Magnetic resonance imaging

Artificial neural networks

Artificial Neural Network Model Shows Promising Potential for Advanced Glioma Grading Assessment

Researchers from Central South University have developed an artificial neural network (ANN) model using magnetic resonance imaging (MRI) radiomics data to accurately classify gliomas into four pathological grades. The model demonstrates a high degree of diagnostic accuracy, with overall ratings of 91.28% and 87.04% for the training and

Artificial neural networks

Deep-Learning-Based Computer-Aided Grading of Cervical Spinal Stenosis Yields Strong Performance and Clinical Value

Research from the Fifth Affiliated Hospital of Sun Yat-sen University in Zhuhai, People's Republic of China, has made significant strides in the non-invasive grading of cervical spinal stenosis using deep learning convolutional neural network algorithms. The study, which utilized a dataset of 954 patients with cervical spine magnetic