AIMC Topic: Predictive Value of Tests

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A Diagnostic Prediction Model of Acute Symptomatic Portal Vein Thrombosis.

Annals of vascular surgery Jul 25, 2019
BACKGROUND: The aim of this study was to develop a diagnostic prediction model to improve identification of acute symptomatic portal vein thrombosis (PVT).
Support Vector Machine Adult Venous Thrombosis Humans Male Predictive Value of Tests Middle Aged Acute Disease Diagnosis, Computer-Assisted Portal Vein China Decision Support Techniques Retrospective Studies Female Reproducibility of Results Risk Factors
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Development and validation of a deep-learning algorithm for the detection of neovascular age-related macular degeneration from colour fundus photographs.

Clinical & experimental ophthalmology Jul 25, 2019
IMPORTANCE: Detection of early onset neovascular age-related macular degeneration (AMD) is critical to protecting vision.
False Positive Reactions Humans Fluorescein Angiography Algorithms Sensitivity and Specificity Retrospective Studies Wet Macular Degeneration Female Male ROC Curve Choroidal Neovascularization Reproducibility of Results Middle Aged Visual Acuity Diagnosis, Computer-Assisted Predictive Value of Tests Photography Deep Learning Area Under Curve Ophthalmologists Fundus Oculi
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Response to repeat echoendoscopic celiac plexus neurolysis in pancreatic cancer patients: A machine learning approach.

Pancreatology : official journal of the International Association of Pancreatology (IAP) ... [et al.] Jul 24, 2019
BACKGROUND: /Objectives: Efficacy of repeat echoendoscopic celiac plexus neurolysis is still unclear. Aim of the study was to assess the efficacy of repeat celiac plexus neurolysis and to build an artificial neural network model able to predict pain ...
Pain Management Endosonography Likelihood Functions Celiac Plexus Treatment Outcome Humans Pancreatic Neoplasms Machine Learning Predictive Value of Tests Female Pain Measurement Aged Adult Pain Aged, 80 and over Male Middle Aged Disease Progression Nerve Block
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Prediction of Chemotherapy Response of Osteosarcoma Using Baseline F-FDG Textural Features Machine Learning Approaches with PCA.

Contrast media & molecular imaging Jul 24, 2019
PURPOSE: Patients with high-grade osteosarcoma undergo several chemotherapy cycles before surgical intervention. Response to chemotherapy, however, is affected by intratumor heterogeneity. In this study, we assessed the ability of a machine learning ...
Humans Principal Component Analysis Area Under Curve Machine Learning Drug Monitoring Support Vector Machine Positron-Emission Tomography Osteosarcoma Fluorodeoxyglucose F18 Predictive Value of Tests
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Predicting ideal outcome after pediatric liver transplantation: An exploratory study using machine learning analyses to leverage Studies of Pediatric Liver Transplantation Data.

Pediatric transplantation Jul 22, 2019
Machine learning analyses allow for the consideration of numerous variables in order to accommodate complex relationships that would not otherwise be apparent in traditional statistical methods to better classify patient risk. The SPLIT registry data...
Risk Factors Young Adult Adult Risk Assessment Infant Adolescent Child, Preschool Child Treatment Outcome Liver Failure Predictive Value of Tests Biliary Tract Surgical Procedures Registries Humans Liver Transplantation Machine Learning Pediatrics Software Anastomosis, Surgical Algorithms Prospective Studies
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Artificial neural networks accurately predict intra-abdominal infection in moderately severe and severe acute pancreatitis.

Journal of digestive diseases Jul 21, 2019
OBJECTIVE: The aim of this study was to evaluate the efficacy of artificial neural networks (ANN) in predicting intra-abdominal infection in moderately severe (MASP) and severe acute pancreatitis (SAP) compared with that of a logistic regression mode...
APACHE Pancreatitis Humans Female Intraabdominal Infections Severity of Illness Index Neural Networks, Computer Risk Factors Prognosis Adult ROC Curve Retrospective Studies Male Logistic Models Middle Aged Predictive Value of Tests Models, Biological
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Denoising and artefact removal for transthoracic echocardiographic imaging in congenital heart disease: utility of diagnosis specific deep learning algorithms.

The international journal of cardiovascular imaging Jul 19, 2019
Deep learning (DL) algorithms are increasingly used in cardiac imaging. We aimed to investigate the utility of DL algorithms in de-noising transthoracic echocardiographic images and removing acoustic shadowing artefacts specifically in patients with ...
Humans Female Reproducibility of Results Young Adult Adult Predictive Value of Tests Male Image Interpretation, Computer-Assisted Echocardiography Artifacts Middle Aged Signal-To-Noise Ratio Case-Control Studies Heart Defects, Congenital Deep Learning
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Artificial intelligence in digital pathology: a roadmap to routine use in clinical practice.

The Journal of pathology Jul 18, 2019
The use of artificial intelligence will transform clinical practice over the next decade and the early impact of this will likely be the integration of image analysis and machine learning into routine histopathology. In the UK and around the world, a...
Diagnosis, Computer-Assisted Predictive Value of Tests Workflow Diffusion of Innovation Image Interpretation, Computer-Assisted Pathology Humans Artificial Intelligence Forecasting Reproducibility of Results
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Deep learning outperformed 11 pathologists in the classification of histopathological melanoma images.

European journal of cancer (Oxford, England : 1990) Jul 18, 2019
BACKGROUND: The diagnosis of most cancers is made by a board-certified pathologist based on a tissue biopsy under the microscope. Recent research reveals a high discordance between individual pathologists. For melanoma, the literature reports on 25-2...
Diagnosis, Computer-Assisted Deep Learning Diagnosis, Differential Predictive Value of Tests Biopsy Humans Melanoma Skin Neoplasms Reproducibility of Results Pathologists Microscopy Image Interpretation, Computer-Assisted Observer Variation Nevus
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Fully Automated, Quality-Controlled Cardiac Analysis From CMR: Validation and Large-Scale Application to Characterize Cardiac Function.

JACC. Cardiovascular imaging Jul 17, 2019
OBJECTIVES: This study sought to develop a fully automated framework for cardiac function analysis from cardiac magnetic resonance (CMR), including comprehensive quality control (QC) algorithms to detect erroneous output.
Ventricular Function, Right Middle Aged Diagnosis, Computer-Assisted Deep Learning Predictive Value of Tests Quality Control Heart Diseases Image Interpretation, Computer-Assisted Quality Indicators, Health Care Automation Magnetic Resonance Imaging, Cine Reproducibility of Results Aged Male Ventricular Function, Left Stroke Volume Humans Female
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