AIMC Topic: Humans

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Construction of the prediction model for multiple myeloma based on machine learning.

International journal of laboratory hematology
INTRODUCTION: The global burden of multiple myeloma (MM) is increasing every year. Here, we have developed machine learning models to provide a reference for the early detection of MM.

Artificial Intelligence in Rhinology.

Otolaryngologic clinics of North America
Rhinology, allergy, and skull base surgery are fields primed for the integration and implementation of artificial intelligence (AI). The heterogeneity of the disease processes within these fields highlights the opportunity for AI to augment clinical ...

Management of orofacial clefts in times of artificial intelligence: advances and challenges.

European archives of paediatric dentistry : official journal of the European Academy of Paediatric Dentistry

Artificial intelligence (AI) implementation within the National Health Service (NHS): the South West London AI Working Group experience.

Clinical radiology
In the rapidly evolving field of artificial intelligence (AI) for radiology, with a plethora of vendor options and use-cases and evidence claims to sift through, the pressing question is how to effectively implement the right tool for enhanced patien...

Graph convolutional network with attention mechanism improve major depressive depression diagnosis based on plasma biomarkers and neuroimaging data.

Journal of affective disorders
BACKGROUND: The absence of clinically-validated biomarkers or objective protocols hinders effective major depressive disorder (MDD) diagnosis. Compared to healthy control (HC), MDD exhibits anomalies in plasma protein levels and neuroimaging presenta...

Deep Learning-Based Segmentation and Risk Stratification for Gastrointestinal Stromal Tumors in Transabdominal Ultrasound Imaging.

Journal of ultrasound in medicine : official journal of the American Institute of Ultrasound in Medicine
PURPOSE: To develop a deep neural network system for the automatic segmentation and risk stratification prediction of gastrointestinal stromal tumors (GISTs).

Optimizing large language models in digestive disease: strategies and challenges to improve clinical outcomes.

Liver international : official journal of the International Association for the Study of the Liver
Large Language Models (LLMs) are transformer-based neural networks with billions of parameters trained on very large text corpora from diverse sources. LLMs have the potential to improve healthcare due to their capability to parse complex concepts an...

Fully Automatic Quantitative Measurement of Equilibrium Radionuclide Angiocardiography Using a Convolutional Neural Network.

Clinical nuclear medicine
PURPOSE: The aim of this study was to generate deep learning-based regions of interest (ROIs) from equilibrium radionuclide angiography datasets for left ventricular ejection fraction (LVEF) measurement.

HD-Former: A hierarchical dependency Transformer for medical image segmentation.

Computers in biology and medicine
Medical image segmentation is a compelling fundamental problem and an important auxiliary tool for clinical applications. Recently, the Transformer model has emerged as a valuable tool for addressing the limitations of convolutional neural networks b...

No code machine learning: validating the approach on use-case for classifying clavicle fractures.

Clinical imaging
PURPOSE: We created an infrastructure for no code machine learning (NML) platform for non-programming physicians to create NML model. We tested the platform by creating an NML model for classifying radiographs for the presence and absence of clavicle...