FDA General

Repurposing potential of FDA-approved and investigational drugs for COVID-19 targeting SARS-CoV-2 spike and main protease and validation by machine learning algorithm.

The present study aimed to assess the repurposing potential of existing antiviral drug candidates (F...

Assessing Drug Development Risk Using Big Data and Machine Learning.

Identifying new drug targets and developing safe and effective drugs is both challenging and risky. ...

Artificial intelligence and its impact on quality improvement in upper and lower gastrointestinal endoscopy.

Artificial intelligence (AI) and its application in medicine has grown large interest. Within gastro...

The effect of topical application of meloxicam on inflamed dental pulp.

BACKGROUND/PURPOSE: Effective regulation of the inflammatory process is essential for pulp repair an...

Population-Scale CT-based Body Composition Analysis of a Large Outpatient Population Using Deep Learning to Derive Age-, Sex-, and Race-specific Reference Curves.

Background Although CT-based body composition (BC) metrics may inform disease risk and outcomes, obt...

Target-Centered Drug Repurposing Predictions of Human Angiotensin-Converting Enzyme 2 (ACE2) and Transmembrane Protease Serine Subtype 2 (TMPRSS2) Interacting Approved Drugs for Coronavirus Disease 2019 (COVID-19) Treatment through a Drug-Target Interaction Deep Learning Model.

Previously, our group predicted commercially available Food and Drug Administration (FDA) approved d...

DeepSIBA: chemical structure-based inference of biological alterations using deep learning.

Predicting whether a chemical structure leads to a desired or adverse biological effect can have a s...

Identification of early liver toxicity gene biomarkers using comparative supervised machine learning.

Screening agrochemicals and pharmaceuticals for potential liver toxicity is required for regulatory ...

Matrix Factorization-based Technique for Drug Repurposing Predictions.

Classical drug design methodologies are hugely costly and time-consuming, with approximately 85% of ...

Deep Learning Applied to Vegetation Identification and Removal Using Multidimensional Aerial Data.

When performing structural inspection, the generation of three-dimensional (3D) point clouds is a co...

Profiling SARS-CoV-2 Main Protease (M) Binding to Repurposed Drugs Using Molecular Dynamics Simulations in Classical and Neural Network-Trained Force Fields.

The current COVID-19 pandemic caused by a novel coronavirus SARS-CoV-2 urgently calls for a working ...

2019 Novel Coronavirus-Infected Pneumonia on CT: A Feasibility Study of Few-Shot Learning for Computerized Diagnosis of Emergency Diseases.

COVID-19 is an emerging disease with transmissibility and severity. So far, there are no effective t...

Industry ties and evidence in public comments on the FDA framework for modifications to artificial intelligence/machine learning-based medical devices: a cross sectional study.

OBJECTIVES: To determine the extent and disclosure of financial ties to industry and use of scientif...

Approval policies for modifications to machine learning-based software as a medical device: A study of bio-creep.

Successful deployment of machine learning algorithms in healthcare requires careful assessments of t...

Discussion on "Approval policies for modifications to machine learning-based software as a medical device: A study of bio-creep" by Jean Feng, Scott Emerson, and Noah Simon.

Discussion on "Approval policies for modifications to machine learning-based software as a medical device: A study of biocreep" by Jean Feng, Scott Emerson, and Noah Simon.

I applaud the authors of Feng (2020) for tackling a challenging statistical problem on approval pol...

Rejoinder to Discussions on "Approval policies for modifications to machine learning-based software as a medical device: A study of bio-creep".

We thank the discussants for sharing their unique perspectives on the problem of designing automatic...

Machine-learning-based multiple abnormality prediction with large-scale chest computed tomography volumes.

Machine learning models for radiology benefit from large-scale data sets with high quality labels fo...

Deep Learning to Estimate RECIST in Patients with NSCLC Treated with PD-1 Blockade.

Real-world evidence (RWE), conclusions derived from analysis of patients not treated in clinical tri...

Machine learning at the interface of structural health monitoring and non-destructive evaluation.

While both non-destructive evaluation (NDE) and structural health monitoring (SHM) share the objecti...

Adverse drug event detection using reason assignments in FDA drug labels.

Adverse drug events (ADEs) are unintended incidents that involve the taking of a medication. ADEs po...