AI Medical Compendium Topic

Explore the latest research on artificial intelligence and machine learning in medicine.

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Boosting wisdom of the crowd for medical image annotation using training performance and task features.

Cognitive research: principles and implications
A crucial bottleneck in medical artificial intelligence (AI) is high-quality labeled medical datasets. In this paper, we test a large variety of wisdom of the crowd algorithms to label medical images that were initially classified by individuals recr...

Machine learning-based classification of petrofacies in fine laminated limestones.

Anais da Academia Brasileira de Ciencias
Characterization and development of hydrocarbon reservoirs depends on the classification of lithological patterns from well log data. In thin reservoir units, limited vertical data impedes the efficient classification of lithologies. We present a tes...

AI-based disease category prediction model using symptoms from low-resource Ethiopian language: Afaan Oromo text.

Scientific reports
Automated disease diagnosis and prediction, powered by AI, play a crucial role in enabling medical professionals to deliver effective care to patients. While such predictive tools have been extensively explored in resource-rich languages like English...

Evaluating distributed-learning on real-world obstetrics data: comparing distributed, centralized and local models.

Scientific reports
This study focused on comparing distributed learning models with centralized and local models, assessing their efficacy in predicting specific delivery and patient-related outcomes in obstetrics using real-world data. The predictions focus on key mom...

Enhancing genome-wide populus trait prediction through deep convolutional neural networks.

The Plant journal : for cell and molecular biology
As a promising model, genome-based plant breeding has greatly promoted the improvement of agronomic traits. Traditional methods typically adopt linear regression models with clear assumptions, neither obtaining the linkage between phenotype and genot...

Omics data classification using constitutive artificial neural network optimized with single candidate optimizer.

Network (Bristol, England)
Recent technical advancements enable omics-based biological study of molecules with very high throughput and low cost, such as genomic, proteomic, and microbionics'. To overcome this drawback, Omics Data Classification using Constitutive Artificial N...

A fully autonomous robotic ultrasound system for thyroid scanning.

Nature communications
The current thyroid ultrasound relies heavily on the experience and skills of the sonographer and the expertise of the radiologist, and the process is physically and cognitively exhausting. In this paper, we report a fully autonomous robotic ultrasou...

Development and application of machine learning models for prediction of soil available cadmium based on soil properties and climate features.

Environmental pollution (Barking, Essex : 1987)
Identifying the key influencing factors in soil available cadmium (Cd) is crucial for preventing the Cd accumulation in the food chain. However, current experimental methods and traditional prediction models for assessing available Cd are time-consum...

Improving sepsis classification performance with artificial intelligence algorithms: A comprehensive overview of healthcare applications.

Journal of critical care
PURPOSE: This study investigates the potential of machine learning (ML) algorithms in improving sepsis diagnosis and prediction, focusing on their relevance in healthcare decision-making. The primary objective is to contribute to healthcare decision-...

Application of machine learning for antibiotic resistance in water and wastewater: A systematic review.

Chemosphere
Antibiotic resistance (AR) is considered one of the greatest global threats in the current century, which can only be overcome if all interconnected areas of humans, animals and the environment are taken into account as part of the One Health concept...