Although knee measurements yield high classification rates in metric sex estimation, there is a paucity of studies exploring the knee in artificial intelligence-based sexing. This proof-of-concept study aimed to develop deep learning algorithms for s...
Tuberculosis (TB) continues to be a leading cause of death in children despite global efforts focused on early diagnosis and interventions to limit the spread of the disease. This challenge has been made more complex in the context of the coronavirus...
Under the black-box nature in the deep learning model, it is uncertain how the change in contrast level and format affects the performance. We aimed to investigate the effect of contrast level and image format on the effectiveness of deep learning fo...
BACKGROUND: Chest radiographs are routinely performed in intensive care unit (ICU) to confirm the correct position of an endotracheal tube (ETT) relative to the carina. However, their interpretation is often challenging and requires substantial time ...
Artificial intelligence (AI)-generated clinical advice is becoming more prevalent in healthcare. However, the impact of AI-generated advice on physicians' decision-making is underexplored. In this study, physicians received X-rays with correct diagno...
OBJECTIVE: The aim of this study is to evaluate whether fully automatic cephalometric analysis software with artificial intelligence algorithms is as accurate as non-automated cephalometric analysis software for clinical diagnosis and research.
AIM: To understand the attitudes of UK radiology trainees towards artificial intelligence (AI) in Radiology, in particular, assessing the demand for AI education.
Background Patients presenting to the emergency department (ED) with acute chest pain (ACP) syndrome undergo additional testing to exclude acute coronary syndrome (ACS), pulmonary embolism (PE), or aortic dissection (AD), often yielding negative resu...
BACKGROUND: It is difficult for orthodontists to accurately predict the growth trend of the mandible in children with anterior crossbite. This study aims to develop a deep learning model to automatically predict the mandibular growth result into norm...
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