BACKGROUND: This review article provides an overview of artificial intelligence (AI) and machine learning (ML) as it relates to cardiovascular health care.
This study presents a novel idea of rebraiding thin McKibben muscles to overcome their limitation of contraction. The thin McKibben muscles, presented in the authors' previous work, have the flexibility that allows them to be braided. According to th...
A theory and model of spatial coordinate transforms in the dorsal visual system through the parietal cortex that enable an interface via posterior cingulate and related retrosplenial cortex to allocentric spatial representations in the primate hippoc...
Segmentation of lungs with acute respiratory distress syndrome (ARDS) is a challenging task due to diffuse opacification in dependent regions which results in little to no contrast at the lung boundary. For segmentation of severely injured lungs, loc...
In recent months two major fields of interest in pulmonary imaging have stood out: pulmonary fibrosis and pulmonary nodules. New guidelines have been released to define pulmonary fibrosis and subsequent studies have proved the value of these changes....
Application of machine learning techniques has the potential to yield unseen insights into movement and permits visualisation of complex behaviours and tangible profiles. The aim of this study was to identify profiles of relative motor competence (MC...
Different kinds of biological databases publicly available nowadays provide us a goldmine of multidiscipline big data. The Cancer Genome Atlas is a cancer database including detailed information of many patients with cancer. DrugBank is a database in...
Treating patients with major depressive disorder is challenging because it takes several months for antidepressants prescribed for the patients to take effect. This limitation may result in increased risks and treatment costs. To address this limitat...
In in-silico prediction for molecular binding of human genomes, promising results have been demonstrated by deep neural multi-task learning due to its strength in training tasks with imbalanced data and its ability to avoid over-fitting. Although the...
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