AIMC Topic: Humans

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3-1-3 Weight averaging technique-based performance evaluation of deep neural networks for Alzheimer's disease detection using structural MRI.

Biomedical physics & engineering express
Alzheimer's disease (AD) is a progressive neurological disorder. It is identified by the gradual shrinkage of the brain and the loss of brain cells. This leads to cognitive decline and impaired social functioning, making it a major contributor to dem...

Learnable real-time inference of molecular composition from diffuse spectroscopy of brain tissue.

Journal of biomedical optics
SIGNIFICANCE: Diffuse optical modalities such as broadband near-infrared spectroscopy (bNIRS) and hyperspectral imaging (HSI) represent a promising alternative for low-cost, non-invasive, and fast monitoring of living tissue. Particularly, the possib...

The legal regulation of artificial intelligence in the European Union: A practical guide for radiologists.

Radiologia
The European Union is taking the lead globally on the regulation of Artificial Intelligence (AI) and developing important legislation, namely the AI Act. The purpose of this article is to describe this regulation and examine three implications that w...

Robust identification key predictors of short- and long-term weight status in children and adolescents by machine learning.

Frontiers in public health
BACKGROUND: Early identification of high-risk individuals for weight problems in children and adolescents is crucial for implementing timely preventive measures. While machine learning (ML) techniques have shown promise in addressing this complex cha...

Machine learning-based discovery of UPP1 as a key oncogene in tumorigenesis and immune escape in gliomas.

Frontiers in immunology
INTRODUCTION: Gliomas are the most common and aggressive type of primary brain tumor, with a poor prognosis despite current treatment approaches. Understanding the molecular mechanisms underlying glioma development and progression is critical for imp...

Novel prognostic signature for hepatocellular carcinoma using a comprehensive machine learning framework to predict prognosis and guide treatment.

Frontiers in immunology
BACKGROUND: Hepatocellular carcinoma (HCC) is highly aggressive, with delayed diagnosis, poor prognosis, and a lack of comprehensive and accurate prognostic models to assist clinicians. This study aimed to construct an HCC prognosis-related gene sign...

Early diagnosis of Alzheimer's Disease based on multi-attention mechanism.

PloS one
Alzheimer's Disease is a neurodegenerative disorder, and one of its common and prominent early symptoms is language impairment. Therefore, early diagnosis of Alzheimer's Disease through speech and text information is of significant importance. Howeve...

Analysis of influencing factors of traffic accidents on urban ring road based on the SVM model optimized by Bayesian method.

PloS one
Based on small scale sample of accident data from specific scenarios, fully exploring the potential influencing factors of the severity of traffic accidents has become a key and effective research method. In order to analyze the factors mentioned abo...

A Robust Deep Learning Method with Uncertainty Estimation for the Pathological Classification of Renal Cell Carcinoma Based on CT Images.

Journal of imaging informatics in medicine
This study developed and validated a deep learning-based diagnostic model with uncertainty estimation to aid radiologists in the preoperative differentiation of pathological subtypes of renal cell carcinoma (RCC) based on computed tomography (CT) ima...