AIMC Topic: Retrospective Studies

Clear Filters Showing 6711 to 6720 of 9989 articles

Generating High-Quality Lymph Node Clinical Target Volumes for Head and Neck Cancer Radiation Therapy Using a Fully Automated Deep Learning-Based Approach.

International journal of radiation oncology, biology, physics
PURPOSE: To develop a deep learning model that generates consistent, high-quality lymph node clinical target volumes (CTV) contours for head and neck cancer (HNC) patients, as an integral part of a fully automated radiation treatment planning workflo...

Recurrent Hemoptysis After Bronchial Artery Embolization: Prediction Using a Nomogram and Artificial Neural Network Model.

AJR. American journal of roentgenology
The purpose of this study was to develop an effective nomogram and artificial neural network (ANN) model for predicting recurrent hemoptysis after bronchial artery embolization (BAE). The institutional ethics review boards of the two participating ...

Combination of Deep Learning-Based Denoising and Iterative Reconstruction for Ultra-Low-Dose CT of the Chest: Image Quality and Lung-RADS Evaluation.

AJR. American journal of roentgenology
The objective of our study was to assess the effect of the combination of deep learning-based denoising (DLD) and iterative reconstruction (IR) on image quality and Lung Imaging Reporting and Data System (Lung-RADS) evaluation on chest ultra-low-dos...

[Epicardial fat Tissue Volumetry: Comparison of Semi-Automatic Measurement and the Machine Learning Algorithm].

Kardiologiia
Aim        To compare assessments of epicardial adipose tissue (EAT) volumes obtained with a semi-automatic, physician-performed analysis and an automatic analysis using a machine-learning algorithm by data of low-dose (LDCT) and standard computed to...

Feasibility, safety and efficacy of argon beam coagulation in robot-assisted partial nephrectomy for solid renal masses ≤ 7 cm in size.

Journal of robotic surgery
One of the most important steps of the partial nephrectomy (PN) is hemostatic control of tumor bed which also effects the warm ischemia time (WIT). Argon beam coagulation (ABC) for decades is a well-known method for surface controls during major open...

Differential diagnosis for esophageal protruded lesions using a deep convolution neural network in endoscopic images.

Gastrointestinal endoscopy
BACKGROUND AND AIMS: Recent advances in deep convolutional neural networks (CNNs) have led to remarkable results in digestive endoscopy. In this study, we aimed to develop CNN-based models for the differential diagnosis of benign esophageal protruded...

The Challenges of Implementing Artificial Intelligence into Surgical Practice.

World journal of surgery
BACKGROUND: Artificial intelligence is touted as the future of medicine. Classical algorithms for the detection of common bile duct stones (CBD) have had poor clinical uptake due to low accuracy. This study explores the challenges of developing and i...

Risks and complications of robot-assisted radical prostatectomy (RARP) in patients receiving antiplatelet and/or anticoagulant therapy: a retrospective cohort study in a single institute.

Journal of robotic surgery
The objective of the study was to evaluate the risk of bleeding complications in patients undergoing robot-assisted radical prostatectomy (RARP) while taking antiplatelet (AP) and/or anticoagulant (AC) agents. We analyzed the data of 334 patients und...

A deep-learning system for the assessment of cardiovascular disease risk via the measurement of retinal-vessel calibre.

Nature biomedical engineering
Retinal blood vessels provide information on the risk of cardiovascular disease (CVD). Here, we report the development and validation of deep-learning models for the automated measurement of retinal-vessel calibre in retinal photographs, using divers...

Prognostic value of FDG-PET radiomics with machine learning in pancreatic cancer.

Scientific reports
Patients with pancreatic cancer have a poor prognosis, therefore identifying particular tumor characteristics associated with prognosis is important. This study aims to investigate the utility of radiomics with machine learning using F-fluorodeoxyglu...