AIMC Topic: Humans

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Artificial intelligence and smile design: An e-Delphi consensus statement of ethical challenges.

Journal of prosthodontics : official journal of the American College of Prosthodontists
PURPOSE: Smile design software increasingly relies on artificial intelligence (AI). However, using AI for smile design raises numerous technical and ethical concerns. This study aimed to evaluate these ethical issues.

Progression from Prediabetes to Diabetes in a Diverse U.S. Population: A Machine Learning Model.

Diabetes technology & therapeutics
To date, there are no widely implemented machine learning (ML) models that predict progression from prediabetes to diabetes. Addressing this knowledge gap would aid in identifying at-risk patients within this heterogeneous population who may benefit...

Application of deep learning and radiomics in the prediction of hematoma expansion in intracerebral hemorrhage: a fully automated hybrid approach.

Diagnostic and interventional radiology (Ankara, Turkey)
PURPOSE: Spontaneous intracerebral hemorrhage (ICH) is the most severe form of stroke. The timely assessment of early hematoma enlargement and its proper treatment are of great significance in curbing the deterioration and improving the prognosis of ...

MLMFNet: A multi-level modality fusion network for multi-modal accelerated MRI reconstruction.

Magnetic resonance imaging
Magnetic resonance imaging produces detailed anatomical and physiological images of the human body that can be used in the clinical diagnosis and treatment of diseases. However, MRI suffers its comparatively longer acquisition time than other imaging...

Multi-modal medical image fusion using improved dual-channel PCNN.

Medical & biological engineering & computing
This paper proposes a medical image fusion method in the non-subsampled shearlet transform (NSST) domain to combine a gray-scale image with the respective pseudo-color image obtained through different imaging modalities. The proposed method applies a...

FPGA-based fast bin-ratio spiking ensemble network for radioisotope identification.

Neural networks : the official journal of the International Neural Network Society
In this work, we demonstrate the training, conversion, and implementation flow of an FPGA-based bin-ratio ensemble spiking neural network applied for radioisotope identification. The combination of techniques including learned step quantisation (LSQ)...

Exploiting protein language models for the precise classification of ion channels and ion transporters.

Proteins
This study introduces TooT-PLM-ionCT, a comprehensive framework that consolidates three distinct systems, each meticulously tailored for one of the following tasks: distinguishing ion channels (ICs) from membrane proteins (MPs), segregating ion trans...

Identification of common mechanisms and biomarkers of atrial fibrillation and heart failure based on machine learning.

ESC heart failure
AIMS: Atrial fibrillation (AF) is the most common arrhythmia. Heart failure (HF) is a disease caused by heart dysfunction. The prevalence of AF and HF were progressively increasing over time. The co-existence of AF and HF presents a significant thera...

Prospective prediction of anxiety onset in the Canadian longitudinal study on aging (CLSA): A machine learning study.

Journal of affective disorders
BACKGROUND: Anxiety disorders are among the most common mental health disorders in the middle aged and older population. Because older individuals are more likely to have multiple comorbidities or increased frailty, the impact of anxiety disorders on...