Fetal Alcohol Spectrum Disorder (FASD), a wide range of physical and neurobehavioral abnormalities associated with prenatal alcohol exposure (PAE), is recognized as a significant public health concern. Advancements in the diagnosis of FASD have been ...
Journal of clinical neuroscience : official journal of the Neurosurgical Society of Australasia
Mar 11, 2021
Multiple sclerosis and neuromyelitis optica spectrum disorders are both neuroinflammatory diseases and have overlapping clinical manifestations. We developed a convolutional neural network model that differentiates between the two based on magnetic r...
We developed machine learning (ML) algorithms to predict abnormal tau accumulation among patients with prodromal AD. We recruited 64 patients with prodromal AD using the Alzheimer's Disease Neuroimaging Initiative (ADNI) dataset. Supervised ML approa...
OBJECTIVES: To build and validate random forest (RF) models for the classification of bone tumors based on the conventional radiographic features of the lesion and patients' clinical characteristics, and identify the most essential features for the c...
To investigate validity evidence for a simulator-based test in robot-assisted radical prostatectomy (RARP). The test consisted of three modules on the RobotiX Mentor VR-simulator: and . Validity evidence was investigated by using Messick's framewo...
PURPOSE: To develop convolutional neural network (CNN) models for differentiating intrahepatic cholangiocarcinoma (ICC) from hepatocellular carcinoma (HCC) and predicting histopathological grade of HCC.
The international journal of medical robotics + computer assisted surgery : MRCAS
Mar 10, 2021
BACKGROUND: Image-guided brachytherapy (BT) robots can be used to assist urologists during seed implantation, thereby improving therapeutic effects. However, safety issues must be considered in the design of such robots, including their structure, me...
BACKGROUND: Early diagnosis of severe acute pancreatitis (SAP) is essential to minimize its mortality and improve prognosis. We aimed to develop an accurate and applicable machine learning predictive model based on routine clinical testing results fo...
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