AIMC Topic: Machine Learning

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Estimation of foveal avascular zone area from a B-scan OCT image using machine learning algorithms.

PloS one
PURPOSE: The objective of this study is to estimate the area of the Foveal Avascular Zone (FAZ) from B-scan OCT images using machine learning algorithms.

Dynamic graph consistency and self-contrast learning for semi-supervised medical image segmentation.

Neural networks : the official journal of the International Neural Network Society
Semi-supervised medical image segmentation endeavors to exploit a limited set of labeled data in conjunction with a substantial corpus of unlabeled data, with the aim of training models that can match or even exceed the efficacy of fully supervised s...

Endocrine disruptor identification and multitoxicity level assessment of organic chemicals: An example of multiple machine learning models.

Journal of hazardous materials
Endocrine-disrupting chemicals (EDCs) pollution is a major global environmental issue. Assessing the multiple toxic effects of EDCs is key to managing their risks. This study successfully developed an EDCs classification and recognition model based o...

Machine learning and multi-omics characterization of SLC2A1 as a prognostic factor in hepatocellular carcinoma: SLC2A1 is a prognostic factor in HCC.

Gene
Hepatocellular carcinoma (HCC) is characterized by high incidence, significant mortality, and marked heterogeneity, making accurate molecular subtyping essential for effective treatment. Using multi-omics data from HCC patients, we applied diverse cl...

Are we ready to integrate advanced artificial intelligence models in clinical laboratory?

Biochemia medica
The application of advanced artificial intelligence (AI) models and algorithms in clinical laboratories is a new inevitable stage of development of laboratory medicine, since in the future, diagnostic and prognostic panels specific to certain disease...

Labyrinthine Wrinkle-Patterned Fiber Sensors Based on a 3D Stress Complementary Strategy for Machine Learning-Enabled Medical Monitoring and Action Recognition.

Small (Weinheim an der Bergstrasse, Germany)
Fiber strain sensors show good application potential in the field of wearable smart fabrics and equipment because of their characteristics of easy deformation and weaving. However, the integration of fiber strain sensors with sensitive response, good...

Utility of an Echocardiographic Machine Learning Model to Predict Outcomes in Intensive Cardiac Care Unit Patients.

Journal of the American Society of Echocardiography : official publication of the American Society of Echocardiography
INTRODUCTION: The risk stratification at admission to the intensive cardiac care unit (ICCU) is crucial and remains challenging.

Enhanced iodinated disinfection byproducts formation in iodide/iodate-containing water undergoing UV-chloramine sequential disinfection: Machine learning-aided identification of reaction mechanisms.

Water research
Restricted to the complex nature of dissolved organic matter (DOM) in various aquatic environments, the mechanisms of enhanced iodinated disinfection byproducts (I-DBPs) formation in water containing both I and IO (designated as I/IO in this study) d...

Ensemble learning-assisted quantitative identifying influencing factors of cadmium and arsenic concentration in rice grain based multiplexed data.

Journal of hazardous materials
Rapid and accurate prediction of rice Cd (rCd) and rice As (rAs) bioaccumulation are important for assessing the safe utilization of rice. Currently, there is lack of comprehensive and systematic exploration of the factors of rCd and rAs. Herein, ens...

Predicting the adsorption of ammonia nitrogen by biochar in water bodies using machine learning strategies: Model optimization and analysis of key characteristic variables.

Environmental research
Biochar adsorption technology has been widely used to remove ammonia nitrogen from water bodies. However, existing methods for predicting adsorption efficiency often lack sufficient accuracy and practical usability. This study evaluated eight machine...