AIMC Topic: Radiographic Image Interpretation, Computer-Assisted
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Classification of COVID-19 Chest CT Images Based on Ensemble Deep Learning.
Journal of healthcare engineering
Apr 20, 2021
Novel coronavirus pneumonia (NCP) has become a global pandemic disease, and computed tomography-based (CT) image analysis and recognition are one of the important tools for clinical diagnosis. In order to assist medical personnel to achieve an effici...
Deep-learning-based image reconstruction in dynamic contrast-enhanced abdominal CT: image quality and lesion detection among reconstruction strength levels.
Clinical radiology
Apr 18, 2021
AIM: To evaluate the use of deep-learning-based image reconstruction (DLIR) algorithms in dynamic contrast-enhanced computed tomography (CT) of the abdomen, and to compare the image quality and lesion conspicuity among the reconstruction strength lev...
Alberta Stroke Program Early CT Score Calculation Using the Deep Learning-Based Brain Hemisphere Comparison Algorithm.
Journal of stroke and cerebrovascular diseases : the official journal of National Stroke Association
Apr 18, 2021
OBJECTIVES: The Alberta Stroke Program Early Computed Tomography Score (ASPECTS) is a promising tool for the evaluation of stroke expansion to determine suitability for reperfusion therapy. The aim of this study was to validate deep learning-based AS...
Predictive Value of Tests
Thrombolytic Therapy
Radiographic Image Interpretation, Computer-Assisted
Stroke
Patient Selection
Aged, 80 and over
Male
Deep Learning
Imaging, Three-Dimensional
Thrombectomy
Humans
Prognosis
Retrospective Studies
Female
Reproducibility of Results
Aged
Tomography, X-Ray Computed
Clinical Decision-Making
Automated detection of pneumonia cases using deep transfer learning with paediatric chest X-ray images.
The British journal of radiology
Apr 16, 2021
OBJECTIVE: Pneumonia is a lung infection and causes the inflammation of the small air sacs (Alveoli) in one or both lungs. Proper and faster diagnosis of pneumonia at an early stage is imperative for optimal patient care. Currently, chest X-ray is co...
"Bad News, You're Older Than Suspected": Can Deep Learning Estimate Biological Age From Chest Radiographs?
JACC. Cardiovascular imaging
Apr 14, 2021
Can perihaematomal radiomics features predict haematoma expansion?
Clinical radiology
Apr 13, 2021
AIM: To evaluate the association between perihaematomal radiomics features and haematoma expansion (HE).
Real-World Experience with Artificial Intelligence-Based Triage in Transferred Large Vessel Occlusion Stroke Patients.
Cerebrovascular diseases (Basel, Switzerland)
Apr 13, 2021
BACKGROUND AND PURPOSE: Randomized controlled trials have demonstrated the importance of time to endovascular therapy (EVT) in clinical outcomes in large vessel occlusion (LVO) acute ischemic stroke. Delays to treatment are particularly prevalent whe...
Middle Aged
Endovascular Procedures
Humans
Diagnosis, Computer-Assisted
Computed Tomography Angiography
Predictive Value of Tests
Databases, Factual
Ischemic Stroke
Cerebral Angiography
Radiographic Image Interpretation, Computer-Assisted
Triage
Artificial Intelligence
Retrospective Studies
Delivery of Health Care, Integrated
Workflow
Female
Decision Support Techniques
Time-to-Treatment
Clinical Decision-Making
Aged
Aged, 80 and over
Male
Real-time coronary artery stenosis detection based on modern neural networks.
Scientific reports
Apr 7, 2021
Invasive coronary angiography remains the gold standard for diagnosing coronary artery disease, which may be complicated by both, patient-specific anatomy and image quality. Deep learning techniques aimed at detecting coronary artery stenoses may fac...
Detecting the Early Infarct Core on Non-Contrast CT Images with a Deep Learning Residual Network.
Journal of stroke and cerebrovascular diseases : the official journal of National Stroke Association
Mar 27, 2021
PURPOSE: To explore a new approach mainly based on deep learning residual network (ResNet) to detect infarct cores on non-contrast CT images and improve the accuracy of acute ischemic stroke diagnosis.
Comparison of machine learning methods for prediction of osteoradionecrosis incidence in patients with head and neck cancer.
The British journal of radiology
Mar 18, 2021
OBJECTIVES: Mandible osteoradionecrosis (ORN) is one of the most severe toxicities in patients with head and neck cancer (HNC) undergoing radiotherapy (RT). The existing literature focuses on the correlation of mandible ORN and clinical and dosimetri...
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