BACKGROUND: Survival of liver transplant recipients beyond 1 year since transplantation is compromised by an increased risk of cancer, cardiovascular events, infection, and graft failure. Few clinical tools are available to identify patients at risk ...
Deep learning (DL) is an advanced machine learning approach used in diverse areas such as bioinformatics, image analysis, and natural language processing. Here, using brain magnetic resonance imaging (MRI) data obtained at early stages of infarcts, w...
BACKGROUND: To establish a decision model based on two- (2D) and three-dimensional (3D) eye data of patients with ptosis for developing personalized surgery plans.
BACKGROUND: To evaluate the role of radiomics based on magnetic resonance imaging (MRI) in the biological activity of hepatic alveolar echinococcosis (HAE).
Computational intelligence and neuroscience
Apr 8, 2021
This paper aims to investigate the use of transfer learning architectures in the detection of COVID-19 from CT lung scans. The study evaluates the performances of various transfer learning architectures, as well as the effects of the standard Histogr...
: This study developed and evaluated a deep learning ensemble method to automatically grade the stages of glaucoma depending on its severity.: After cross-validation of three glaucoma specialists, the final dataset comprised of 3,460 fundus photograp...
Clinical pharmacology and therapeutics
Apr 6, 2021
Therapeutic drug monitoring of mycophenolic acid (MPA) based on area under the curve (AUC) is well-established and machine learning (ML) approaches could help to estimate AUC. The aim of this work is to estimate the AUC of MPA in organ transplant pat...
OBJECTIVE: H3K27M mutation in gliomas has prognostic implications. Previous magnetic resonance imaging (MRI) studies have reported variable rates of tumoral enhancement, necrotic changes, and peritumoral edema in H3K27M-mutant gliomas, with no distin...
IMPORTANCE: Quality improvement programs for colorectal cancer surgery have been introduced with benchmarking based on quality indicators, such as mortality. Detailed (pre)operative characteristics may offer relevant information for proper case-mix c...
International journal of computer assisted radiology and surgery
Mar 31, 2021
PURPOSE: In this study, we try to consider the most common type of total anomalous pulmonary venous connection and established a machine learning-based prediction model for postoperative pulmonary venous obstruction by using clinical data and CT imag...