4 research outputs found
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An Artificial Intelligence application for drone-assisted 5G remote e-Health
Artificial intelligence (AI) algorithms are experiencing growing research interest due to their ability to improve decision making capabilities for promising applications in different economic sectors. The growing shift toward the Internet of Everything environments brought by devices embedded with sensors that can share information brings immense opportunity for new applications (apps). While these new apps thrive in resource-rich areas (i.e., capitals), neighboring cities that lack the resources and infrastructure to support them may be left behind. It is vital that new technologies can reach those who need them the most, especially healthcare-based. This article proposes an app-based approach for long-distance patient monitoring and care. The app would serve as a platform of communication between patients and healthcare staff, where the latter can send standardized video footage or pictures to the former (e.g., their primary care doctor). This feature is enhanced with a recurrent neural network algorithm as a validation tool for healthcare-related videos exchanged between patients and staff. Thus, the healthcare team does not need to check each video for validity, freeing their time for other activities
Deep Learning Technique for Congenital Heart Disease Detection Using Stacking-Based CNN-LSTM Models from Fetal Echocardiogram: A Pilot Study
Congenital heart defects (CHDs) are a leading cause of death in infants under 1 year of age. Prenatal intervention can reduce the risk of postnatal serious CHD patients, but current diagnosis is based on qualitative criteria, which can lead to variability in diagnosis between clinicians. Objectives: To detect morphological and temporal changes in cardiac ultrasound (US) videos of fetuses with hypoplastic left heart syndrome (HLHS) using deep learning models. A small cohort of 9 healthy and 13 HLHS patients were enrolled, and ultrasound videos at three gestational time points were collected. The videos were preprocessed and segmented to cardiac cycle videos, and five different deep learning CNN-LSTM models were trained (MobileNetv2, ResNet18, ResNet50, DenseNet121, and GoogleNet). The top-performing three models were used to develop a novel stacking CNN-LSTM model, which was trained using five-fold cross-validation to classify HLHS and healthy patients. The stacking CNN-LSTM model outperformed other pre-trained CNN-LSTM models with the accuracy, precision, sensitivity, F1 score, and specificity of 90.5%, 92.5%, 92.5%, 92.5%, and 85%, respectively for video-wise classification, and with the accuracy, precision, sensitivity, F1 score, and specificity of 90.5%, 92.5%, 92.5%, 92.5%, and 85%, respectively for subject-wise classification using ultrasound videos. This study demonstrates the potential of using deep learning models to classify CHD prenatal patients using ultrasound videos, which can aid in the objective assessment of the disease in a clinical setting.This study was funded by Qatar National Research Fund (QNRF), National Priorities Research Program (NPRP 10-0123-170222). The open access publication of this article was funded by the Qatar National Library