AIMC Topic: Humans

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CUAMT: A MRI semi-supervised medical image segmentation framework based on contextual information and mixed uncertainty.

Computer methods and programs in biomedicine
BACKGROUND AND OBJECTIVE: Semi-supervised medical image segmentation is a class of machine learning paradigms for segmentation model training and inference using both labeled and unlabeled medical images, which can effectively reduce the data labelin...

Biological age prediction in schizophrenia using brain MRI, gut microbiome and blood data.

Brain research bulletin
The study of biological age prediction using various biological data has been widely explored. However, single biological data may offer limited insights into the pathological process of aging and diseases. Here we evaluated the performance of machin...

ExpoPath: A method for identifying and annotating exposure pathways from chemical co-occurrence networks.

The Science of the total environment
Improving risk evaluation for environmental and human health is of paramount concern for the U.S. Environmental Protection Agency (EPA). This includes the identification and assessment of chemical transport from commercial and industrial sources to e...

Machine Learning-Enhanced Optimization for High-Throughput Precision in Cellular Droplet Bioprinting.

Advanced science (Weinheim, Baden-Wurttemberg, Germany)
Organoids produce through traditional manual pipetting methods face challenges such as labor-intensive procedures and batch-to-batch variability in quality. To ensure consistent organoid production, 3D bioprinting platforms offer a more efficient alt...

Predictive modeling of response to repetitive transcranial magnetic stimulation in treatment-resistant depression.

Translational psychiatry
Identifying predictors of treatment response to repetitive transcranial magnetic stimulation (rTMS) remain elusive in treatment-resistant depression (TRD). Leveraging electronic medical records (EMR), this retrospective cohort study applied supervise...

The use of a convolutional neural network to automate radiologic scoring of computed tomography of paranasal sinuses.

Biomedical engineering online
BACKGROUND: Chronic rhinosinusitis (CRS) is diagnosed with symptoms and objective endoscopy or computed tomography (CT). The Lund-Mackay score (LMS) is often used to determine the radiologic severity of CRS and make clinical decisions. This proof-of-...

Potential for near-term AI risks to evolve into existential threats in healthcare.

BMJ health & care informatics
The recent emergence of foundation model-based chatbots, such as ChatGPT (OpenAI, San Francisco, CA, USA), has showcased remarkable language mastery and intuitive comprehension capabilities. Despite significant efforts to identify and address the nea...

Predicting 30-day survival after in-hospital cardiac arrest: a nationwide cohort study using machine learning and SHAP analysis.

BMJ open
OBJECTIVE: In-hospital cardiac arrest (IHCA) presents a critical challenge with low survival rates and limited prediction tools. Despite advances in resuscitation, predicting 30-day survival remains difficult, and current methods lack interpretabilit...

The role of artificial intelligence in the prediction, identification, diagnosis and treatment of perinatal depression and anxiety among women in LMICs: a systematic review protocol.

BMJ open
INTRODUCTION: Perinatal depression and anxiety (PDA) is associated with a high risk of maternal mortality. Existing data shows that 95% of maternal mortality in low- and middle-income countries (LMICs) is due to resource constraints and negligence in...

Machine learning analysis of FOSL2 and RHoBTB1 as central immunological regulators in knee osteoarthritis synovium.

The Journal of international medical research
BackgroundKnee osteoarthritis is a debilitating disease with a complex pathogenesis. Synovitis, which refers to inflammation of the synovial membrane surrounding the joint, is believed to play an important role in the development and progression of k...