Diagnostic Radiology

Involvement of Machine Learning Tools in Healthcare Decision Making.

In the present day, there are many diseases which need to be identified at their early stages to start relevant treatments. If not, they could be uncurable and deadly. Due to this reason, there is a need of analysing complex medical data, medical reports, and medical images at a lesser time but with greater accuracy. There are even some instances where certain abnormalities cannot be directly reco...

RadLex Normalization in Radiology Reports.

Radiology reports have been widely used for extraction of various clinically significant information about patients' imaging studies. However, limited research has focused on standardizing the entities to a common radiology-specific vocabulary. Further, no study to date has attempted to leverage RadLex for standardization. In this paper, we aim to normalize a diverse set of radiological entities t...

Deep learning in breast radiology: current progress and future directions.

This review provides an overview of current applications of deep learning methods within breast radiology. The diagnostic capabilities of deep learnin...

Analysis of Stroke Detection during the COVID-19 Pandemic Using Natural Language Processing of Radiology Reports.

BACKGROUND AND PURPOSE: The coronavirus disease 2019 (COVID-19) pandemic has led to decreases in neuroimaging volume. Our aim was to quantify the chan...

Artificial intelligence and hybrid imaging: the best match for personalized medicine in oncology.

Artificial intelligence (AI) refers to a field of computer science aimed to perform tasks typically requiring human intelligence. Currently, AI is rec...

Machine learning in cardiovascular radiology: ESCR position statement on design requirements, quality assessment, current applications, opportunities, and challenges.

Machine learning offers great opportunities to streamline and improve clinical care from the perspective of cardiac imagers, patients, and the industr...

Artificial Intelligence and Big Data.

Medical images play an important role in ophthalmology and radiology. Medical image analysis has greatly benefited from the application of "deep learn...

Preparing Radiologists to Lead in the Era of Artificial Intelligence: Designing and Implementing a Focused Data Science Pathway for Senior Radiology Residents.

Artificial intelligence and machine learning (AI-ML) have taken center stage in medical imaging. To develop as leaders in AI-ML, radiology residents m...

Canadian Association of Radiologists White Paper on De-Identification of Medical Imaging: Part 1, General Principles.

The application of big data, radiomics, machine learning, and artificial intelligence (AI) algorithms in radiology requires access to large data sets ...

Canadian Association of Radiologists White Paper on De-identification of Medical Imaging: Part 2, Practical Considerations.

The application of big data, radiomics, machine learning, and artificial intelligence (AI) algorithms in radiology requires access to large data sets ...

Regulatory Frameworks for Development and Evaluation of Artificial Intelligence-Based Diagnostic Imaging Algorithms: Summary and Recommendations.

Although artificial intelligence (AI)-based algorithms for diagnosis hold promise for improving care, their safety and effectiveness must be ensured t...

Findings from machine learning in clinical medical imaging applications - Lessons for translation to the forensic setting.

Machine learning (ML) techniques are increasingly being used in clinical medical imaging to automate distinct processing tasks. In post-mortem forensi...

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 for abnormalities. We curated and analyzed a chest c...

Artificial Intelligence in Low- and Middle-Income Countries: Innovating Global Health Radiology.

Scarce or absent radiology resources impede adoption of artificial intelligence (AI) for medical imaging by resource-poor health institutions. They fa...

Potential use of deep learning techniques for postmortem imaging.

The use of postmortem computed tomography in forensic medicine, in addition to conventional autopsy, is now a standard procedure in several countries....

A review on recent advancements in diagnosis and classification of cancers using artificial intelligence.

Artificial intelligence has illustrated drastic changes in radiology and medical imaging techniques which in turn led to tremendous changes in screeni...

3D Deep Learning on Medical Images: A Review.

The rapid advancements in machine learning, graphics processing technologies and the availability of medical imaging data have led to a rapid increase...

Computational Radiology in Breast Cancer Screening and Diagnosis Using Artificial Intelligence.

Breast cancer screening has been shown to significantly reduce mortality in women. The increased utilization of screening examinations has led to grow...

Between Always and Never: Evaluating Uncertainty in Radiology Reports Using Natural Language Processing.

The ideal radiology report reduces diagnostic uncertainty, while avoiding ambiguity whenever possible. The purpose of this study was to characterize t...

Unbiased identification of novel subclinical imaging biomarkers using unsupervised deep learning.

Artificial intelligence has recently made a disruptive impact in medical imaging by successfully automatizing expert-level diagnostic tasks. However, ...