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.
PURPOSE OF REVIEW: While MESS has historically influenced limb salvage versus amputation decisions, its universal applicability remains uncertain. With trauma systems expanding and advancements in trauma care, the need for a nuanced understanding of ...
To evaluate the diagnostic performance of our deep learning (DL) model of COVID-19 and investigate whether the diagnostic performance of radiologists was improved by referring to our model. Our datasets contained chest X-rays (CXRs) for the following...
Orifice reduction strategies for da Vinci robotic surgery have been a hot topic of research in recent years. We retrospectively analyzed the perioperative outcomes of robotic-assisted thoracoscopic surgery (RATS) with two, three, and four-hole approa...
BACKGROUND: Because chest CT scan has largely supplanted surgical lung biopsy for diagnosing most cases of interstitial lung disease (ILD), tools to standardize CT scan interpretation are urgently needed.
Multivisceral robotic surgery may be an alternative to sequential procedures in select patients with colorectal cancer who are diagnosed with synchronous lesions or in those who require additional procedures at the time of resection. The aim of this ...
Machine learning tools have demonstrated viability in visualizing pain accurately using vital sign data; however, it remains uncertain whether incorporating individual patient baselines could enhance accuracy. This study aimed to investigate improvin...
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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