AIMC Topic: Humans

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Robot-assisted vs. laparoscopic repair of complete upside-down stomach hiatal hernia (the RATHER-study): a prospective comparative single center study.

Surgical endoscopy
BACKGROUND: Complete upside-down stomach (cUDS) hernias are a subgroup of large hiatal hernias characterized by high risk of life-threatening complications and technically challenging surgical repair including complex mediastinal dissection. In a pro...

Deep learning for automatically predicting early haematoma expansion in Chinese patients.

Stroke and vascular neurology
BACKGROUND AND PURPOSE: Early haematoma expansion is determinative in predicting outcome of intracerebral haemorrhage (ICH) patients. The aims of this study are to develop a novel prediction model for haematoma expansion by applying deep learning mod...

Detection of Diabetic Retinopathy from Ultra-Widefield Scanning Laser Ophthalmoscope Images: A Multicenter Deep Learning Analysis.

Ophthalmology. Retina
PURPOSE: To develop a deep learning (DL) system that can detect referable diabetic retinopathy (RDR) and vision-threatening diabetic retinopathy (VTDR) from images obtained on ultra-widefield scanning laser ophthalmoscope (UWF-SLO).

Improved Segmentation of the Intracranial and Ventricular Volumes in Populations with Cerebrovascular Lesions and Atrophy Using 3D CNNs.

Neuroinformatics
Successful segmentation of the total intracranial vault (ICV) and ventricles is of critical importance when studying neurodegeneration through neuroimaging. We present iCVMapper and VentMapper, robust algorithms that use a convolutional neural networ...

Artificial intelligence enables whole-body positron emission tomography scans with minimal radiation exposure.

European journal of nuclear medicine and molecular imaging
PURPOSE: To generate diagnostic F-FDG PET images of pediatric cancer patients from ultra-low-dose F-FDG PET input images, using a novel artificial intelligence (AI) algorithm.

Integrating Multiomics Information in Deep Learning Architectures for Joint Actuarial Outcome Prediction in Non-Small Cell Lung Cancer Patients After Radiation Therapy.

International journal of radiation oncology, biology, physics
PURPOSE: Novel actuarial deep learning neural network (ADNN) architectures are proposed for joint prediction of radiation therapy outcomes-radiation pneumonitis (RP) and local control (LC)-in stage III non-small cell lung cancer (NSCLC) patients. Unl...

Artificial intelligence-enhanced electrocardiography in cardiovascular disease management.

Nature reviews. Cardiology
The application of artificial intelligence (AI) to the electrocardiogram (ECG), a ubiquitous and standardized test, is an example of the ongoing transformative effect of AI on cardiovascular medicine. Although the ECG has long offered valuable insigh...

Brain tumor segmentation using K-means clustering and deep learning with synthetic data augmentation for classification.

Microscopy research and technique
Image processing plays a major role in neurologists' clinical diagnosis in the medical field. Several types of imagery are used for diagnostics, tumor segmentation, and classification. Magnetic resonance imaging (MRI) is favored among all modalities ...

Machine learning techniques to detect and forecast the daily total COVID-19 infected and deaths cases under different lockdown types.

Microscopy research and technique
COVID-19 has impacted the world in many ways, including loss of lives, economic downturn and social isolation. COVID-19 was emerged due to the SARS-CoV-2 that is highly infectious pandemic. Every country tried to control the COVID-19 spread by imposi...

An algorithm for using deep learning convolutional neural networks with three dimensional depth sensor imaging in scoliosis detection.

The spine journal : official journal of the North American Spine Society
BACKGROUND CONTEXT: Timely intervention in growing individuals, such as brace treatment, relies on early detection of adolescent idiopathic scoliosis (AIS). To this end, several screening methods have been implemented. However, these methods have lim...