AIMC Topic: Humans

Clear Filters Showing 26891 to 26900 of 95995 articles

Targeted metabolomics combined with machine learning to identify and validate new biomarkers for early SLE diagnosis and disease activity.

Clinical immunology (Orlando, Fla.)
BACKGROUND: The early diagnosis of systemic lupus erythematosus (SLE) and the assessment of disease activity progression remain a great challenge. Targeted metabolomics has great potential to identify new biomarkers of SLE.

An extensive review on lung cancer therapeutics using machine learning techniques: state-of-the-art and perspectives.

Journal of drug targeting
There are over 100 types of human cancer, accounting for millions of deaths every year. Lung cancer alone claims over 1.8 million lives per year and is expected to surpass 3.2 million by 2050, which underscores the urgent need for rapid drug developm...

Explaining deep learning for ECG analysis: Building blocks for auditing and knowledge discovery.

Computers in biology and medicine
Deep neural networks have become increasingly popular for analyzing ECG data because of their ability to accurately identify cardiac conditions and hidden clinical factors. However, the lack of transparency due to the black box nature of these models...

Recent advancements and applications of deep learning in heart failure: Α systematic review.

Computers in biology and medicine
BACKGROUND: Heart failure (HF), a global health challenge, requires innovative diagnostic and management approaches. The rapid evolution of deep learning (DL) in healthcare necessitates a comprehensive review to evaluate these developments and their ...

The role and future prospects of artificial intelligence algorithms in peptide drug development.

Biomedicine & pharmacotherapy = Biomedecine & pharmacotherapie
Peptide medications have been more well-known in recent years due to their many benefits, including low side effects, high biological activity, specificity, effectiveness, and so on. Over 100 peptide medications have been introduced to the market to ...

Deep learning generation of preclinical positron emission tomography (PET) images from low-count PET with task-based performance assessment.

Medical physics
BACKGROUND: Preclinical low-count positron emission tomography (LC-PET) imaging offers numerous advantages such as facilitating imaging logistics, enabling longitudinal studies of long- and short-lived isotopes as well as increasing scanner throughpu...

Multimodal radiotherapy dose prediction using a multi-task deep learning model.

Medical physics
BACKGROUND: In radiation therapy (RT), accelerated partial breast irradiation (APBI) has emerged as an increasingly preferred treatment modality over conventional whole breast irradiation due to its targeted dose delivery and shorter course of treatm...

Improving Adversarial Robustness of ECG Classification Based on Lipschitz Constraints and Channel Activation Suppression.

Sensors (Basel, Switzerland)
Deep neural networks (DNNs) are increasingly important in the medical diagnosis of electrocardiogram (ECG) signals. However, research has shown that DNNs are highly vulnerable to adversarial examples, which can be created by carefully crafted perturb...

Enhancing Fetal Electrocardiogram Signal Extraction Accuracy through a CycleGAN Utilizing Combined CNN-BiLSTM Architecture.

Sensors (Basel, Switzerland)
The fetal electrocardiogram (FECG) records changes in the graph of fetal cardiac action potential during conduction, reflecting the developmental status of the fetus in utero and its physiological cardiac activity. Morphological alterations in the FE...