AIMC Topic: Humans

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CMINNs: Compartment model informed neural networks - Unlocking drug dynamics.

Computers in biology and medicine
In the field of pharmacokinetics and pharmacodynamics (PKPD) modeling, which plays a pivotal role in the drug development process, traditional models frequently encounter difficulties in fully encapsulating the complexities of drug absorption, distri...

Accurate Spatial Heterogeneity Dissection and Gene Regulation Interpretation for Spatial Transcriptomics using Dual Graph Contrastive Learning.

Advanced science (Weinheim, Baden-Wurttemberg, Germany)
Recent advances in spatial transcriptomics have enabled simultaneous preservation of high-throughput gene expression profiles and the spatial context, enabling high-resolution exploration of distinct regional characterization in tissue. To effectivel...

The role of artificial intelligence in immune checkpoint inhibitor research: A bibliometric analysis.

Human vaccines & immunotherapeutics
Immune checkpoint inhibitors (ICIs) are revolutionizing cancer treatment, and Artificial Intelligence (AI) is a key player in this field. A comprehensive analysis of AI's impact on these inhibitors was lacking, but this study addresses that by analyz...

The Role and Future of Artificial Intelligence in Robotic Image-Guided Interventions.

Techniques in vascular and interventional radiology
Artificial intelligence and robotics are transforming interventional radiology, driven by advancements in computer vision, robotics and procedural automation. Historically focused on diagnostics, AI now also enhances procedural capabilities in IR, en...

Rapid diagnosis of latent and active pulmonary tuberculosis by autofluorescence spectroscopy of blood plasma combined with artificial neural network algorithm.

Photodiagnosis and photodynamic therapy
The existing clinical diagnostic methods of pulmonary tuberculosis (TB) usually have some of the following limitations, such as time-consuming, invasive, radioactive, insufficiently sensitive and accurate. This study demonstrates the possibility of u...

Deep learning for early diagnosis of oral cancer via smartphone and DSLR image analysis: a systematic review.

Expert review of medical devices
INTRODUCTION: Diagnosing oral cancer is crucial in healthcare, with technological advancements enhancing early detection and outcomes. This review examines the impact of handheld AI-based tools, focusing on Convolutional Neural Networks (CNNs) and th...

Severity prediction markers in dengue: a prospective cohort study using machine learning approach.

Biomarkers : biochemical indicators of exposure, response, and susceptibility to chemicals
BACKGROUND: Dengue virus causes illnesses with or without warning indicators for severe complications. There are no clear prognostic signs linked to the disease outcomes.

STANet: A Novel Spatio-Temporal Aggregation Network for Depression Classification with Small and Unbalanced FMRI Data.

Tomography (Ann Arbor, Mich.)
: Early diagnosis of depression is crucial for effective treatment and suicide prevention. Traditional methods rely on self-report questionnaires and clinical assessments, lacking objective biomarkers. Combining functional magnetic resonance imaging ...

MACNet: A Multidimensional Attention-Based Convolutional Neural Network for Lower-Limb Motor Imagery Classification.

Sensors (Basel, Switzerland)
Decoding lower-limb motor imagery (MI) is highly important in brain-computer interfaces (BCIs) and rehabilitation engineering. However, it is challenging to classify lower-limb MI from electroencephalogram (EEG) signals, because lower-limb motions (L...

Research on Multi-Scale Spatio-Temporal Graph Convolutional Human Behavior Recognition Method Incorporating Multi-Granularity Features.

Sensors (Basel, Switzerland)
Aiming at the problem that the existing human skeleton behavior recognition methods are insensitive to human local movements and show inaccurate recognition in distinguishing similar behaviors, a multi-scale spatio-temporal graph convolution method i...