AIMC Topic: Humans

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Disentangled deep learning method for interior tomographic reconstruction of low-dose x-ray CT.

Physics in medicine and biology
. Low-dose interior tomography integrates low-dose CT (LDCT) with region-of-interest (ROI) imaging which finds wide application in radiation dose reduction and high-resolution imaging. However, the combined effects of noise and data truncation pose g...

Development of a prostate cancer biochemical recurrence risk signature using machine learning and motor protein-related genes.

PloS one
BACKGROUND: Motor proteins play significant roles in cancer progression, but their involvement in biochemical recurrence (BCR) of prostate cancer remains unclear. The objective of the study is to develop a prognostic indicator for BCR using machine l...

A deep learning approach to gender equality: Forecasting educational indicators with 1D-CNN aligned with SDG 5.

PloS one
Sustainable development goal (SDG) 5 focuses on gender equality and empowerment and it is considered as one of the most important SDGs. Therefore, this article presented a time series prediction model that predicts gender-related educational results ...

Enhanced gallbladder cancer detection via active and self-supervised learning integration: Innovating B-ultrasound image analysis.

PloS one
Gallbladder cancer, a common yet often under diagnosed malignancy, is typically characterized by late detection and a poor prognosis. The rise of deep learning has introduced new methods for its early identification through B-ultrasound imaging, but ...

Cutting-edge nanobiosensors: Revolutionizing cancer diagnosis and enabling precision medicine.

Clinica chimica acta; international journal of clinical chemistry
Integrating nanotechnology and artificial intelligence (AI) revolutionizes cancer diagnostics, propelling precision medicine into a transformative era. This review examines state-of-the-art nanobiosensors capable of detecting critical biomarkers-circ...

Enhanced epileptic seizure detection using CNNs with convolutional block attention and short-term memory networks.

Behavioural brain research
Analyzing the electroencephalography (EEG) signals of epilepsy patients can monitor the condition, detect and intervene in epileptic seizures in time. To enhance the lives of these patients, it is necessary to develop accurate methods to detect epile...

Nordihydroguaiaretic acid (NDGA) exhibits potent anti-biofilm and antimicrobial activity against methicillin-resistant Staphylococcus aureus and Candida albicans.

Phytomedicine : international journal of phytotherapy and phytopharmacology
INTRODUCTION: The global rise of antimicrobial resistance (AMR), particularly among biofilm-forming bacteria and fungi, has created an urgent need for novel therapeutics. Nordihydroguaiaretic acid (NDGA), a plant-derived polyphenolic lignan, has demo...

Advantages of fully automated AI-enhanced algorithm (5D CNS+™) for generating a fetal neurosonogram in clinical routine.

Journal of perinatal medicine
OBJECTIVES: The objective was to demonstrate superiority of a fully vs. semi-automated approach (5D CNS+™) and to verify operators could handle and benefit from a fully automated rendering volumetric datasets to generate a complete fetal neurosonogra...

Multimodal MRI analysis selecting key brain features for machine learning based classification of diabetic neuropathic pain and phenotypes.

Journal of the neurological sciences
Cerebral alterations are associated with diabetic peripheral neuropathy (DPN) and neuropathic pain, including reductions in brain volumes, cortical thickness, sulcus depth, and alterations in metabolites and functional connectivity. This study combin...

Machine learning-based evaluation of risk factors for carbapenem-resistant dissemination in neonatal units.

mSystems
Healthcare-associated infections (HAIs), particularly in neonatal intensive care units (NICUs), pose significant challenges due to neonates' vulnerability and the rapid infection spread. However, risk factors facilitating pathogen persistence and dis...