AIMC Topic: Humans

Clear Filters Showing 50531 to 50540 of 95995 articles

Bayesian Fully Convolutional Networks for Brain Image Registration.

Journal of healthcare engineering
The purpose of medical image registration is to find geometric transformations that align two medical images so that the corresponding voxels on two images are spatially consistent. Nonrigid medical image registration is a key step in medical image p...

Intelligent Network Applications Monitoring and Diagnosis Employing Software Sensing and Machine Learning Solutions.

Sensors (Basel, Switzerland)
The article presents a research in the field of complex sensing, detection, and recovery of communications networks applications and hardware, in case of failures, maloperations, or unauthorized intrusions. A case study, based on Davis AI engine oper...

Deep grey matter quantitative susceptibility mapping from small spatial coverages using deep learning.

Zeitschrift fur medizinische Physik
INTRODUCTION: Quantitative Susceptibility Mapping (QSM) is generally acquired with full brain coverage, even though many QSM brain-iron studies focus on the deep grey matter (DGM) region only. Reducing the spatial coverage to the DGM vicinity can sub...

Machine learning analysis of multispectral imaging and clinical risk factors to predict amputation wound healing.

Journal of vascular surgery
OBJECTIVE: Prediction of amputation wound healing is challenging due to the multifactorial nature of critical limb ischemia and lack of objective assessment tools. Up to one-third of amputations require revision to a more proximal level within 1 year...

Needs for re-intervention on restored teeth in adults: a practice-based study.

Clinical oral investigations
OBJECTIVES: Evaluate the need for re-intervention on dental coronal restorations in adults seen in a network of general dental practitioners (ReCOL).  MATERIALS AND METHODS: This observational, cross-sectional, multicenter study involved 40 practitio...

Application of deep learning to identify COVID-19 infection in posteroanterior chest X-rays.

Clinical imaging
INTRODUCTION: The objective of this study was to assess seven configurations of six convolutional deep neural network architectures for classification of chest X-rays (CXRs) as COVID-19 positive or negative.

Three artificial intelligence data challenges based on CT and ultrasound.

Diagnostic and interventional imaging
PURPOSE: The 2020 edition of these Data Challenges was organized by the French Society of Radiology (SFR), from September 28 to September 30, 2020. The goals were to propose innovative artificial intelligence solutions for the current relevant proble...

Joint fully convolutional and graph convolutional networks for weakly-supervised segmentation of pathology images.

Medical image analysis
Tissue/region segmentation of pathology images is essential for quantitative analysis in digital pathology. Previous studies usually require full supervision (e.g., pixel-level annotation) which is challenging to acquire. In this paper, we propose a ...

Development of quantitative model of a local lymph node assay for evaluating skin sensitization potency applying machine learning CatBoost.

Regulatory toxicology and pharmacology : RTP
The estimated concentrations for a stimulation index of 3 (EC3) in murine local lymph node assay (LLNA) is an important quantitative value for determining the strength of skin sensitization to chemicals, including cosmetic ingredients. However, anima...

Robotic Dual-Console Distal Pancreatectomy: Could it be Considered a Safe Approach and Surgical Teaching even in Pancreatic Surgery? A Retrospective Observational Study Cohort.

World journal of surgery
BACKGROUND: The study aims to assess the safety and feasibility of the robotic dual-console during a robotic distal pancreatectomy METHODS: The data of the consecutive patients submitted to RDP from 2012 to 2019 at the Verona University were retrieve...