BACKGROUND: Longitudinal measurement of glioma burden with MRI is the basis for treatment response assessment. In this study, we developed a deep learning algorithm that automatically segments abnormal fluid attenuated inversion recovery (FLAIR) hype...
Metabolic syndrome and related disorders
Nov 1, 2019
We investigated the prevalence and the most relevant features of nonalcoholic steatohepatitis (NASH), a stage of nonalcoholic fatty liver disease, (NAFLD) in which the inflammation of hepatocytes can lead to increased cardiovascular risk, liver fibr...
Sheng wu yi xue gong cheng xue za zhi = Journal of biomedical engineering = Shengwu yixue gongchengxue zazhi
Oct 25, 2019
Alzheimer's disease (AD) is a typical neurodegenerative disease, which is clinically manifested as amnesia, loss of language ability and self-care ability, and so on. So far, the cause of the disease has still been unclear and the course of the disea...
From initial human papillomavirus (HPV) infection and precursor stages, the development of cervical cancer takes decades. High-sensitivity HPV DNA testing is currently recommended as primary screening method for cervical cancer, whereas better triage...
Clinical and translational gastroenterology
Oct 1, 2019
INTRODUCTION: Adverse histopathological status (AHS) decreases outcomes of gastric cancer (GC). With the lack of a single factor with great reliability to preoperatively predict AHS, we developed a computational approach by integrating large-scale im...
Automatic and accurate segmentation of intravascular optical coherence tomography imagery is of great importance in computer-aided diagnosis and in treatment of cardiovascular diseases. However, this task has not been well addressed for two reasons. ...
PURPOSE: The prediction of clinical outcomes for patients with cancer is central to precision medicine and the design of clinical trials. We developed and validated machine-learning models for three important clinical end points in patients with adva...
BACKGROUND: Current outcomes prediction tools are largely based on and limited by regression methods. Utilization of machine learning (ML) methods that can handle multiple diverse inputs could strengthen predictive abilities and improve patient outco...
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