AIMC Topic: Radiographic Image Interpretation, Computer-Assisted

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Hematoma expansion prediction in intracerebral hemorrhage patients by using synthesized CT images in an end-to-end deep learning framework.

Computerized medical imaging and graphics : the official journal of the Computerized Medical Imaging Society Sep 5, 2024
Spontaneous intracerebral hemorrhage (ICH) is a type of stroke less prevalent than ischemic stroke but associated with high mortality rates. Hematoma expansion (HE) is an increase in the bleeding that affects 30%-38% of hemorrhagic stroke patients. I...
Humans Tomography, X-Ray Computed Male Deep Learning Cerebral Hemorrhage Radiographic Image Interpretation, Computer-Assisted Hematoma
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Reconstruction of patient-specific confounders in AI-based radiologic image interpretation using generative pretraining.

Cell reports. Medicine Sep 5, 2024
Reliably detecting potentially misleading patterns in automated diagnostic assistance systems, such as those powered by artificial intelligence (AI), is crucial for instilling user trust and ensuring reliability. Current techniques fall short in visu...
Radiography, Thoracic Adult Male Humans Middle Aged Image Processing, Computer-Assisted Radiographic Image Interpretation, Computer-Assisted Artificial Intelligence Algorithms Female Reproducibility of Results Aged
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Combining Multistaged Filters and Modified Segmentation Network for Improving Lung Nodules Classification.

IEEE journal of biomedical and health informatics Sep 5, 2024
Advancements in computational technology have led to a shift towards automated detection processes in lung cancer screening, particularly through nodule segmentation techniques. These techniques employ thresholding to distinguish between soft and fir...
Algorithms Tomography, X-Ray Computed Lung Lung Neoplasms Solitary Pulmonary Nodule Radiographic Image Interpretation, Computer-Assisted Humans Neural Networks, Computer
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Artificial intelligence assisted automatic screening of opportunistic osteoporosis in computed tomography images from different scanners.

European radiology Sep 4, 2024
OBJECTIVES: It is feasible to evaluate bone mineral density (BMD) and detect osteoporosis through an artificial intelligence (AI)-assisted system by using quantitative computed tomography (QCT) as a reference without additional radiation exposure or ...
Osteoporosis Tomography, X-Ray Computed Female Aged Aged, 80 and over Humans Artificial Intelligence Bone Density Radiographic Image Interpretation, Computer-Assisted Radiation Dosage Adult Sensitivity and Specificity Male Middle Aged Deep Learning
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Volume Measurements for Surveillance after Endovascular Aneurysm Repair using Artificial Intelligence.

European journal of vascular and endovascular surgery : the official journal of the European Society for Vascular Surgery Sep 3, 2024
OBJECTIVE: Surveillance after endovascular aneurysm repair (EVAR) is suboptimal due to limited compliance and relatively large variability in measurement methods of abdominal aortic aneurysm (AAA) sac size after treatment. Measuring volume offers a m...
Endovascular Aneurysm Repair Male Treatment Outcome Predictive Value of Tests Aortic Aneurysm, Abdominal Radiographic Image Interpretation, Computer-Assisted Blood Vessel Prosthesis Implantation Endovascular Procedures Retrospective Studies Computed Tomography Angiography Humans Female Aortography Artificial Intelligence Reproducibility of Results Software Aged Aged, 80 and over
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Fully automated epicardial adipose tissue volume quantification with deep learning and relationship with CAC score and micro/macrovascular complications in people living with type 2 diabetes: the multicenter EPIDIAB study.

Cardiovascular diabetology Sep 3, 2024
BACKGROUND: The aim of this study (EPIDIAB) was to assess the relationship between epicardial adipose tissue (EAT) and the micro and macrovascular complications (MVC) of type 2 diabetes (T2D).
Humans Prognosis Female Pericardium Epicardial Adipose Tissue Predictive Value of Tests Coronary Angiography Radiographic Image Interpretation, Computer-Assisted Middle Aged Coronary Artery Disease Risk Assessment Reproducibility of Results Adiposity Deep Learning Automation Risk Factors Diabetes Mellitus, Type 2 Diabetic Angiopathies Aged Adipose Tissue Vascular Calcification Male Computed Tomography Angiography
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Deep Location Soft-Embedding-Based Network With Regional Scoring for Mammogram Classification.

IEEE transactions on medical imaging Sep 3, 2024
Early detection and treatment of breast cancer can significantly reduce patient mortality, and mammogram is an effective method for early screening. Computer-aided diagnosis (CAD) of mammography based on deep learning can assist radiologists in makin...
Breast Neoplasms Deep Learning Mammography Radiographic Image Interpretation, Computer-Assisted Breast Humans Algorithms Databases, Factual Female
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Mammography classification with multi-view deep learning techniques: Investigating graph and transformer-based architectures.

Medical image analysis Sep 2, 2024
The potential and promise of deep learning systems to provide an independent assessment and relieve radiologists' burden in screening mammography have been recognized in several studies. However, the low cancer prevalence, the need to process high-re...
Breast Neoplasms Deep Learning Mammography Radiographic Image Interpretation, Computer-Assisted Humans Neural Networks, Computer Female
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Hybrid clinical-radiomics model based on fully automatic segmentation for predicting the early expansion of spontaneous intracerebral hemorrhage: A multi-center study.

Journal of stroke and cerebrovascular diseases : the official journal of National Stroke Association Aug 31, 2024
BACKGROUND: Early prediction of hematoma expansion (HE) is important for the development of therapeutic strategies for spontaneous intracerebral hemorrhage (sICH). Radiomics can help to predict early hematoma expansion in intracerebral hemorrhage. Ho...
Hematoma Automation Aged, 80 and over Nomograms Radiomics Humans Cerebral Hemorrhage Male Risk Assessment Tomography, X-Ray Computed Decision Support Techniques Middle Aged Predictive Value of Tests Disease Progression Female Prognosis Radiographic Image Interpretation, Computer-Assisted Deep Learning Reproducibility of Results Retrospective Studies Risk Factors China Aged Time Factors
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Machine Learning Based on Clinical Information and Integrated CT Radiomics to Predict Local Recurrence of Stage Ia Lung Adenocarcinoma after Microwave Ablation.

Journal of vascular and interventional radiology : JVIR Aug 28, 2024
PURPOSE: To develop and compare 3 different machine learning-based models of clinical information and integrated radiomics features predicting the local recurrence of Stage Ia lung adenocarcinoma after microwave ablation (MWA) for assisting clinical ...
Humans Machine Learning Decision Support Techniques Margins of Excision Predictive Value of Tests Risk Assessment Risk Factors Adenocarcinoma of Lung Neoplasm Staging Time Factors Aged Ablation Techniques Radiographic Image Interpretation, Computer-Assisted Lung Neoplasms Radiomics Neoplasm Recurrence, Local Treatment Outcome Male Microwaves Retrospective Studies Middle Aged Tomography, X-Ray Computed Clinical Decision-Making Female
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