AIMC Topic: Adult

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Retrospective motion artifact correction of structural MRI images using deep learning improves the quality of cortical surface reconstructions.

NeuroImage
Head motion during MRI acquisition presents significant challenges for neuroimaging analyses. In this work, we present a retrospective motion correction framework built on a Fourier domain motion simulation model combined with established 3D convolut...

Robotic Versus Laparoscopic Adrenalectomy: Pluriannual Experience in a High-Volume Center Evaluating Indications and Results.

Journal of laparoendoscopic & advanced surgical techniques. Part A
Robotic adrenalectomy offers several clinical benefits if compared with laparoscopic adrenalectomy; however, its superiority is still under debate. The aim of this study was the investigation of differences between the two techniques, and a comparis...

Stochastic synchronization of dynamics on the human connectome.

NeuroImage
Synchronization is a collective mechanism by which oscillatory networks achieve their functions. Factors driving synchronization include the network's topological and dynamical properties. However, how these factors drive the emergence of synchroniza...

Predicting adult neuroscience intensive care unit admission from emergency department triage using a retrospective, tabular-free text machine learning approach.

Scientific reports
Early admission to the neurosciences intensive care unit (NSICU) is associated with improved patient outcomes. Natural language processing offers new possibilities for mining free text in electronic health record data. We sought to develop a machine ...

Improving prognostic performance in resectable pancreatic ductal adenocarcinoma using radiomics and deep learning features fusion in CT images.

Scientific reports
As an analytic pipeline for quantitative imaging feature extraction and analysis, radiomics has grown rapidly in the past decade. On the other hand, recent advances in deep learning and transfer learning have shown significant potential in the quanti...

Classification of malignant tumours in breast ultrasound using unsupervised machine learning approaches.

Scientific reports
Traditional computer-aided diagnosis (CAD) processes include feature extraction, selection, and classification. Effective feature extraction in CAD is important in improving the classification's performance. We introduce a machine-learning method and...

Aqueous autotaxin and TGF-βs are promising diagnostic biomarkers for distinguishing open-angle glaucoma subtypes.

Scientific reports
The purpose of this study is to examine if aqueous autotaxin (ATX) and TGF-β levels could be used for differentiating glaucoma subtypes. This prospective observational study was performed using aqueous humor samples obtained from 281 consecutive pati...

Machine learning predicts lymph node metastasis of poorly differentiated-type intramucosal gastric cancer.

Scientific reports
To construct a machine learning algorithm model of lymph node metastasis (LNM) in patients with poorly differentiated-type intramucosal gastric cancer. 1169 patients with postoperative gastric cancer were divided into a training group and a test grou...

Deep learning-based automatic delineation of the hippocampus by MRI: geometric and dosimetric evaluation.

Radiation oncology (London, England)
BACKGROUND: Whole brain radiotherapy (WBRT) can impair patients' cognitive function. Hippocampal avoidance during WBRT can potentially prevent this side effect. However, manually delineating the target area is time-consuming and difficult. Here, we p...