AIMC Topic: Cross-Sectional Studies
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Early prediction of body composition parameters on metabolically unhealthy in the Chinese population via advanced machine learning.
Frontiers in endocrinology
Aug 29, 2023
BACKGROUND: Metabolic syndrome (Mets) is considered a global epidemic of the 21st century, predisposing to cardiometabolic diseases. This study aims to describe and compare the body composition profiles between metabolic healthy (MH) and metabolic un...
Re-investigation of functional gastrointestinal disorders utilizing a machine learning approach.
BMC medical informatics and decision making
Aug 26, 2023
BACKGROUND: Functional gastrointestinal disorders (FGIDs), as a group of syndromes with no identified structural or pathophysiological biomarkers, are currently classified by Rome criteria based on gastrointestinal symptoms (GI). However, the high ov...
Applicability of machine learning technique in the screening of patients with mild traumatic brain injury.
PloS one
Aug 24, 2023
Even though the demand of head computed tomography (CT) in patients with mild traumatic brain injury (TBI) has progressively increased worldwide, only a small number of individuals have intracranial lesions that require neurosurgical intervention. As...
Norwegian radiologists' expectations of artificial intelligence in mammographic screening - A cross-sectional survey.
European journal of radiology
Aug 23, 2023
PURPOSE: To explore Norwegian breast radiologists' expectations of adding artificial intelligence (AI) in the interpretation procedure of screening mammograms.
Artificial intelligence chatbot performance in triage of ophthalmic conditions.
Canadian journal of ophthalmology. Journal canadien d'ophtalmologie
Aug 9, 2023
BACKGROUND: Timely access to human expertise for affordable and efficient triage of ophthalmic conditions is inconsistent. With recent advancements in publicly available artificial intelligence (AI) chatbots, the lay public may turn to these tools fo...
The knowledge, experience, and attitude on artificial intelligence-assisted cephalometric analysis: Survey of orthodontists and orthodontic students.
American journal of orthodontics and dentofacial orthopedics : official publication of the American Association of Orthodontists, its constituent societies, and the American Board of Orthodontics
Aug 9, 2023
INTRODUCTION: Artificial intelligence (AI) developed rapidly in orthodontics, and AI-based cephalometric applications have been adopted. This study aimed to assess AI-assisted cephalometric technologies related knowledge, experience, and attitude amo...
Human's moral judgements towards different social actors: A cross-sectional study.
The British journal of developmental psychology
Aug 8, 2023
The proliferation of artificial intelligence may pose new challenges to people's moral judgements. We examined moral judgements towards different social actors and their influencing factors in children, adolescents and adults. Moral judgements were m...
The Effects of Stakeholder Perceptions on the Use of Humanoid Robots in Care for Older Adults: Postinteraction Cross-Sectional Study.
Journal of medical Internet research
Aug 4, 2023
BACKGROUND: Efficient use of humanoid social robots in the care for older adults requires precise knowledge of expectations in this area. There is little research in this field that includes the interaction of stakeholders with the robot. Even fewer ...
Views about perceived training needs of health care professionals in relation to socially assistive robots: an international online survey.
Contemporary nurse
Aug 4, 2023
BACKGROUND: As Artificial Intelligence and social robots are increasingly used in health and social care, it is imperative to explore the training needs of the workforce, factoring in their cultural background.
A deep learning approach to investigate the filtration bleb functionality after glaucoma surgery: a preliminary study.
Graefe's archive for clinical and experimental ophthalmology = Albrecht von Graefes Archiv fur klinische und experimentelle Ophthalmologie
Aug 2, 2023
PURPOSE: To distinguish functioning from failed filtration blebs (FBs) implementing a deep learning (DL) model on slit-lamp images.
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