Diagnostic Radiology

Multimodal imaging and deep learning in geographic atrophy secondary to age-related macular degeneration.

Geographic atrophy (GA) secondary to age-related macular degeneration is among the most common cause...

Comparative analysis of diagnostic imaging models built with automated machine learning.

Age-Specific Diagnostic Classification of ASD Using Deep Learning Approaches.

Autism Spectrum Disorder (ASD) is a highly heterogeneous condition, due to high variance in its etio...

Early Experiences of Integrating an Artificial Intelligence-Based Diagnostic Decision Support System into Radiology Settings: A Qualitative Study.

Artificial Intelligence (AI) based clinical decision support systems to aid diagnosis are increasing...

Commercially Available Chest Radiograph AI Tools for Detecting Airspace Disease, Pneumothorax, and Pleural Effusion.

Background Commercially available artificial intelligence (AI) tools can assist radiologists in inte...

Diagnostic Performance of Deep Learning Models for Gastric Intestinal Metaplasia Detection in Narrow-band Images.

Gastric Intestinal Metaplasia (GIM) is one of the precancerous conditions in the gastric carcinogene...

Super-resolution multimode fiber imaging with an untrained neural network.

Multimode fiber endoscopes provide extreme miniaturization of imaging components for minimally invas...

Evolution of the newest diagnostic methods for COVID-19: a Chinese perspective.

Coronavirus disease 2019 (COVID-19) has continued to spread globally since late 2019, representing a...

[Research status and outlook of deep learning in oral and maxillofacial medical imaging].

Artificial intelligence, represented by deep learning, has received increasing attention in the fiel...

Computer-aided detection and diagnosis/radiomics/machine learning/deep learning in medical imaging.

[Development and Application of Medical Imaging Analysis Platform Based on Radiomics and Machine Learning Technologies].

OBJECTIVE: In order to solve the technical problems, clinical researchers face the process of medica...

ENRICHing medical imaging training sets enables more efficient machine learning.

OBJECTIVE: Deep learning (DL) has been applied in proofs of concept across biomedical imaging, inclu...

Addressing the Challenges of Implementing Artificial Intelligence Tools in Clinical Practice: Principles From Experience.

The multitude of artificial intelligence (AI)-based solutions, vendors, and platforms poses a challe...

Ontologies in the New Computational Age of Radiology: RadLex for Semantics and Interoperability in Imaging Workflows.

From basic research to the bedside, precise terminology is key to advancing medicine and ensuring op...

Robotics in Interventional Radiology: Review of Current and Future Applications.

This review is a brief overview of the current status and the potential role of robotics in interven...

Artificial intelligence 101 for veterinary diagnostic imaging.

The prevalence and pervasiveness of artificial intelligence (AI) with medical images in veterinary a...

The role of artificial intelligence in clinical imaging and workflows.

Evidence-based medicine, outcomes management, and multidisciplinary systems are laying the foundatio...

Intelligent imaging: Applications of machine learning and deep learning in radiology.

Artificial intelligence (AI) in radiology is transforming medical image analysis. While applications...

Anti-noise computational imaging using unsupervised deep learning.

Computational imaging enables spatial information retrieval of objects with the use of single-pixel ...

Key concepts, common pitfalls, and best practices in artificial intelligence and machine learning: focus on radiomics.

Artificial intelligence (AI) and machine learning (ML) are increasingly used in radiology research t...

[Artificial intelligence in medical application: problems and legal countermeasures].

Artificial intelligence (AI) is widely used in the current medical field at home and abroad, includi...