AIMC Topic: Adult

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Accelerated CEST imaging through deep learning quantification from reduced frequency offsets.

Magnetic resonance in medicine
PURPOSE: To shorten CEST acquisition time by leveraging Z-spectrum undersampling combined with deep learning for CEST map construction from undersampled Z-spectra.

Automatic generation of diffusion tensor imaging for the lumbar nerve using convolutional neural networks.

Magnetic resonance imaging
【PURPOSE】: Diffusion Tensor Imaging (DTI) with tractography is useful for the functional diagnosis of degenerative lumbar disorders. However, it is not widely used in clinical settings due to time and health care provider costs, as it is performed ma...

Integrating StEP-COMPAC definition and enhanced recovery after surgery status in a machine-learning-based model for postoperative pulmonary complications in laparoscopic hepatectomy.

Anaesthesia, critical care & pain medicine
BACKGROUND: Postoperative pulmonary complications (PPCs) contribute to high mortality rates and impose significant financial burdens. In this study, a machine learning-based prediction model was developed to identify patients at high risk of developi...

Perceived Usefulness of Robotic Technology for Patient Fall Prevention.

Workplace health & safety
BACKGROUND: Technology has the potential to prevent patient falls in healthcare settings and to reduce work-related injuries among healthcare providers. However, the usefulness and acceptability of each technology requires careful evaluation. Framed ...

Latent Trajectories of Cerebral Perfusion Pressure and Risk Prediction Models Among Patients with Traumatic Brain Injury: Based on an Interpretable Artificial Neural Network.

World neurosurgery
OBJECTIVE: This study aimed to characterize long-term cerebral perfusion pressure (CPP) trajectory in traumatic brain injury (TBI) patients and construct an interpretable prediction model to assess the risk of unfavorable CPP evolution patterns.

Perceptions and expectations of an artificially intelligent physical activity digital assistant - A focus group study.

Applied psychology. Health and well-being
Artificially intelligent physical activity digital assistants that use the full spectrum of machine learning capabilities have not yet been developed and examined. This study aimed to explore potential users' perceptions and expectations of using suc...

Advanced Non-linear Modeling and Explainable Artificial Intelligence Techniques for Predicting 30-Day Complications in Bariatric Surgery: A Single-Center Study.

Obesity surgery
PURPOSE: Metabolic bariatric surgery (MBS) became integral to managing severe obesity. Understanding surgical risks associated with MBS is crucial. Different scores, such as the Metabolic and Bariatric Surgery Accreditation and Quality Improvement Pr...

Construction of a molecular diagnostic system for neurogenic rosacea by combining transcriptome sequencing and machine learning.

BMC medical genomics
Patients with neurogenic rosacea (NR) frequently demonstrate pronounced neurological manifestations, often unresponsive to conventional therapeutic approaches. A molecular-level understanding and diagnosis of this patient cohort could significantly g...

Engagement analysis of a persuasive-design-optimized eHealth intervention through machine learning.

Scientific reports
The challenge of sustaining user engagement in eHealth interventions is a pressing issue with significant implications for the effectiveness of these digital health tools. This study investigates user engagement in a cognitive-behavioral therapy-base...

Automatic 3D pelvimetry framework in CT images and its validation.

Scientific reports
In the field of spinal pathology, sagittal balance of the spine is usually judged by the spatial structure and morphology of pelvis, which can be represented by pelvic parameters. Pelvic parameters, including pelvic incidence, pelvic tilt and sacral ...