AIMC Journal:
Academic radiology

Showing 221 to 230 of 317 articles

Lung-Optimized Deep-Learning-Based Reconstruction for Ultralow-Dose CT.

Academic radiology Jun 20, 2022
RATIONALE AND OBJECTIVES: To evaluate the image properties of lung-specialized deep-learning-based reconstruction (DLR) and its applicability in ultralow-dose CT (ULDCT) relative to hybrid- (HIR) and model-based iterative-reconstructions (MBIR).
Radiation Dosage Algorithms Tomography, X-Ray Computed Deep Learning Radiographic Image Interpretation, Computer-Assisted Lung Humans
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Deep Learning-Based Digitally Reconstructed Tomography of the Chest in the Evaluation of Solitary Pulmonary Nodules: A Feasibility Study.

Academic radiology Jun 9, 2022
RATIONALE AND OBJECTIVES: Computed tomography (CT) is preferred for evaluating solitary pulmonary nodules (SPNs) but access or availability may be lacking, in addition, overlapping anatomy can hinder detection of SPNs on chest radiographs. We develop...
Retrospective Studies Tomography, X-Ray Computed Female Lung Neoplasms Humans Aged Deep Learning Solitary Pulmonary Nodule Feasibility Studies
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Automated Endotracheal Tube Placement Check Using Semantically Embedded Deep Neural Networks.

Academic radiology May 27, 2022
RATIONALE AND OBJECTIVES: To develop artificial intelligence (AI) system that assists in checking endotracheal tube (ETT) placement on chest X-rays (CXRs) and evaluate whether it can move into clinical validation as a quality improvement tool.
Humans Neural Networks, Computer Artificial Intelligence Intubation, Intratracheal Retrospective Studies Trachea
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Comprehensive Clinical Evaluation of a Deep Learning-Accelerated, Single-Breath-Hold Abdominal HASTE at 1.5 T and 3 T.

Academic radiology Apr 22, 2022
To evaluate the clinical performance of a deep learning-accelerated single-breath-hold half-Fourier acquisition single-shot turbo spin echo (HASTE)-sequence for T2-weighted fat-suppressed MRI of the abdomen at 1.5 T and 3 T in comparison to standard ...
Artifacts Retrospective Studies Deep Learning Abdomen Magnetic Resonance Imaging Liver Neoplasms Humans
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Pros and Cons of Applying Deep Learning Automatic Scan-Range Adjustment to Low-Dose Chest CT in Lung Cancer Screening Programs.

Academic radiology Apr 8, 2022
Tomography, X-Ray Computed Deep Learning Lung Neoplasms Humans Early Detection of Cancer Thorax
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An Interpretable Chest CT Deep Learning Algorithm for Quantification of COVID-19 Lung Disease and Prediction of Inpatient Morbidity and Mortality.

Academic radiology Apr 4, 2022
RATIONALE AND OBJECTIVES: The burden of coronavirus disease 2019 (COVID-19) airspace opacities is time consuming and challenging to quantify on computed tomography. The purpose of this study was to evaluate the ability of a deep convolutional neural ...
COVID-19 Testing Inpatients Morbidity Retrospective Studies Tomography, X-Ray Computed Lung Humans Deep Learning SARS-CoV-2 COVID-19 Algorithms
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Deep Learning Classification of Spinal Osteoporotic Compression Fractures on Radiographs using an Adaptation of the Genant Semiquantitative Criteria.

Academic radiology Mar 26, 2022
RATIONALE AND OBJECTIVES: Osteoporosis affects 9% of individuals over 50 in the United States and 200 million women globally. Spinal osteoporotic compression fractures (OCFs), an osteoporosis biomarker, are often incidental and under-reported. Accura...
Osteoporosis Female Humans Spinal Fractures Deep Learning Male Fractures, Compression Radiography
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Development of Deep Learning-based Automatic Scan Range Setting Model for Lung Cancer Screening Low-dose CT Imaging.

Academic radiology Feb 5, 2022
RATIONALE AND OBJECTIVES: To develop an automatic setting of a deep learning-based system for detecting low-dose computed tomography (CT) lung cancer screening scan range and compare its efficiency with the radiographer's performance.
Early Detection of Cancer Deep Learning Humans Lung Neoplasms Retrospective Studies Tomography, X-Ray Computed
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FDA-regulated AI Algorithms: Trends, Strengths, and Gaps of Validation Studies.

Academic radiology Dec 27, 2021
RATIONALE AND OBJECTIVES: To assess key trends, strengths, and gaps in validation studies of the Food and Drug Administration (FDA)-regulated imaging-based artificial intelligence/machine learning (AI/ML) algorithms.
ROC Curve Machine Learning United States Humans United States Food and Drug Administration Algorithms Artificial Intelligence
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MRI Radiomics of Breast Cancer: Machine Learning-Based Prediction of Lymphovascular Invasion Status.

Academic radiology Dec 4, 2021
RATIONALE AND OBJECTIVES: In patients with breast cancer (BC), lymphovascular invasion (LVI) status is considered an important prognostic factor. We aimed to develop machine learning (ML)-based radiomics models for the prediction of LVI status in pat...
Reproducibility of Results Breast Neoplasms Lymph Nodes Retrospective Studies Humans Female Magnetic Resonance Imaging Machine Learning Neoplasm Invasiveness
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