Diagnostic Radiology

Personalized Breast Cancer Treatments Using Artificial Intelligence in Radiomics and Pathomics.

Progress in computing power and advances in medical imaging over recent decades have culminated in new opportunities for artificial intelligence (AI), computer vision, and using radiomics to facilitate clinical decision-making. These opportunities are growing in medical specialties, such as radiology, pathology, and oncology. As medical imaging and pathology are becoming increasingly digitized, it...

An investigation of CNN models for differentiating malignant from benign lesions using small pathologically proven datasets.

Cancer has been one of the most threatening diseases to human health. There have been many efforts devoted to the advancement of radiology and transformative tools (e.g. non-invasive computed tomographic or CT imaging) to detect cancer in early stages. One of the major goals is to identify malignant from benign lesions. In recent years, machine deep learning (DL), e.g. convolutional neural network...

Support Vector Machine Classification of Nonmelanoma Skin Lesions Based on Fluorescence Lifetime Imaging Microscopy.

Early diagnosis of malignant skin lesions is critical for prompt treatment and a clinical prognosis of skin cancers. However, it is difficult to preci...

Management of Thyroid Nodules Seen on US Images: Deep Learning May Match Performance of Radiologists.

BackgroundManagement of thyroid nodules may be inconsistent between different observers and time consuming for radiologists. An artificial intelligenc...

Imaging Quality Control in the Era of Artificial Intelligence.

The advent of artificial intelligence (AI) promises to have a transformational impact on quality in medicine, including in radiology. However, experie...

Feasibility of Natural Language Processing-Assisted Auditing of Critical Findings in Chest Radiology.

OBJECTIVE: Time-sensitive communication of critical imaging findings like pneumothorax or pulmonary embolism to referring physicians is essential for ...

Overview of image-to-image translation by use of deep neural networks: denoising, super-resolution, modality conversion, and reconstruction in medical imaging.

Since the advent of deep convolutional neural networks (DNNs), computer vision has seen an extremely rapid progress that has led to huge advances in m...

Artificial intelligence and radiomics in pulmonary nodule management: current status and future applications.

Artificial intelligence (AI) has been present in some guise within the field of radiology for over 50 years. The first studies investigating computer-...

Convolutional sparse kernel network for unsupervised medical image analysis.

The availability of large-scale annotated image datasets and recent advances in supervised deep learning methods enable the end-to-end derivation of r...

A Road Map for Translational Research on Artificial Intelligence in Medical Imaging: From the 2018 National Institutes of Health/RSNA/ACR/The Academy Workshop.

Advances in machine learning in medical imaging are occurring at a rapid pace in research laboratories both at academic institutions and in industry. ...

Using Artificial Intelligence to Revise ACR TI-RADS Risk Stratification of Thyroid Nodules: Diagnostic Accuracy and Utility.

Background Risk stratification systems for thyroid nodules are often complicated and affected by low specificity. Continual improvement of these syste...

Canadian Association of Radiologists White Paper on Ethical and Legal Issues Related to Artificial Intelligence in Radiology.

Artificial intelligence (AI) software that analyzes medical images is becoming increasingly prevalent. Unlike earlier generations of AI software, whic...

Machine learning concepts, concerns and opportunities for a pediatric radiologist.

Machine learning, a subfield of artificial intelligence, is a rapidly evolving technology that offers great potential for expanding the quality and va...

The present and future of deep learning in radiology.

The advent of Deep Learning (DL) is poised to dramatically change the delivery of healthcare in the near future. Not only has DL profoundly affected t...

Novel Machine Learning Identifies Brain Patterns Distinguishing Diagnostic Membership of Human Immunodeficiency Virus, Alcoholism, and Their Comorbidity of Individuals.

The incidence of alcohol use disorder (AUD) in human immunodeficiency virus (HIV) infection is twice that of the rest of the population. This study do...

Artificial intelligence and the radiologist: the future in the Armed Forces Medical Services.

Artificial intelligence (AI) involves computational networks (neural networks) that simulate human intelligence. The incorporation of AI in radiology ...

Recent technical development of artificial intelligence for diagnostic medical imaging.

Deep learning has caused a third boom of artificial intelligence and great changes of diagnostic medical imaging systems such as radiology, pathology,...

Convolutional Neural Networks for Radiologic Images: A Radiologist's Guide.

Deep learning has rapidly advanced in various fields within the past few years and has recently gained particular attention in the radiology community...

Comparative effectiveness of convolutional neural network (CNN) and recurrent neural network (RNN) architectures for radiology text report classification.

This paper explores cutting-edge deep learning methods for information extraction from medical imaging free text reports at a multi-institutional scal...

Artificial intelligence and medical imaging 2018: French Radiology Community white paper.

The rapid development of information technology and data processing capabilities has led to the creation of new tools known as artificial intelligence...