder features, which adversely affects the final prediction. Second, it lacks capturing multi-scale context information and ignores the contribution of all semantic information through the segmentation process. Therefore, we propose a model named MDA-Unet, a novel multi-scale deep learning segmentation model. MDA-Unet improves upon U-Net and enhances its performance in segmenting medical images with variability in the shape and size of the region of interest. The model is integrated with a multi-scale spatial attention module, where spatial attention maps are derived from a hybrid hierarchical dilated convolution module that captures multi-scale context information. To ease the training process and reduce the gradient vanishing problem, residual blocks are deployed instead of the basic U-net blocks. Through a channel attention mechanism, the high-level decoder features are used to guide the low-level encoder features to promote the selection of meaningful context information, thus ensuring effective fusion. We evaluated our model on 2 different datasets: a lung dataset of 2628 axial CT images and an echocardiographic dataset of 2000 images, each with its own challenges. Our model has achieved a significant gain in performance with a slight increase in the number of trainable parameters in comparison with the basic U-Net model, providing a dice score of 98.3% on the lung dataset and 96.7% on the echocardiographic dataset, where the basic U-Net has achieved 94.2% on the lung dataset and 93.9% on the echocardiographic dataset

Automated assessment of echocardiographic image quality using deep convolutional neural networks

Myocardial ischemia tops the list of causes of death around the globe, but its diagnosis and early detection thrives on clinical echocardiography. Although echocardiography presents a huge advantage of a non-intrusive, low-cost point of care diagnosis, its image quality is inherently subjective with strong dependence on operators’ experience level and acquisition skill. In some countries, echo specialists are mandated to supplementary years of training to achieve ‘gold standard’ free-hand acquisition skill without which exacerbates the reliability of echocardiogram and increases possibility for misdiagnosis. These drawbacks pose significant challenges to adopting echocardiography as authoritative modalities for cardiac diagnosis. However, the prevailing and currently adopted solution is to manually carry out quality evaluation where an echocardiography specialist visually inspects several acquired images to make clinical decisions of its perceived quality and prognosis. This is a lengthening process and laced with variability of opinion consequently affection diagnostic responses. The goal of the research is to provide a multi-discipline, state-of-the-art solution that allows objective quality assessment of echocardiogram and to guarantee the reliability of clinical quantification processes. Computer graphic processing unit simulations, medical imaging analysis and deep convolutional neural network models were employed to achieve this goal. From a finite pool of echocardiographic patient datasets, 1650 random samples of echocardiogram cine-loops from different patients with age ranges from 17 and 85 years, who had undergone echocardiography between 2010 and 2020 were evaluated. We defined a set of pathological and anatomical criteria of image quality by which apical-four and parasternal long axis frames can be evaluated with feasibility for real-time optimization. The selected samples were annotated for multivariate model developments and validation of predicted quality score per frame. The outcome presents a robust artificial intelligence algorithm that indicate frames’ quality rating, real-time visualisation of element of quality and updates quality optimization in real-time. A prediction errors of 0.052, 0.062, 0.069, 0.056 for visibility, clarity, depth-gain, and foreshortening attributes were achieved, respectively. The model achieved a combined error rate of 3.6% with average prediction speed of 4.24 ms per frame. The novel method established a superior approach to two-dimensional image quality estimation, assessment, and clinical adequacy on acquisition of echocardiogram prior to quantification and diagnosis of myocardial infarction

Automated Diagnosis of Cardiovascular Diseases from Cardiac Magnetic Resonance Imaging Using Deep Learning Models: A Review

In recent years, cardiovascular diseases (CVDs) have become one of the leading causes of mortality globally. CVDs appear with minor symptoms and progressively get worse. The majority of people experience symptoms such as exhaustion, shortness of breath, ankle swelling, fluid retention, and other symptoms when starting CVD. Coronary artery disease (CAD), arrhythmia, cardiomyopathy, congenital heart defect (CHD), mitral regurgitation, and angina are the most common CVDs. Clinical methods such as blood tests, electrocardiography (ECG) signals, and medical imaging are the most effective methods used for the detection of CVDs. Among the diagnostic methods, cardiac magnetic resonance imaging (CMR) is increasingly used to diagnose, monitor the disease, plan treatment and predict CVDs. Coupled with all the advantages of CMR data, CVDs diagnosis is challenging for physicians due to many slices of data, low contrast, etc. To address these issues, deep learning (DL) techniques have been employed to the diagnosis of CVDs using CMR data, and much research is currently being conducted in this field. This review provides an overview of the studies performed in CVDs detection using CMR images and DL techniques. The introduction section examined CVDs types, diagnostic methods, and the most important medical imaging techniques. In the following, investigations to detect CVDs using CMR images and the most significant DL methods are presented. Another section discussed the challenges in diagnosing CVDs from CMR data. Next, the discussion section discusses the results of this review, and future work in CVDs diagnosis from CMR images and DL techniques are outlined. The most important findings of this study are presented in the conclusion section