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

A novel hybrid approach for automated detection of retinal detachment using ultrasound images

Retinal detachment (RD) is an ocular emergency, which needs quick intervention to preclude permanent vision loss. In general, ocular ultrasound is used by ophthalmologists to enhance their judgment in detecting RD in eyes with media opacities which precludes the retinal evaluation. However, the quality of ultrasound (US) images may be degraded due to the presence of noise, and other retinal conditions may cause membranous echoes. All these can influence the accuracy of diagnosis. Hence, to overcome the above, we are proposing an automated system to detect RD using texton, higher order spectral (HOS) cumulants and locality sensitive discriminant analysis (LSDA) techniques. Our developed method is able to classify the posterior vitreous detachment and RD using support vector machine classifier with highest accuracy of 99.13%. Our system is ready to be tested with more diverse ultrasound images and aid ophthalmologists to arrive at a more accurate diagnosis

Calcium identification and scoring based on echocardiography imaging

Currently, an echocardiography expert is needed to identify calcium in the aortic valve, and a cardiac CT-Scan image is needed for calcium quantification. When performing a CT-scan, the patient is subject to radiation, and therefore the number of CT-scans that can be performed should be limited, restricting the patient's monitoring. Computer Vision (CV) has opened new opportunities for improved efficiency when extracting knowledge from an image. Applying CV techniques on echocardiography imaging may reduce the medical workload for identifying the calcium and quantifying it, helping doctors to maintain a better tracking of their patients. In our approach, we developed a simple technique to identify and extract the calcium pixel count from echocardiography imaging, by using CV. Based on anonymized real patient echocardiographic images, this approach enables semi-automatic calcium identification. As the brightness of echocardiography images (with the highest intensity corresponding to calcium) vary depending on the acquisition settings, we performed echocardiographic adaptive image binarization. Given that blood maintains the same intensity on echocardiographic images – being always the darker region – we used blood structures in the image to create an adaptive threshold for binarization. After binarization, the region of interest (ROI) with calcium, was interactively selected by an echocardiography expert and extracted, allowing us to compute a calcium pixel count, corresponding to the spatial amount of calcium. The results obtained from our experiments are encouraging. With our technique, from echocardiographic images collected for the same patient with different acquisition settings and different brightness, we were able to obtain a calcium pixel count, where pixels values show an absolute pixel value margin of error of 3 (on a scale from 0 to 255), that correlated well with human expert assessment of calcium area for the same images.Atualmente, é necessário um perito em ecocardiografia para identificar o cálcio na válvula aórtica, e é necessária uma imagem Tomográfica Computorizada (TAC) cardíaca para a quantificação do cálcio. Ao realizar uma TAC, o paciente é sujeito a radiação, pelo que o número de TACs que podem ser realizadas deve ser limitado, restringindo a monitorização do paciente. A Visão por Computador (VC) abriu novas oportunidades para uma maior eficiência na extração de conhecimentos de uma imagem. A aplicação de técnicas de VC na ecocardiografia pode reduzir a carga de trabalho médico para identificar o cálcio e quantificálo, ajudando os médicos a manter um melhor acompanhamento dos seus pacientes. Na nossa abordagem, desenvolvemos uma técnica simples para identificar e extrair o número de pixéis de cálcio da ecocardiografia, através da utilização de VC. Com base em ecocardiografias anónimas de doentes reais, esta abordagem permite a identificação semiautomática do cálcio. Como o brilho das imagens de ecocardiografia (com a intensidade mais elevada corresponde ao cálcio) varia consoante os parâmetros de aquisição, realizámos a binarização das ecocardiografias de forma adaptativa. Dado que o sangue mantém a mesma intensidade nas ecocardiografias - sendo sempre a região mais escura - utilizámos estruturas sanguíneas na imagem para criar um limiar adaptativo para a binarização. Após a binarização, a região de interesse (ROI) com cálcio, foi selecionada interactivamente por um especialista em ecocardiografia e extraída, permitindo-nos calcular o número de pixéis de cálcio, correspondente à quantidade espacial de cálcio. Os resultados obtidos com as nossas experiências são encorajadores. Com a nossa técnica, a partir de ecocardiografias recolhidas para o mesmo paciente com diferentes configurações de aquisição e diferentes brilhos, conseguimos obter uma contagem de pixéis de cálcio, onde os valores de pixéis mostram uma margem de erro absoluta de 3 (numa escala de 0 a 255), que se correlacionou bem com a avaliação humana perita da área de cálcio para as mesmas imagens

