AIMC Topic: Adult

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Using machine learning models to predict falls in hospitalised adults.

International journal of medical informatics
BACKGROUND: Identifying patients at high risk of falling is crucial in implementing effective fall prevention programs. While the integration of information systems is becoming more widespread in the healthcare industry, it poses a significant challe...

Machine learning models help differentiate between causes of recurrent spontaneous vertigo.

Journal of neurology
BACKGROUND: Vestibular migraine (VM) and Menière's disease (MD) are two common causes of recurrent spontaneous vertigo. Using history, video-nystagmography and audiovestibular tests, we developed machine learning models to separate these two disorder...

Artificial intelligence algorithm accurately assesses oestrogen receptor immunohistochemistry in metastatic breast cancer cytology specimens: A pilot study.

Cytopathology : official journal of the British Society for Clinical Cytology
OBJECTIVE: The Visiopharm artificial intelligence (AI) algorithm for oestrogen receptor (ER) immunohistochemistry (IHC) in whole slide images (WSIs) has been successfully validated in surgical pathology. This study aimed to assess its efficacy in cyt...

Using multivariate pattern analysis to increase effect sizes for event-related potential analyses.

Psychophysiology
Multivariate pattern analysis (MVPA) approaches can be applied to the topographic distribution of event-related potential (ERP) signals to "decode" subtly different stimulus classes, such as different faces or different orientations. These approaches...

Deep learning reconstruction for high-resolution computed tomography images of the temporal bone: comparison with hybrid iterative reconstruction.

Neuroradiology
PURPOSE: We investigated whether the quality of high-resolution computed tomography (CT) images of the temporal bone improves with deep learning reconstruction (DLR) compared with hybrid iterative reconstruction (HIR).

Machine Learning Analysis Using RNA Sequencing to Distinguish Neuromyelitis Optica from Multiple Sclerosis and Identify Therapeutic Candidates.

The Journal of molecular diagnostics : JMD
This study aims to identify RNA biomarkers distinguishing neuromyelitis optica (NMO) from relapsing-remitting multiple sclerosis (RRMS) and explore potential therapeutic applications leveraging machine learning (ML). An ensemble approach was develope...

A machine learning stacking model accurately estimating gastric fluid volume in patients undergoing elective sedated gastrointestinal endoscopy.

Postgraduate medicine
BACKGROUND: The current point-of-care ultrasound (POCUS) assessment of gastric fluid volume primarily relies on the traditional linear approach, which often suffers from moderate accuracy. This study aimed to develop an advanced machine learning (ML)...

A motion-corrected deep-learning reconstruction framework for accelerating whole-heart magnetic resonance imaging in patients with congenital heart disease.

Journal of cardiovascular magnetic resonance : official journal of the Society for Cardiovascular Magnetic Resonance
BACKGROUND: Cardiovascular magnetic resonance (CMR) is an important imaging modality for the assessment and management of adult patients with congenital heart disease (CHD). However, conventional techniques for three-dimensional (3D) whole-heart acqu...

Comparison of Conventional Anesthesia Nurse Education and an Artificial Intelligence Chatbot (ChatGPT) Intervention on Preoperative Anxiety: A Randomized Controlled Trial.

Journal of perianesthesia nursing : official journal of the American Society of PeriAnesthesia Nurses
PURPOSE: This study aimed to evaluate the effects of an artificial intelligence (AI) chatbot (ChatGPT-3.5, OpenAI) on preoperative anxiety reduction and patient satisfaction in adult patients undergoing surgery under general anesthesia.