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Assessing machine learning model efficacy for brain tumor MRI classification: a multi-model approach

Dhingra et al. | Mar 14, 2026

Assessing machine learning model efficacy for brain tumor MRI classification: a multi-model approach
Image credit: Dhingra and Dhingra

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.

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Effects of common supplements on human platelet aggregation in vitro

Prabhakar et al. | Apr 16, 2025

Effects of common supplements on human platelet aggregation in vitro
Image credit: The authors

There is a need for safe and effective therapies to prevent platelet aggregation associated with cardiovascular diseases. Prabhakar and Prabhakar test to see whether dietary supplements claiming to reduce cardiovascular disease risk will affect aggregation of human platelets.

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Using Artificial Intelligence to Forecast Continuous Glucose Monitor(CGM) readings for Type One Diabetes

Jalla et al. | Aug 07, 2024

Using Artificial Intelligence to Forecast Continuous Glucose Monitor(CGM) readings for Type One Diabetes
Image credit: The authors

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.

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