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The authors studied the accuracy of two commonly used explainable AI techniques for detecting pneumonia in chest x-rays.

This study shows the efficacy of leveraging transfer learning, specifically from residual networks, to detect CVDs and possible signs of CVDs. The findings indicate that leveraging transfer learning from residual networks alongside medical professionals is a highly promising approach for CVD detection and diagnosis, warranting further investigation.

This manuscript explores the performance of five different machine learning models in classifying brain tumors from a dataset of MRI scans. The authors find that several of the models showed >90% accuracy. Thus, the authors suggest that machine learning models demonstrate potential for effective implementation in clinical settings, including as a diagnostic tool that can be used to complement the expertise of neuroradiologists.

The authors looked at using computer vision to evaluate the degree of exotropia in individuals with strabismus.

Elevated acetylcholinesterase (AChE) activity contributes to cognitive decline and neurodegenerative diseases such as Alzheimer’s, motivating the search for more effective inhibitors with better bioavailability. This study used computational methods to design novel, non-toxic AChE inhibitors.

This study analyzes genetic alterations and expression patterns of protein kinases involved in phagocytosis across multiple cancers using TCGA data.

Detecting cervical spondylosis using deep learning

The authors looked at how vocabulary is impacted in Alzheimer's disease and whether it could be used a predictor of disease onset.

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

Here the authors investigated the natural recovery of stress-induced dopamine-related gene deficits in C. elegans by measuring the expression of cat-2 (dopamine biosynthesis) and sod-2 (oxidative stress) following exposure to starvation or hydrocortisone. They found that the reversibility of sod-2 and the expression of cat-2 were highly dependent on the type and severity of the stressor, suggesting that the body's natural ability to recover from dopamine dysfunction has biological limitations.