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

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·log10(SigFea/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

Automated detection of chronic kidney disease using image fusion and graph embedding techniques with ultrasound images

Chronic Kidney disease (CKD) is a progressive disease affecting more than twenty million individuals in the United States. Disease progression is often characterized by complications such as cardiovascular diseases, anemia, hyperlipidemia and metabolic bone diseases etc., Based on estimated GFR values, the disease is categorized in 5 stages which significantly influence patient outcome. Cardiovascular ultrasound (US) (echocardiography) imagery demonstrate significant hemodynamic alterations that are secondary to CKD in the form of volume/ pressure overload. As the CKD pathology directly impacts cardiovascular disease, the US imaging shows structural and hemodynamic adaptation. Hence, the development of a computer-aided diagnosis (CAD) model to predict CKD would be desirable, and can potentially improve treatment. Several prior studies have utilized kidney features for quantitative analysis. In this paper, acquisition of the four-chamber heart US image is employed to predict CKD stage. The method combines image and feature fusion techniques under a graph embedding framework to characterize heart chamber properties. Moreover, a support vector machine is incorporated to classify heart US images. The proposed method achieved 100 % accuracy for a two-class system, and 99.09 % accuracy for a multi-class categorization scenario. Hence, our proposed CAD tool is deployable in both clinic and hospital settings for computer-aided screening of CKD