AIMC Topic: Retrospective Studies

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Prediction of cervical spine injury in young pediatric patients: an optimal trees artificial intelligence approach.

Journal of pediatric surgery
BACKGROUND: Cervical spine injuries (CSI) are a major concern in young pediatric trauma patients. The consequences of missed injuries and difficulties in injury clearance for non-verbal patients have led to a tendency to image young children. Imaging...

Data-driven synthetic MRI FLAIR artifact correction via deep neural network.

Journal of magnetic resonance imaging : JMRI
BACKGROUND: FLAIR (fluid attenuated inversion recovery) imaging via synthetic MRI methods leads to artifacts in the brain, which can cause diagnostic limitations. The main sources of the artifacts are attributed to the partial volume effect and flow,...

Automatic Segmentation of the Prostate on CT Images Using Deep Neural Networks (DNN).

International journal of radiation oncology, biology, physics
PURPOSE: Recent advances in deep neural networks (DNNs) have unlocked opportunities for their application for automatic image segmentation. We have evaluated a DNN-based algorithm for automatic segmentation of the prostate gland on a large cohort of ...

Multi-parametric MRI-based radiomics signature for discriminating between clinically significant and insignificant prostate cancer: Cross-validation of a machine learning method.

European journal of radiology
PURPOSE: To evaluate the performance of a multi-parametric MRI (mp-MRI)-based radiomics signature for discriminating between clinically significant prostate cancer (csPCa) and insignificant PCa (ciPCa).

Incorporating prior knowledge via volumetric deep residual network to optimize the reconstruction of sparsely sampled MRI.

Magnetic resonance imaging
For sparse sampling that accelerates magnetic resonance (MR) image acquisition, non-linear reconstruction algorithms have been developed, which incorporated patient specific a prior information. More generic a prior information could be acquired via ...

Development of an intelligent decision support system for ischemic stroke risk assessment in a population-based electronic health record database.

PloS one
BACKGROUND: Intelligent decision support systems (IDSS) have been applied to tasks of disease management. Deep neural networks (DNNs) are artificial intelligent techniques to achieve high modeling power. The application of DNNs to large-scale data fo...

Prediction of survival outcomes in patients with epithelial ovarian cancer using machine learning methods.

Journal of gynecologic oncology
OBJECTIVES: The aim of this study was to develop a new prognostic classification for epithelial ovarian cancer (EOC) patients using gradient boosting (GB) and to compare the accuracy of the prognostic model with the conventional statistical method.