Lymphoma

Using Artificial Intelligence to Detect COVID-19 and Community-acquired Pneumonia Based on Pulmonary CT: Evaluation of the Diagnostic Accuracy.

Background Coronavirus disease 2019 (COVID-19) has widely spread all over the world since the beginning of 2020. It is desirable to develop automatic and accurate detection of COVID-19 using chest CT. Purpose To develop a fully automatic framework to detect COVID-19 using chest CT and evaluate its performance. Materials and Methods In this retrospective and multicenter study, a deep learning model...

Artificial Intelligence based Models for Screening of Hematologic Malignancies using Cell Population Data.

Cell Population Data (CPD) provides various blood cell parameters that can be used for differential diagnosis. Data analytics using Machine Learning (ML) have been playing a pivotal role in revolutionizing medical diagnostics. This research presents a novel approach of using ML algorithms for screening hematologic malignancies using CPD. The data collection was done at Konkuk University Medical Ce...

Climate-induced thermoregulatory responses in a non-linear thermal environment: investigating the inter-dependencies using a facile artificial neural network-based predictive strategy.

. Given the burgeoning impacts of climatic variability on human health, suitable computational paradigms are used to explore the subsequent ergonomic ...

Addressing database variability in learning from medical data: An ensemble-based approach using convolutional neural networks and a case of study applied to automatic sleep scoring.

In this work we examine some of the problems associated with the development of machine learning models with the objective to achieve robust generaliz...

Deep learning-based attenuation correction in the absence of structural information for whole-body positron emission tomography imaging.

Deriving accurate structural maps for attenuation correction (AC) of whole-body positron emission tomography (PET) remains challenging. Common problem...

Machine learning methods for microbiome studies.

Researches on the microbiome have been actively conducted worldwide and the results have shown human gut bacterial environment significantly impacts o...

Modeling and optimization of imidacloprid degradation by catalytic percarbonate oxidation using artificial neural network and Box-Behnken experimental design.

Due to its toxicity and persistence, pesticide pollution poses a serious threat to human health and the environment. Imidacloprid or IMD is an archety...

Radiogenomic Models Using Machine Learning Techniques to Predict EGFR Mutations in Non-Small Cell Lung Cancer.

BACKGROUND: The purpose of this study was to build radiogenomics models from texture signatures derived from computed tomography (CT) and F-FDG PET-CT...

Improving genomic prediction accuracy for meat tenderness in Nellore cattle using artificial neural networks.

The goal of this study was to compare the predictive performance of artificial neural networks (ANNs) with Bayesian ridge regression, Bayesian Lasso, ...

Radial basis function artificial neural network able to accurately predict disinfection by-product levels in tap water: Taking haloacetic acids as a case study.

Control of risks caused by disinfection by-products (DBPs) requires pre-knowledge of their levels in drinking water. In this study, a radial basis fun...

Machine learning derived input-function in a dynamic F-FDG PET study of mice.

Tracer kinetic modelling, based on dynamic F-fluorodeoxyglucose (FDG) positron emission tomography (PET) is used to quantify glucose metabolism in hum...

Machine learning as new promising technique for selection of significant features in obese women with type 2 diabetes.

Background The global trend of obesity and diabetes is considerable. Recently, the early diagnosis and accurate prediction of type 2 diabetes mellitus...

Quantification of interfacial energies associated with membrane fouling in a membrane bioreactor by using BP and GRNN artificial neural networks.

Interfacial energy between sludge foulants and rough membrane surface critically determines adhesive fouling in membrane bioreactors (MBRs). As a curr...

Using transfer learning from prior reference knowledge to improve the clustering of single-cell RNA-Seq data.

In many research areas scientists are interested in clustering objects within small datasets while making use of prior knowledge from large reference ...

Extreme learning machine for a new hybrid morphological/linear perceptron.

Morphological neural networks (MNNs) can be characterized as a class of artificial neural networks that perform an operation of mathematical morpholog...

Classifying T cell activity in autofluorescence intensity images with convolutional neural networks.

The importance of T cells in immunotherapy has motivated developing technologies to improve therapeutic efficacy. One objective is assessing antigen-i...

Radiomics allows for detection of benign and malignant histopathology in patients with metastatic testicular germ cell tumors prior to post-chemotherapy retroperitoneal lymph node dissection.

OBJECTIVES: To evaluate whether a computed tomography (CT) radiomics-based machine learning classifier can predict histopathology of lymph nodes (LNs)...

Deep learning segmentation of orbital fat to calibrate conventional MRI for longitudinal studies.

In conventional non-quantitative magnetic resonance imaging, image contrast is consistent within images, but absolute intensity can vary arbitrarily b...

Computational analysis of non-invasive deep brain stimulation based on interfering electric fields.

Neuromodulation modalities are used as effective treatments for some brain disorders. Non-invasive deep brain stimulation (NDBS) via temporally interf...

Dissection of gene expression datasets into clinically relevant interaction signatures via high-dimensional correlation maximization.

Gene expression is controlled by many simultaneous interactions, frequently measured collectively in biology and medicine by high-throughput technolog...