AIMC Topic: Tomography, X-Ray Computed
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Prospective Evaluation of Artificial Intelligence Triage of Intracranial Hemorrhage on Noncontrast Head CT Examinations.
AJR. American journal of roentgenology
Sep 4, 2024
Retrospective studies evaluating artificial intelligence (AI) algorithms for intracranial hemorrhage (ICH) detection on noncontrast CT (NCCT) have shown promising results but lack prospective validation. The purpose of this article was to evaluate ...
Computer-aided diagnosis for lung cancer using waterwheel plant algorithm with deep learning.
Scientific reports
Sep 4, 2024
Lung cancer (LC) is a life-threatening and dangerous disease all over the world. However, earlier diagnoses and treatment can save lives. Earlier diagnoses of malevolent cells in the lungs responsible for oxygenating the human body and expelling carb...
Real-time patient-specific-dose in CT through use of artificial intelligence.
Journal of radiological protection : official journal of the Society for Radiological Protection
Sep 4, 2024
The Effectiveness of Artificial Intelligence-based Pedicle Screw Trajectory Planning in Patients With Different Levels of Bone Mineral Density.
Clinical spine surgery
Sep 3, 2024
STUDY DESIGN: Retrospective cohort study.
A Scoping Review of Machine-Learning Derived Radiomic Analysis of CT and PET Imaging to Investigate Atherosclerotic Cardiovascular Disease.
Tomography (Ann Arbor, Mich.)
Sep 3, 2024
BACKGROUND: Cardiovascular disease affects the carotid arteries, coronary arteries, aorta and the peripheral arteries. Radiomics involves the extraction of quantitative data from imaging features that are imperceptible to the eye. Radiomics analysis ...
Non-invasive multimodal CT deep learning biomarker to predict pathological complete response of non-small cell lung cancer following neoadjuvant immunochemotherapy: a multicenter study.
Journal for immunotherapy of cancer
Sep 3, 2024
OBJECTIVES: Although neoadjuvant immunochemotherapy has been widely applied in non-small cell lung cancer (NSCLC), predicting treatment response remains a challenge. We used pretreatment multimodal CT to explore deep learning-based immunochemotherapy...
PCNet: Prior Category Network for CT Universal Segmentation Model.
IEEE transactions on medical imaging
Sep 3, 2024
Accurate segmentation of anatomical structures in Computed Tomography (CT) images is crucial for clinical diagnosis, treatment planning, and disease monitoring. The present deep learning segmentation methods are hindered by factors such as data scale...
Better Rough Than Scarce: Proximal Femur Fracture Segmentation With Rough Annotations.
IEEE transactions on medical imaging
Sep 3, 2024
Proximal femoral fracture segmentation in computed tomography (CT) is essential in the preoperative planning of orthopedic surgeons. Recently, numerous deep learning-based approaches have been proposed for segmenting various structures within CT scan...
Machine learning-assisted diagnosis of parotid tumor by using contrast-enhanced CT imaging features.
Journal of stomatology, oral and maxillofacial surgery
Sep 2, 2024
PURPOSE: This study aims to develop a machine learning diagnostic model for parotid gland tumors based on preoperative contrast-enhanced CT imaging features to assist in clinical decision-making.
Comprehensive Clinical Usability-Oriented Contour Quality Evaluation for Deep Learning Auto-segmentation: Combining Multiple Quantitative Metrics Through Machine Learning.
Practical radiation oncology
Sep 2, 2024
PURPOSE: The current commonly used metrics for evaluating the quality of auto-segmented contours have limitations and do not always reflect the clinical usefulness of the contours. This work aims to develop a novel contour quality classification (CQC...
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