AIMC Topic: Dimensional Measurement Accuracy

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Natural Language Processing to Identify Pulmonary Nodules and Extract Nodule Characteristics From Radiology Reports.

Chest Jun 4, 2021
BACKGROUND: There is an urgent need for population-based studies on managing patients with pulmonary nodules.
Radiology Tumor Burden Calcinosis Natural Language Processing Radiography, Thoracic Dimensional Measurement Accuracy Quality Improvement Documentation Lung Lung Neoplasms Multiple Pulmonary Nodules Solitary Pulmonary Nodule Tomography, X-Ray Computed Sensitivity and Specificity Humans Algorithms
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Semiautomatic Three-Dimensional Threshold-Based Cardiac Computed Tomography Ventricular Volumetry in Repaired Tetralogy of Fallot: Comparison with Cardiac Magnetic Resonance Imaging.

Korean journal of radiology Dec 27, 2018
OBJECTIVE: To assess the accuracy and potential bias of computed tomography (CT) ventricular volumetry using semiautomatic three-dimensional (3D) threshold-based segmentation in repaired tetralogy of Fallot, and to compare them to those of two-dimens...
Young Adult Cone-Beam Computed Tomography Adult Magnetic Resonance Imaging Retrospective Studies Male Dimensional Measurement Accuracy Adolescent Cardiac Surgical Procedures Female Stroke Volume Tetralogy of Fallot Heart Ventricles Child Humans
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Diagnostic assessment of a deep learning system for detecting atrial fibrillation in pulse waveforms.

Heart (British Cardiac Society) May 31, 2018
OBJECTIVE: To evaluate the diagnostic performance of a deep learning system for automated detection of atrial fibrillation (AF) in photoplethysmographic (PPG) pulse waveforms.
Telemedicine Atrial Fibrillation Photoplethysmography Electrocardiography Dimensional Measurement Accuracy Comparative Effectiveness Research Monitoring, Ambulatory Ventricular Premature Complexes Male Middle Aged Deep Learning Smartphone Aged Sensitivity and Specificity Female Humans
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Predicting protein conformational changes for unbound and homology docking: learning from intrinsic and induced flexibility.

Proteins Dec 5, 2016
Predicting protein conformational changes from unbound structures or even homology models to bound structures remains a critical challenge for protein docking. Here we present a study directly addressing the challenge by reducing the dimensionality a...
Protein Binding Computational Biology Thermodynamics Models, Statistical Proteins Machine Learning Protein Multimerization Research Design Dimensional Measurement Accuracy Software Benchmarking Structural Homology, Protein Molecular Docking Simulation Binding Sites Protein Conformation
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