Best practice & research. Clinical haematology
Jun 7, 2020
Artificial intelligence, and more narrowly machine-learning, is beginning to expand humanity's capacity to analyze increasingly large and complex datasets. Advances in computer hardware and software have led to breakthroughs in multiple sectors of ou...
AIM: Some conditions within specific populations are so rare rigorous evidence is unavailable. Childhood hyperkinesis is one example, yet presents an opportunity to examine sensation's contribution to motor function.
BACKGROUND: The electrocardiogram (ECG) is one of the most commonly used diagnostic tools in medicine and healthcare. Deep learning methods have achieved promising results on predictive healthcare tasks using ECG signals.
Many neurological diseases and delineating pathological regions have been analyzed, and the anatomical structure of the brain researched with the aid of magnetic resonance imaging (MRI). It is important to identify patients with Alzheimer's disease (...
Over the last years, the amount, variety, and complexity of neuroimaging data acquired in patients with brain tumors for routine clinical purposes and the resulting number of imaging parameters have substantially increased. Consequently, a timely and...
OBJECTIVE: To apply a deep learning object detection technique to CT images for detecting cervical lymph nodes metastasis in patients with oral cancers, and to clarify the detection performance.
Computer methods and programs in biomedicine
Jun 6, 2020
BACKGROUND AND OBJECTIVES: Hypoalbuminemia can be life threatening among critically ill patients. In this study, we develop a patient-specific monitoring and forecasting model based on deep neural networks to predict concentrations of albumin and a s...
Low CFR is associated with poor prognosis, whereas it is a heterogeneous condition according to the actual coronary flow, such as high resting or low hyperemic coronary flow, which should have different physiological traits and clinical implications....
Computerized medical imaging and graphics : the official journal of the Computerized Medical Imaging Society
Jun 6, 2020
It has been shown that deep neural networks are powerful and flexible models that can be applied on fMRI data with superb representation ability over traditional methods. However, a challenge of neural network architecture design has also attracted a...
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