AIMC Topic: Humans

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Artificial intelligence in medical education: a cross-sectional needs assessment.

BMC medical education
BACKGROUND: As the information age wanes, enabling the prevalence of the artificial intelligence age; expectations, responsibilities, and job definitions need to be redefined for those who provide services in healthcare. This study examined the perce...

Utilizing machine learning algorithms to predict subject genetic mutation class from in silico models of neuronal networks.

BMC medical informatics and decision making
BACKGROUND: Epilepsy is the fourth-most common neurological disorder, affecting an estimated 50 million patients globally. Nearly 40% of patients have uncontrolled seizures yet incur 80% of the cost. Anti-epileptic drugs commonly result in resistance...

Deep learning-based framework for slide-based histopathological image analysis.

Scientific reports
Digital pathology coupled with advanced machine learning (e.g., deep learning) has been changing the paradigm of whole-slide histopathological images (WSIs) analysis. Major applications in digital pathology using machine learning include automatic ca...

Machine learning for morbid glomerular hypertrophy.

Scientific reports
A practical research method integrating data-driven machine learning with conventional model-driven statistics is sought after in medicine. Although glomerular hypertrophy (or a large renal corpuscle) on renal biopsy has pathophysiological implicatio...

Establishment of a model for predicting the outcome of induced labor in full-term pregnancy based on machine learning algorithm.

Scientific reports
To evaluate and establish a prediction model of the outcome of induced labor based on machine learning algorithm. This was a cross-sectional design. The subjects were divided into primipara and multipara, and the risk factors for the outcomes of indu...

A formal validation of a deep learning-based automated workflow for the interpretation of the echocardiogram.

Nature communications
This study compares a deep learning interpretation of 23 echocardiographic parameters-including cardiac volumes, ejection fraction, and Doppler measurements-with three repeated measurements by core lab sonographers. The primary outcome metric, the in...

Historical evolution and new trends for soil-intruder interaction modeling.

Bioinspiration & biomimetics
Soil is a crucial resource for life on Earth. Every activity, whether natural or man-made, that interacts with the sub or deep soil can affect the land at large scales (e.g. geological risks). Understanding such interactions can help identify more su...

Ferrobotic swarms enable accessible and adaptable automated viral testing.

Nature
Expanding our global testing capacity is critical to preventing and containing pandemics. Accordingly, accessible and adaptable automated platforms that in decentralized settings perform nucleic acid amplification tests resource-efficiently are requi...

Prediction of anemia using facial images and deep learning technology in the emergency department.

Frontiers in public health
BACKGROUND: According to the WHO, anemia is a highly prevalent disease, especially for patients in the emergency department. The pathophysiological mechanism by which anemia can affect facial characteristics, such as membrane pallor, has been proven ...

Federated Learning Optimization Algorithm for Automatic Weight Optimal.

Computational intelligence and neuroscience
Federated learning (FL), a distributed machine-learning framework, is poised to effectively protect data privacy and security, and it also has been widely applied in variety of fields in recent years. However, the system heterogeneity and statistical...