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The authors use blood smears from individuals with sickle cell disease to correlate sickle cell frequency with the occurrence of vaso-occlusive crises.

The authors looked at how soil temperature changes with fire to develop a sensor system that could aid in earlier detection of fires.

The authors looked at the ability to detect heart disease before the onset of severe clinical symptoms.

The authors looked at how students performed on standardized tests when they were taught material via memorization vs. conceptual based approaches.

The authors looked at ways to provide better forecasting on website traffic. They found that deep learning models performed better than statistical models.

Robot-assisted minimally invasive surgery (RMIS) benefits from increased precision and faster recovery, with force feedback from the surgical tool being critical for control. Researchers tested the use of neural networks for detecting the vanishing point of the tool, a key element for force feedback.

Here the authors sought to investigate whether and how cerebral stroke and other health-related variables are influenced together and amongst each other by using statistical analyses. Their analysis suggested relations between nearly all variables considered, with the strongest association between having heart disease and a cerebral stroke.

People with Type One diabetes often rely on Continuous Blood Glucose Monitors (CGMs) to track their blood glucose and manage their condition. Researchers are now working to help people with Type One diabetes more easily monitor their health by developing models that will future blood glucose levels based on CGM readings. Jalla and Ghanta tackle this issue by exploring the use of AI models to forecast blood glucose levels with CGM data.

Authors emphasize the challenges of manual tumor segmentation and the potential of deep learning models to enhance accuracy by automatically analyzing MRI scans.

Sadly, around 800,000 people die by suicide worldwide each year. Dong and Pearce analyze health survey data to identify associations between suicidal ideation and relevant variables, such as sleep quality, hopelessness, and anxious behavior.