AIMC Topic: Adult

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Enhanced Human Crawling Phase Recognition Based on Kinematic Synergies and Machine Learning.

Journal of biomechanical engineering
Hands-and-knees crawling, an effective rehabilitation method for children with motor impairments, requires precise phase detection for optimizing assistive devices. However, research on phase detection in human crawling remains limited. The research ...

Effect of cooking and food serving robot design images and information on consumer liking, willingness to try food, and emotional responses.

Food research international (Ottawa, Ont.)
The utilization of robots in the food industry, including restaurants and cafés, has increased in recent years. This study investigated participants' responses to robots in the serving and cooking domains, which require varying degrees of consumer in...

Nomograms versus artificial intelligence platforms: which one can better predict sentinel node positivity in melanoma patients?

Melanoma research
Nomograms are commonly used in oncology to assist clinicians in individualized decision-making processes, such as considering sentinel node biopsy (SNB) for melanoma patients. Concurrently, artificial intelligence (AI) is increasingly being utilized ...

From Guidelines to Intelligence: How AI Refines Thyroid Nodule Biopsy Decisions.

Ultrasound in medicine & biology
OBJECTIVE: To evaluate the value of combining American College of Radiology (ACR) Thyroid Imaging Reporting and Data System (TI-RADS) with the Demetics ultrasound diagnostic system in reducing the rate of fine-needle aspiration (FNA) biopsies for thy...

Discriminating Clear Cell From Non-Clear Cell Renal Cell Carcinoma: A Machine Learning Approach Using Contrast-enhanced Ultrasound Radiomics.

Ultrasound in medicine & biology
OBJECTIVE: The aim of this investigation is to assess the clinical usefulness of a machine learning model using contrast-enhanced ultrasound (CEUS) radiomics in discriminating clear cell renal cell carcinoma (ccRCC) from non-ccRCC.

CLABpredICU---AI-driven risk prediction for CLABSI in intensive care units based on clinical and biochemical parameters.

American journal of infection control
BACKGROUND: Central line--associated bloodstream infections (CLABSI) are major causes of morbidity and mortality in intensive care units. This study aimed to develop an artificial intelligence-driven predictive model for CLABSI within 2 calendar days...

Machine-learning modeL based on computed tomography body composition analysis for the estimation of resting energy expenditure: A pilot study.

Clinical nutrition ESPEN
BACKGROUND & AIMS: The assessment of resting energy expenditure (REE) is a challenging task with the current existing methods. The reference method, indirect calorimetry (IC), is not widely available, and other surrogates, such as equations and bioim...

Anatomical Considerations for Achieving Optimized Outcomes in Individualized Cochlear Implantation.

Otology & neurotology : official publication of the American Otological Society, American Neurotology Society [and] European Academy of Otology and Neurotology
HYPOTHESIS: Machine learning models can assist with the selection of electrode arrays required for optimal insertion angles.

Clinical validation of a proposed diagnostic classification for pulpitis.

International endodontic journal
AIM: Determine the reliability and clinical validity of the Wolters classification of pulpitis.

From resting-state functional hippocampal centrality to functional outcome: An extended neurocognitive model of psychosis.

Psychiatry research
BACKGROUND: We previously proposed a neurocognitive model of psychosis in which reduced morphometric hippocampal-cortical connectivity precedes impaired episodic memory, social cognition, negative symptoms, and functional outcome. We provided support...