Rheumatic Heart Disease (RHD) is a complication of rheumatic fever with higher incidence in developing countries. It causes inflammation and fibrosis of heart valves, among others, and can lead to severe heart damage, heart failure and death, it not treated in its early stages.
Process a large set of medical images, along with additional metadata and clinical information, efficiently and securely, to extract features that could be used to assist and even automate diagnosis.
ATMOSPHERE developed an automatic method to assist its detection, with the aim of achieving an early diagnosis and improve patients' quality of life.
Retrospective data from PROVAR (Programa de Rastreamento da VAlvopatia Reumatica) initiative in Brazil were used. This database was composed by 5600 echocardiography studies (5330 labeled as normal; 238 as borderline RHD; 32 as definite RHD). To compensate for that imbalance, the same number of pathological cases (borderline and definite) and normal cases were selected. Clinical endpoints were extracted from Doppler sequences in a fully-automatic manner by: frame splitting; differentiation in Doppler and anatomical frames by color inspection; color-based segmentation through k-means clustering; preprocessing and view classification using a Convolutional Neural Network; first- and second-order texture analysis and blood-flow velocity calculation; z-score features normalization; and features classification. Deep learning models were computed considering two classes for classification: healthy and pathological.
Furthermore, all sensitive data is processed using SGX containers, which protects them from memory reading-based attacks. It also includes an anonymization module responsible for removing potentially identifying information from medical images. Once critically sensitive information has been securely removed, images can leave the infrastructure deployed in Brazil and be stored on the European volumes. The GPU resources available in Europe are leveraged to perform the data processing. This infrastructure was used to set up a secure virtual environment to create an automatic classifier of echo-cardio images for the screening of Rheumatic Heart Disease (RHD). The application employed high-end computing resources and
ensured that sensitive data were only accessible within the region of origin.
This methodology has shown potential to help physicians in the diagnosis of RHD by performing an initial screening of cases, thus reducing the workload and improving the time-efficiency of health personnel while enhancing patient outcomes.