COVID-19

Joint Detection of Tap and CEA Based on Deep Learning Medical Image Segmentation: Risk Prediction of Thyroid Cancer.

In recent years, the incidence of thyroid nodules has shown an increasing trend year by year and has become one of the important diseases that endanger human health. Ultrasound medical images based on deep learning are widely used in clinical diagnosis due to their cheapness, no radiation, and low cost. The use of image processing technology to accurately segment the nodule area provides important...

Toward understanding COVID-19 pneumonia: a deep-learning-based approach for severity analysis and monitoring the disease.

We report a new approach using artificial intelligence (AI) to study and classify the severity of COVID-19 using 1208 chest X-rays (CXRs) of 396 COVID-19 patients obtained through the course of the disease at Emory Healthcare affiliated hospitals (Atlanta, GA, USA). Using a two-stage transfer learning technique to train a convolutional neural network (CNN), we show that the algorithm is able to cl...

Predictive modeling for peri-implantitis by using machine learning techniques.

The purpose of this retrospective cohort study was to create a model for predicting the onset of peri-implantitis by using machine learning methods an...

Mini-COVIDNet: Efficient Lightweight Deep Neural Network for Ultrasound Based Point-of-Care Detection of COVID-19.

Lung ultrasound (US) imaging has the potential to be an effective point-of-care test for detection of COVID-19, due to its ease of operation with mini...

Detecting adaptive introgression in human evolution using convolutional neural networks.

Studies in a variety of species have shown evidence for positively selected variants introduced into a population via introgression from another, dist...

Improvement of Neoantigen Identification Through Convolution Neural Network.

Accurate prediction of neoantigens and the subsequent elicited protective anti-tumor response are particularly important for the development of cancer...

Clinical risk prediction models and informative cluster size: Assessing the performance of a suicide risk prediction algorithm.

Clinical visit data are clustered within people, which complicates prediction modeling. Cluster size is often informative because people receiving mor...

Predictive Approaches for Acute Dialysis Requirement and Death in COVID-19.

BACKGROUND AND OBJECTIVES: AKI treated with dialysis initiation is a common complication of coronavirus disease 2019 (COVID-19) among hospitalized pat...

COVID-19 diagnosis by routine blood tests using machine learning.

Physicians taking care of patients with COVID-19 have described different changes in routine blood parameters. However, these changes hinder them from...

Predicting Prolonged Hospitalization and Supplemental Oxygenation in Patients with COVID-19 Infection from Ambulatory Chest Radiographs using Deep Learning.

RATIONALE AND OBJECTIVES: The clinical prognosis of outpatients with coronavirus disease 2019 (COVID-19) remains difficult to predict, with outcomes i...

Deep learning the structural determinants of protein biochemical properties by comparing structural ensembles with DiffNets.

Understanding the structural determinants of a protein's biochemical properties, such as activity and stability, is a major challenge in biology and m...

Benchmarking deep learning splice prediction tools using functional splice assays.

Hereditary disorders are frequently caused by genetic variants that affect pre-messenger RNA splicing. Though genetic variants in the canonical splice...

Segmenting lung lesions of COVID-19 from CT images via pyramid pooling improved Unet.

Segmenting lesion regions of Coronavirus Disease 2019 (COVID-19) from computed tomography (CT) images is a challenge owing to COVID-19 lesions charact...

COVID-19 and the kidney: A retrospective analysis of 37 critically ill patients using machine learning.

INTRODUCTION: There is evidence that SARS-CoV2 has a particular affinity for kidney tissue and is often associated with kidney failure.

Feasibility of predicting allele specific expression from DNA sequencing using machine learning.

Allele specific expression (ASE) concerns divergent expression quantity of alternative alleles and is measured by RNA sequencing. Multiple studies sho...

Digital imaging, technologies and artificial intelligence applications during COVID-19 pandemic.

The advancement of technology remained an immersive interest for humankind throughout the past decades. Tech enterprises offered a stream of innovatio...

Kernel machine SNP set analysis finds the association of BUD13, ZPR1, and APOA5 variants with metabolic syndrome in Tehran Cardio-metabolic Genetics Study.

Metabolic syndrome (MetS) is one of the most important risk factors for cardiovascular disease. The 11p23.3 chromosomal region plays a potential role ...

Deep learning for predicting COVID-19 malignant progression.

As COVID-19 is highly infectious, many patients can simultaneously flood into hospitals for diagnosis and treatment, which has greatly challenged publ...

Selecting a stable solid form of remdesivir using microcrystal electron diffraction and crystal structure prediction.

Therapeutic options in response to the coronavirus disease 2019 (COVID-19) outbreak are urgently needed. In this communication, we demonstrate how to ...

Eliminating Indefiniteness of Clinical Spectrum for Better Screening COVID-19.

The coronavirus disease 2019 (COVID-19) has swept all over the world. Due to the limited detection facilities, especially in developing countries, a l...