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Pulmonary diseases like lung cancer and valley fever pose serious health challenges, making accurate and rapid diagnostics essential. This study developed a MATLAB-based software tool that uses computer vision techniques to differentiate between these diseases by analyzing features of lung nodules in CT scans, achieving higher precision than traditional methods.

The authors looked at different models of semantic segmentation to determine which may be best used in the future for segmentation of CT scans to help diagnose certain conditions.

The authors test various machine learning models to improve the accuracy and efficiency of pneumonia diagnosis from X-ray images.

Lung cancer is highly fatal, largely due to late diagnoses, but early detection can greatly improve survival. This study developed three models to enhance early diagnosis: an MLP for clinical data, a CNN for imaging data, and a hybrid model combining both.

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.

Mammographic screening is a common diagnostic tool for breast cancer among average-risk women. The authors hypothesized that adherence rates for mammographic screening may be lower among minorities (non-Hispanic black (NHB) and Hispanic/Latino) than among non-Hispanic whites (NHW) regardless of the guideline applied. The findings support other studies’ results that different racial/ethnic and socio-demographic factors can affect screening adherence. Therefore, healthcare providers should promote breast cancer screening especially among NHW/Hispanic women and women lacking insurance coverage.

Staphylococcus aureus (S. aureus) has a mortality rate of up to 30% in developing countries. The purpose of this experiment was to determine if enzymatic and volatile compound-based approaches would perform more quickly in comparison to existing S. aureus diagnostic methods and to evaluate these novel methods on accuracy. Ultimately, this device provided results in less than 30 seconds, which is much quicker than existing methods that take anywhere from 10 minutes to 48 hours based on approach. Statistical analysis of accuracy provides preliminary confirmation that the device based on enzymatic and volatile compound-based approaches can be an accurate and time-efficient tool to detect pathogenic S. aureus.

The diagnosis of malaria remains one of the major hurdles to eradicating the disease, especially among poorer populations. Here, the authors use machine learning to improve the accuracy of deep learning algorithms that automate the diagnosis of malaria using images of blood smears from patients, which could make diagnosis easier and faster for many.

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