Radiology

Latest AI and machine learning research in radiology for healthcare professionals.

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Machine-learning based prediction of future outcome using multimodal MRI during early childhood.

The human brain undergoes rapid changes from the fetal stage to two years postnatally, during which ...

Improved patient identification by incorporating symptom severity in deep learning using neuroanatomic images in first episode schizophrenia.

Brain alterations associated with illness severity in schizophrenia remain poorly understood. Establ...

Automated detection of bone lesions using CT and MRI: a systematic review.

PURPOSE: The aim of this study was to systematically review the use of automated detection systems f...

Computed tomography enterography radiomics and machine learning for identification of Crohn's disease.

BACKGROUND: Crohn's disease is a severe chronic and relapsing inflammatory bowel disease. Although c...

Artificial intelligence-assisted magnetic resonance imaging technology in the differential diagnosis and prognosis prediction of endometrial cancer.

It aimed to analyze the value of deep learning algorithm combined with magnetic resonance imaging (M...

Autonomous mobile robots for exploratory synthetic chemistry.

Autonomous laboratories can accelerate discoveries in chemical synthesis, but this requires automate...

Classification of Multi-Parametric Body MRI Series Using Deep Learning.

Multi-parametric magnetic resonance imaging (mpMRI) exams have various series types acquired with di...

Mitigating Diagnostic Errors in Lung Cancer Classification: A Multi-Eyes Principle to Uncertainty Quantification.

In radiology, particularly in lung cancer diagnosis, diagnostic errors and cognitive biases pose sub...

Framework for Deep Learning Based Multi-Modality Image Registration of Snapshot and Pathology Images.

Multi-modality image registration is an important task in medical imaging because it allows for info...

Anatomic Interpretability in Neuroimage Deep Learning: Saliency Approaches for Typical Aging and Traumatic Brain Injury.

The black box nature of deep neural networks (DNNs) makes researchers and clinicians hesitant to rel...

Human-Artificial Intelligence Symbiotic Reporting for Theranostic Cancer Care.

Reporting of diagnostic nuclear images in clinical cancer management is generally qualitative. Thera...

Minimizing prostate diffusion weighted MRI examination time through deep learning reconstruction.

PURPOSE: To study the diagnostic image quality of high b-value diffusion weighted images (DWI) deriv...

AmbiBias Contrast: Enhancing debiasing networks via disentangled space from ambiguity-bias clusters.

The goal of debiasing in classification tasks is to train models to be less sensitive to correlation...

Deep learning approaches for automated classification of neonatal lung ultrasound with assessment of human-to-AI interrater agreement.

Neonatal respiratory disorders pose significant challenges in clinical settings, often requiring rap...

Optimizing knee osteoarthritis severity prediction on MRI images using deep stacking ensemble technique.

Knee osteoarthritis (KOA) represents a well-documented degenerative arthropathy prevalent among the ...

Multi-modal representation learning in retinal imaging using self-supervised learning for enhanced clinical predictions.

Self-supervised learning has become the cornerstone of building generalizable and transferable artif...

FedART: A neural model integrating federated learning and adaptive resonance theory.

Federated Learning (FL) has emerged as a promising paradigm for collaborative model training across ...

Artificial Intelligence and Radiologist Burnout.

IMPORTANCE: Understanding the association of artificial intelligence (AI) with physician burnout is ...

Edge Computing for AI-Based Brain MRI Applications: A Critical Evaluation of Real-Time Classification and Segmentation.

Medical imaging plays a pivotal role in diagnostic medicine with technologies like Magnetic Resonanc...

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