In this study, a deep learning model is used to classify post-traumatic stress disorder patients through novel markers to assist in finding candidate biomarkers for the disorder.

Here, recognizing the difficulty that visually impaired people may have differentiating United States currency, the authors sought to use artificial intelligence (AI) models to identify US currencies. With a one-stage AI they reported a test accuracy of 89%, finding that multi-level deep learning models did not provide any significant advantage over a single-level AI.

The authors use pictures of candy wrappers and neural networks to improve nutritional accuracy of diet-tracking apps.

In this article, the authors investigate the RNA expression differences between groups of chronic cocaine abusers and drug-free subjects.

The authors develop a new method for training machine learning algorithms to differentiate between hate speech and cultural speech in online platforms.

In this study, the authors hypothesized that closed-circuit television images could be stored with improved resolution by using enhanced deep residual (EDSR) networks.

In this article the authors look at the lung microbiome in patients with cystic fibrosis to determine what the major bacterial species present are.

To address whether odor sensory circuits are organized topographically, the authors investigate whether the neuronal responses to similar odors amongst different mice mapped similarly in brain.

To best identify tuberculosis and pneumonia diagnoses in chest x-rays, the authors compare different deep learning convolution neural networks.

In this article, the authors looked at developing a strategy that would allow for earlier diagnosis of Diabetes as that improves long-term outcomes. They were able to find that BMI, tricep skin fold thickness, and blood pressure are the risk factors with the highest accuracy in predicting diabetes risk.