AIMC Topic: Humans

Clear Filters Showing 56591 to 56600 of 95995 articles

Deep learning-based method for reducing residual motion effects in diffusion parameter estimation.

Magnetic resonance in medicine
PURPOSE: Conventional motion-correction techniques for diffusion MRI can introduce motion-level-dependent bias in derived metrics. To address this challenge, a deep learning-based technique was developed to minimize such residual motion effects.

Dermoscopic diagnostic performance of Japanese dermatologists for skin tumors differs by patient origin: A deep learning convolutional neural network closes the gap.

The Journal of dermatology
In the dermoscopic diagnosis of skin tumors, it remains unclear whether a deep neural network (DNN) trained with images from fair-skinned-predominant archives is helpful when applied for patients with darker skin. This study compared the performance ...

Stacked autoencoders as new models for an accurate Alzheimer's disease classification support using resting-state EEG and MRI measurements.

Clinical neurophysiology : official journal of the International Federation of Clinical Neurophysiology
OBJECTIVE: This retrospective and exploratory study tested the accuracy of artificial neural networks (ANNs) at detecting Alzheimer's disease patients with dementia (ADD) based on input variables extracted from resting-state electroencephalogram (rsE...

AI-driven quantification, staging and outcome prediction of COVID-19 pneumonia.

Medical image analysis
Coronavirus disease 2019 (COVID-19) emerged in 2019 and disseminated around the world rapidly. Computed tomography (CT) imaging has been proven to be an important tool for screening, disease quantification and staging. The latter is of extreme import...

CoLe-CNN: Context-learning convolutional neural network with adaptive loss function for lung nodule segmentation.

Computer methods and programs in biomedicine
BACKGROUND AND OBJECTIVE: An accurate segmentation of lung nodules in computed tomography images is a crucial step for the physical characterization of the tumour. Being often completely manually accomplished, nodule segmentation turns to be a tediou...

OCT Signal Enhancement with Deep Learning.

Ophthalmology. Glaucoma
PURPOSE: To establish whether deep learning methods are able to improve the signal-to-noise ratio of time-domain (TD) OCT images to approach that of spectral-domain (SD) OCT images.

Deep learning analysis for the detection of pancreatic cancer on endosonographic images: a pilot study.

Journal of hepato-biliary-pancreatic sciences
BACKGROUND/PURPOSE: The application of artificial intelligence to clinical diagnostics using deep learning has been developed in recent years. In this study, we developed an original computer-assisted diagnosis (CAD) system using deep learning analys...

Fourier transform infrared spectroscopy combined with deep learning and data enhancement for quick diagnosis of abnormal thyroid function.

Photodiagnosis and photodynamic therapy
BACKGROUND: To evaluate the Fourier transform infrared spectroscopy (FT-IR) combined with deep learning models to allow for quick diagnosis of abnormal thyroid function.

Identification of Patients with Nontraumatic Intracranial Hemorrhage Using Administrative Claims Data.

Journal of stroke and cerebrovascular diseases : the official journal of National Stroke Association
INTRODUCTION: Nontraumatic intracranial hemorrhage (ICH) is a neurological emergency of research interest; however, unlike ischemic stroke, has not been well studied in large datasets due to the lack of an established administrative claims-based defi...

Artificial Intelligence and Its Role in Identifying Esophageal Neoplasia.

Digestive diseases and sciences
Randomized trials have demonstrated that ablation of dysplastic Barrett's esophagus can reduce the risk of progression to cancer. Endoscopic resection for early stage esophageal adenocarcinoma and squamous cell carcinoma can significantly reduce post...