AIMC Topic: Humans

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SCMAG: A Semisupervised Single-Cell Clustering Method Based on Matrix Aggregation Graph Convolutional Neural Network.

Computational and mathematical methods in medicine
Clustering analysis is one of the most important technologies for single-cell data mining. It is widely used in the division of different gene sequences, the identification of functional genes, and the detection of new cell types. Although the tradit...

Classification of Alzheimer's Disease Using Gaussian-Based Bayesian Parameter Optimization for Deep Convolutional LSTM Network.

Computational and mathematical methods in medicine
Alzheimer's disease (AD) is one of the most important causes of mortality in elderly people, and it is often challenging to use traditional manual procedures when diagnosing a disease in the early stages. The successful implementation of machine lear...

Segmentation of Overlapping Cervical Cells with Mask Region Convolutional Neural Network.

Computational and mathematical methods in medicine
The task of segmenting cytoplasm in cytology images is one of the most challenging tasks in cervix cytological analysis due to the presence of fuzzy and highly overlapping cells. Deep learning-based diagnostic technology has proven to be effective in...

Geometric Atlas of the Middle Ear and Paranasal Sinuses for Robotic Applications.

Surgical innovation
In otolaryngologic surgery, more and more robots are being studied to meet the clinical needs of operating rooms. However, to help design and optimize these robots, the workspace must be precisely defined taking into account patient variability. The ...

Less invasive and equivalent short-term outcomes with simultaneous en bloc robot-assisted radical cystectomy and laparoscopic nephroureterectomy: Comparison with conventional open radical cystectomy and nephroureterectomy.

Asian journal of endoscopic surgery
INTRODUCTION: To clarify the safety and efficacy of en bloc simultaneous robot-assisted radical cystectomy (RARC) and laparoscopic nephroureterectomy (LNU) for synchronous muscle-invasive bladder carcinoma and upper tract urothelial carcinoma (UTUC) ...

Deep-learning model for screening sepsis using electrocardiography.

Scandinavian journal of trauma, resuscitation and emergency medicine
BACKGROUND: Sepsis is a life-threatening organ dysfunction and a major healthcare burden worldwide. Although sepsis is a medical emergency that requires immediate management, screening for the occurrence of sepsis is difficult. Herein, we propose a d...

The Future Role of High-Performance Computing in Cardiovascular Medicine and Science -Impact of Multi-Dimensional Data Analysis.

Journal of atherosclerosis and thrombosis
Advances in High-performance computing (HPC) technology have reached the capacity to inform cardiovascular (CV) science in the realm of both inductive and constructive approaches. Clinical trials allow for the comparison of the effect of an intervent...

A deep learning algorithm for white matter hyperintensity lesion detection and segmentation.

Neuroradiology
PURPOSE: White matter hyperintensity (WMHI) lesions on MR images are an important indication of various types of brain diseases that involve inflammation and blood vessel abnormalities. Automated quantification of the WMHI can be valuable for the cli...

Prediction of post-stroke cognitive impairment using brain FDG PET: deep learning-based approach.

European journal of nuclear medicine and molecular imaging
PURPOSE: Post-stroke cognitive impairment can affect up to one third of stroke survivors. Since cognitive function greatly contributes to patients' quality of life, an objective quantitative biomarker for early prediction of dementia after stroke is ...