In this review, we address the issue of fairness in the clinical integration of artificial
intelligence (AI) in the medical field. As the clinical adoption of deep learning algorithms, a …

Importance. With the booming growth of artificial intelligence (AI), especially the recent
advancements of deep learning, utilizing advanced deep learning-based methods for …

Intracranial aneurysm is a common life-threatening disease. Computed tomography
angiography is recommended as the standard diagnosis tool; yet, interpretation can be time …

Purpose: To test whether our proposed denoising approach with deep learning-based
reconstruction (dDLR) can effectively denoise brain MR images. Methods: In an initial …

Importance Deep learning has the potential to augment clinician performance in medical
imaging interpretation and reduce time to diagnosis through automated segmentation. Few …

Neurosurgeons receive extensive and lengthy training to equip themselves with various
technical skills, and neurosurgery require a great deal of pre-, intra-and postoperative …

Background Cerebral aneurysm detection is a challenging task. Deep learning may become
a supportive tool for more accurate interpretation. Purpose To develop a highly sensitive …

Optimizing the CT acquisition parameters to obtain diagnostic image quality at the lowest
possible radiation dose is crucial in the radiosensitive pediatric population. The image …

Background Deep learning models have the potential for lung cancer prognostication, but
model output as an independent prognostic factor must be validated with clinical risk factors …

Background Subarachnoid hemorrhage from cerebral aneurysm rupture is a major cause of
morbidity and mortality. Early aneurysm identification, aided by automated systems, may …