AIMC Topic: Humans

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A deep learning model to enhance lung cancer detection using 'Dual-Branch' model classification approach.

PloS one
Cancer remains a life-threatening global challenge, with lung cancer ranking among the most devastating forms, impacting millions annually. Early detection and accurate classification are essential for improving patient survival rates, and computed t...

Dynamic grasping system based on visual algorithm and robot arm collaboration in logistics production line.

PloS one
In response to the urgent need for efficient and accurate dynamic grasping in automated logistics, this study proposes the VRCDS, a dynamic grasping system that integrates a multi-feature weighted PnP vision algorithm with multi-robot arm collaborati...

Establishment of an amino acid metabolism related signature for prognostic and therapeutic sensitivity prediction in breast cancer by machine learning.

PloS one
Amino acid metabolism plays a critical role in tumor growth and immune regulation, yet its comprehensive function in breast cancer remains underexplored. We developed an amino acid metabolism-related gene signature (AAMRGS) to predict prognosis and t...

Machine translationese of large language models: Dependency triplets, text classification, and SHAP analysis.

PloS one
This study addresses the challenge of distinguishing human translations from those generated by Large Language Models (LLMs) by utilizing dependency triplet features and evaluating 16 machine learning classifiers. Using 10-fold cross-validation, the ...

SARS-CoV-2 peptide fragments selectively dysregulate specific immune cell populations via Gaussian curvature targeting.

Proceedings of the National Academy of Sciences of the United States of America
Immune cell populations are dysregulated in COVID-19 for currently unknown reasons: Plasmacytoid dendritic cell (pDC) populations are reduced, thus hampering antiviral responses. CD8 T cell populations are reduced, the level of which has emerged as a...

Selective classification with machine learning uncertainty estimates improves ACS prediction: a retrospective study in the prehospital setting.

Scientific reports
Accurate identification of acute coronary syndrome (ACS) in the prehospital setting is important for timely treatments that reduce damage to the compromised myocardium. Current machine learning approaches lack sufficient performance to safely rule-in...

AI-enabled electrocardiogram alert for potassium imbalance treatment: a pragmatic randomized controlled trial.

Nature communications
Life-threatening dyskalemia, defined as an abnormal serum potassium concentration, is common in emergency settings that requires timely recognition and treatment and can be detected via AI-enabled electrocardiography. We conducted a pragmatic, open-l...

Sex-specific machine learning models for carotid plaque prediction in individuals with fatty liver disease: a cross-sectional study.

BMJ open
INTRODUCTION: Early detection of carotid plaque prevents stroke and myocardial infarction. Individuals with fatty liver might be at an increased risk of developing carotid plaque, yet limited access to carotid artery ultrasound underscores the need f...

Data-driven queueing modelling: a simulation case study of emergency department crowding.

BMJ health & care informatics
OBJECTIVES: Emergency department crowding refers to a complex state of congestion associated with a set of performance indicators such as occupation levels, waiting times and specific scores. Among current methods to model it, an objective gap exists...

Technologies, Clinical Applications, and Implementation Barriers of Digital Twins in Precision Cardiology: Systematic Review.

JMIR cardio
BACKGROUND: Digital twin systems are emerging as promising tools in precision cardiology, enabling dynamic, patient-specific simulations to support diagnosis, risk assessment, and treatment planning. However, the current landscape of cardiovascular d...