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

Here, the authors used machine learning to analyze microscopic images of hair, quantifying various features to distinguish individuals, even within families where traditional DNA analysis is limited. The Discriminant Analysis (DA) model achieved the highest accuracy (88.89%) in identifying individuals, demonstrating its potential to improve the reliability of hair evidence in forensic investigations.

Here, seeking to better understand the effects of gadolinium-based contrast agents, dyes typically used for MRI scans, the authors evaluated the activity of catalase found in beef liver both with and without gadodiamide when exposed to hydrogen peroxide. They found that gadioamide did not significantly inhibit catalase's activity, attributing this lack of effects to the chelating agent found in gadodiamide.

The authors compare neuroimaging datasets to identify potential new biomarkers for earlier detection of Alzheimer's disease.

In this study, the authors use aerosol optical depth data to determine if aerosol levels were lower in major metropolitan areas around the world during the COVID-19 pandemic.

This study compares the performance of two deep learning models, U-Net and DeconvNet, for segmenting gliomas from MRI scans.

The authors use machine learning on MRI images of brain tissue to predict tumor onset as an avenue for early detection of brain cancer.

AI analysis of brain scans offers promise for helping doctors diagnose brain tumors. Haider and Drosis explore this field by developing machine learning models that classify brain scans as "cancer" or "non-cancer" diagnoses.

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.

Autism spectrum disorder (ASD) is a complex neurodevelopmental disorder and is difficult to diagnose in young children. Here magnetoencephalography was used to compare the brain activity in patients with ASD to patients in a control group. The results show that patients with ASD have a high level of activity in different areas of the brain than those in the control group.