AIMC Topic: Feasibility Studies

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Prediction of lead (Pb) adsorption on attapulgite clay using the feasibility of data intelligence models.

Environmental science and pollution research international
This study investigates the performance of support vector machine (SVM), multivariate adaptive regression spline (MARS), and random forest (RF) models for predicting the lead (Pb) adsorption by attapulgite clay. Models are constructed using batch sto...

Feasibility of the use of deep learning classification of teat-end condition in Holstein cattle.

Journal of dairy science
Infections with pathogenic bacteria entering the mammary gland through the teat canal are the most common cause of mastitis in dairy cows; therefore, sustaining the integrity of the teat canal and its adjacent tissues is critical to resist infection....

Feasibility of machine learning methods for predicting hospital emergency room visits for respiratory diseases.

Environmental science and pollution research international
The prediction of hospital emergency room visits (ERV) for respiratory diseases after the outbreak of PM is of great importance in terms of public health, medical resource allocation, and policy decision support. Recently, the machine learning method...

Deep learning for the fully automated segmentation of the inner ear on MRI.

Scientific reports
Segmentation of anatomical structures is valuable in a variety of tasks, including 3D visualization, surgical planning, and quantitative image analysis. Manual segmentation is time-consuming and deals with intra and inter-observer variability. To dev...

Artificial intelligence in sepsis early prediction and diagnosis using unstructured data in healthcare.

Nature communications
Sepsis is a leading cause of death in hospitals. Early prediction and diagnosis of sepsis, which is critical in reducing mortality, is challenging as many of its signs and symptoms are similar to other less critical conditions. We develop an artifici...

Feasibility study to improve deep learning in OCT diagnosis of rare retinal diseases with few-shot classification.

Medical & biological engineering & computing
Deep learning (DL) has been successfully applied to the diagnosis of ophthalmic diseases. However, rare diseases are commonly neglected due to insufficient data. Here, we demonstrate that few-shot learning (FSL) using a generative adversarial network...

Deep Learning Approach to Parse Eligibility Criteria in Dietary Supplements Clinical Trials Following OMOP Common Data Model.

AMIA ... Annual Symposium proceedings. AMIA Symposium
Dietary supplements (DSs) have been widely used in the U.S. and evaluated in clinical trials as potential interventions for various diseases. However, many clinical trials face challenges in recruiting enough eligible patients in a timely fashion, ca...

Feasibility of a deep learning-based method for automated localization of pelvic floor landmarks using stress MR images.

International urogynecology journal
INTRODUCTION AND HYPOTHESIS: Magnetic resonance imaging (MRI) plays an important role in assessing pelvic organ prolapse (POP), and automated pelvic floor landmark localization potentially accelerates MRI-based measurements of POP. Herein, we aimed t...

The 3D reconstructed skin micronucleus assay using imaging flow cytometry and deep learning: A proof-of-principle investigation.

Mutation research. Genetic toxicology and environmental mutagenesis
The reconstructed skin micronucleus (RSMN) assay was developed in 2006, as an in vitro alternative for genotoxicity evaluation of dermally applied chemicals or products. In the years since, significant progress has been made in the optimization of th...