Artificial intelligence and echocardiography

Echocardiography plays a crucial role in the diagnosis and management of cardiovascular disease. However, interpretation remains largely reliant on the subjective expertise of the operator. As a result inter-operator variability and experience can lead to incorrect diagnoses. Artificial intelligence (AI) technologies provide new possibilities for echocardiography to generate accurate, consistent and automated interpretation of echocardiograms, thus potentially reducing the risk of human error. In this review, we discuss a subfield of AI relevant to image interpretation, called machine learning, and its potential to enhance the diagnostic performance of echocardiography. We discuss recent applications of these methods and future directions for AI-assisted interpretation of echocardiograms. The research suggests it is feasible to apply machine learning models to provide rapid, highly accurate and consistent assessment of echocardiograms, comparable to clinicians. These algorithms are capable of accurately quantifying a wide range of features, such as the severity of valvular heart disease or the ischaemic burden in patients with coronary artery disease. However, the applications and their use are still in their infancy within the field of echocardiography. Research to refine methods and validate their use for automation, quantification and diagnosis are in progress. Widespread adoption of robust AI tools in clinical echocardiography practice should follow and have the potential to deliver significant benefits for patient outcome

Local Preserving Class Separation Framework to Identify Gestational Diabetes Mellitus Mother Using Ultrasound Fetal Cardiac Image

In the presence of gestational diabetes mellitus (GDM), the fetus is exposed to a hyperinsulinemia environment. This environment can cause a wide range of metabolic and fetal cardiac structural alterations. Fetal myocardial hypertrophy predominantly affecting the interventricular septum possesses a morphology of disarray similar to hypertrophic cardiomyopathy, and may be present in some GDM neonates after birth. Myocardial thickness may increase in GDM fetuses independent of glycemic control status and fetal weight. Fetal echocardiography performed on fetuses with GDM helps in assessing cardiac structure and function, and to diagnose myocardial hypertrophy. There are few studies in the literature which have established evidence for morphologic variation associated with cardiac hypertrophy among fetuses of GDM mothers. In this study, fetal ultrasound images of normal, pregestational diabetes mellitus (preGDM) and GDM mothers were used to develop a computer aided diagnostic (CAD) tool. We proposed a new method called local preserving class separation (LPCS) framework to preserve the geometrical configuration of normal and preGDM/GDM subjects. The generated shearlet based texture features under LPCS framework showed promising results compared with deep learning algorithms. The proposed method achieved a maximum accuracy of 98.15% using a support vector machine (SVM) classifier. Hence, this paradigm can be helpful to physicians in detecting fetal myocardial hypertrophy in preGDM/GDM mothers

SALMANTICOR study. Rationale and design of a population-based study to identify structural heart disease abnormalities: a spatial and machine learning analysis

[EN]Introduction: This study aims to obtain data on the prevalence and incidence of structural heart disease in a population setting and, to analyse and present those data on the application of spatial and machine learning methods that, although known to geography and statistics, need to become used for healthcare research and for political commitment to obtain resources and support effective public health programme implementation. Methods and analysis: We will perform a cross-sectional survey of randomly selected residents of Salamanca (Spain). 2400 individuals stratified by age and sex and by place of residence (rural and urban) will be studied. The variables to analyse will be obtained from the clinical history, different surveys including social status, Mediterranean diet, functional capacity, ECG, echocardiogram, VASERA and biochemical as well as genetic analysis. Ethics and dissemination: The study has been approved by the ethical committee of the healthcare community. All study participants will sign an informed consent for participation in the study. The results of this study will allow the understanding of the relationship between the different influencing factors and their relative importance weights in the development of structural heart disease