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The authors develop and test a machine learning algorithm for predicting diabetes diagnoses.

A bottleneck in deleting algal blooms is that current data section is manual and is reactionary to an existing algal bloom. These authors made a custom-designed Seek and Destroy Algal Mitigation System (SDAMS) that detects harmful algal blooms at earlier time points with astonishing accuracy, and can instantaneously suppress the pre-bloom algal population.

The cosmic microwave background (CMB) is faint electromagnetic radiation left over from early stages in the formation of the universe. In order to analyze the CMB, scientists need to remove from electromagnetic data foreground radiation that contaminates CMB datasets. In this study, students utilize extensive updated datasets to analyze the correlation between CMB maps and Faraday RM and WMAP sky maps.

Arrhythmias vary in type and treatment, and ECGs are used to detect them, though human interpretation can be inconsistent. The researchers tested four tree-based algorithms (gradient boosting, random forest, decision tree, and extra trees) on ECG data from over 10,000 patients.

Here, recognizing that brain aneurysms pose a severe threat, often misdiagnosed and leading to high mortality, particularly in younger individuals, the authors explored a novel computer-aided engineering approach. They used magnetic resonance angiography images and computational fluid dynamics, to improve aneurysm detection and risk assessment, aiming for more personalized treatment.

The authors created a 1D model to diagnose hepatomegaly and cirrhosis via ultrasound of the liver.

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

Obsessive-compulsive disorder (OCD) can cause significant impairment, and studies indicate that delays in diagnosis and treatment lead to worse outcomes. This study aimed to assess whether these delays have improved over the past three decades and to identify their causes.

Here seeking to develop a method to diagnose, hypertrophic cardiomyopathy which can cause sudden cardiac death, the authors investigated the use of a convolutional neural network (CNN) and long short-term memory (LSTM) models to classify cardiac magnetic resonance and heart electrocardiogram scans. They found that the CNN model had a higher accuracy and precision and better other qualities, suggesting that machine learning models could be valuable tools to assist physicians in the diagnosis of hypertrophic cardiomyopathy.

Here the authors investigated the use of an affordable gas sensor to detect volatile organic compound (VOC) emissions as an early indicator of tree disease, finding statistically significant differences in VOCs between diseased and non-diseased ash, beech, and maple trees. They suggest this sensor has potential for widespread early disease detection, but call for further research with larger sample sizes and diverse locations.