Diagnostic Radiology

Accelerating Development and Clinical Deployment of Diagnostic Imaging Artificial Intelligence.

Application of Medical Imaging Based on Deep Learning in the Treatment of Lumbar Degenerative Diseases and Osteoporosis with Bone Cement Screws.

OBJECTIVE: To explore the application value of magnetic resonance spectroscopy (MRS) and GSI-energy ...

The Trials and Tribulations of Assembling Large Medical Imaging Datasets for Machine Learning Applications.

With vast interest in machine learning applications, more investigators are proposing to assemble la...

Guest Editorial Annotation-Efficient Deep Learning: The Holy Grail of Medical Imaging.

Diagnostic test accuracy of artificial intelligence analysis of cross-sectional imaging in pulmonary hypertension: a systematic literature review.

OBJECTIVES: To undertake the first systematic review examining the performance of artificial intelli...

Dynamic memory to alleviate catastrophic forgetting in continual learning with medical imaging.

Medical imaging is a central part of clinical diagnosis and treatment guidance. Machine learning has...

Combining multi-site magnetic resonance imaging with machine learning predicts survival in pediatric brain tumors.

Brain tumors represent the highest cause of mortality in the pediatric oncological population. Diagn...

Whole-slide imaging, tissue image analysis, and artificial intelligence in veterinary pathology: An updated introduction and review.

Since whole-slide imaging has been commercially available for over 2 decades, digital pathology has ...

Introduction to the artificial intelligence in pediatric radiology imaging special issue.

Health Evaluation and Fault Diagnosis of Medical Imaging Equipment Based on Neural Network Algorithm.

In recent years, high-precision medical equipment, especially large-scale medical imaging equipment,...

Current and emerging artificial intelligence applications in chest imaging: a pediatric perspective.

Artificial intelligence (AI) applications for chest radiography and chest CT are among the most deve...

Visual Analytics for Hypothesis-Driven Exploration in Computational Pathology.

Recent advances in computational and algorithmic power are evolving the field of medical imaging rap...

Radiomics: a primer on high-throughput image phenotyping.

Radiomics is a high-throughput approach to image phenotyping. It uses computer algorithms to extract...

Professionals' responses to the introduction of AI innovations in radiology and their implications for future adoption: a qualitative study.

BACKGROUND: Artificial Intelligence (AI) innovations in radiology offer a potential solution to the ...

Deep learning in knee imaging: a systematic review utilizing a Checklist for Artificial Intelligence in Medical Imaging (CLAIM).

PURPOSE: Our purposes were (1) to explore the methodologic quality of the studies on the deep learni...

A Novel Deep Learning System for Diagnosing Early Esophageal Squamous Cell Carcinoma: A Multicenter Diagnostic Study.

INTRODUCTION: This study aims to construct a real-time deep convolutional neural networks (DCNNs) sy...

Patterns of Metastatic Disease in Patients with Cancer Derived from Natural Language Processing of Structured CT Radiology Reports over a 10-year Period.

Background Patterns of metastasis in cancer are increasingly relevant to prognostication and treatme...

A Survey of Soft Computing Approaches in Biomedical Imaging.

Medical imaging is an essential technique for the diagnosis and treatment of diseases in modern clin...

A Conference-Friendly, Hands-on Introduction to Deep Learning for Radiology Trainees.

Artificial or augmented intelligence, machine learning, and deep learning will be an increasingly im...

Ensuring Adequate Development and Appropriate Use of Artificial Intelligence in Pediatric Medical Imaging.

Of over 100 FDA-cleared artificial intelligence (AI) tools for triage, detection, or diagnosis in me...

Deep reinforcement learning in medical imaging: A literature review.

Deep reinforcement learning (DRL) augments the reinforcement learning framework, which learns a sequ...