Ensemble integrating three architectures achieved area under the curve of 0.9208, outperforming individual models.

BrainIAC, a breakthrough AI foundation model, is able to predict brain age, dementia, time-to-stroke, and brain cancer from brain magnetic resonance imaging (MRI).

New research has been published ahead-of-print by The Journal of Nuclear Medicine (JNM). JNM is published by the Society of Nuclear Medicine and Molecular Imaging, an international scientific and ...

A new study published in the journal of Scientific Reports proposed a potential diagnostic tool by combining deep learning ...

Deep learning techniques can enhance diagnosis of Meniere disease (MD) and severity grading, according to a study published ...

A new machine-learning-based approach to mapping real-time tumor metabolism in brain cancer patients, developed at the University of Michigan, could help doctors discover which treatment strategies ...

Abstract: Brain tumors are abnormal growths in the brain that affect cognitive functions. Effective treatment planning and better patient outcomes depend on the timely diagnosis and precise ...

Explore the advancements in minimal residual disease (MRD) assays, comparing tumor-informed and tumor-agnostic methods for enhanced cancer detection and treatment strategies. Minimal residual disease ...

Abstract: In the domain of modern healthcare research, decentralized learning based computer-aided diagnosis (CAD) is transformative for health monitoring and disease detection, particularly in ...

This important study describes a deep learning framework that analyzes single-cell RNA data to identify a tumor-agnostic gene signature associated with brain metastases. The identified signature ...