Journal of exposure science & environmental epidemiology
Oct 17, 2023
BACKGROUND: Understanding lung deposition dose of black carbon is critical to fully reconcile epidemiological evidence of combustion particles induced health effects and inform the development of air quality metrics concerning black carbon. Macrophag...
BACKGROUND: Frontoorbital distraction osteogenesis (FODO) is an established surgical technique for patients with unicoronal craniosynostosis. The authors' institution has used an endoscope-assisted technique (endo-FODO) in recent years to decrease cu...
BACKGROUND: With the increasing legalization and popularity of marijuana, it is frequently and sometimes unintentionally combined with nicotine-containing products. As a consequence, patients may fail to accurately report usage during preoperative ex...
BACKGROUND: In breast augmentation surgery, selection of the appropriate breast implant size is a crucial step that can greatly affect patient satisfaction and the outcome of the procedure. However, this decision is often based on the subjective judg...
OBJECTIVES: To design a deep learning-based framework for automatic segmentation and detection of intracranial aneurysms (IAs) on magnetic resonance T1 images and test the robustness and performance of framework.
In recent years, increasing efforts have been made to develop advanced techniques that could predict the potential of implantation of each single embryo and prioritize the transfer of those at higher chance. The most promising include non-invasive pr...
The human voice is an essential communication tool, but various disorders and habits can disrupt it. Diagnosis of pathological and abnormal voices is very important. Conventional diagnosis of these voice pathologies can be invasive and costly. Voice ...
PURPOSE: To assess the diagnostic performance of two chatbots, ChatGPT and Glass, in uveitis diagnosis compared to renowned uveitis specialists, and evaluate clinicians' perception about utilizing artificial intelligence (AI) in ophthalmology practic...
The purpose of this study was to develop a fully automated and reliable volumetry of the cerebellum of children during infancy and childhood using deep learning algorithms in comparison to manual segmentation. In addition, the clinical usefulness of ...
AIM: To investigate the effect of deep learning on the diagnostic performance of radiologists and radiology residents in detecting breast cancers on computed tomography (CT).
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