Radiology

Deep learning for 3D vascular segmentation in hierarchical phase contrast tomography: a case study on kidney.

Automated blood vessel segmentation is critical for biomedical image analysis, as vessel morphology changes are associated with numerous pathologies. Still, precise segmentation is difficult due to the complexity of vascular structures, anatomical variations across patients, the scarcity of annotated public datasets, and the quality of images. Our goal is to provide a foundation on the topic and i...

Deep learning automatically distinguishes myocarditis patients from normal subjects based on MRI.

Myocarditis, characterized by inflammation of the myocardial tissue, presents substantial risks to cardiovascular functionality, potentially precipitating critical outcomes including heart failure and arrhythmias. This investigation primarily aims to identify the optimal cardiovascular magnetic resonance imaging (CMRI) views for distinguishing between normal and myocarditis cases, using deep learn...

Cross-instrument optical coherence tomography-angiography (OCTA)-based prediction of age-related macular degeneration (AMD) disease activity using artificial intelligence.

This study investigates the efficacy of predicting age-related macular degeneration (AMD) activity through deep neural networks (DNN) using a cross-in...

Artificial intelligence-based prediction of neurocardiovascular risk score from retinal swept-source optical coherence tomography-angiography.

The recent rise of artificial intelligence represents a revolutionary way of improving current medical practices, including cardiovascular (CV) assess...

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 proper structural and functional maturation lays t...

Image quality in three-dimensional (3D) contrast-enhanced dynamic magnetic resonance imaging of the abdomen using deep learning denoising technique: intraindividual comparison between T1-weighted sequences with compressed sensing and with a modified Fast 3D mode wheel.

PURPOSE: To assess the image quality of a modified Fast three-dimensional (Fast 3D) mode wheel with sequential data filling (mFast 3D wheel) combined ...

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. Establishing linkages between imaging biomarkers and sym...

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 (MRI) in the risk diagnosis and prognosis of endomet...

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 contrast-enhanced computed tomography enterography ...

Multi-Loss Disentangled Generative-Discriminative Learning for Multimodal Representation in Schizophrenia.

Schizophrenia (SCZ) is a multifactorial mental illness, thus it will be beneficial for exploring this disease using multimodal data, including functio...

Characterizing the Contribution of Dependent Features in XAI Methods.

Explainable Artificial Intelligence (XAI) provides tools to help understanding how AI models work and reach a particular decision or outcome. It helps...

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 information from different domains to be correlated. H...

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 substantial challenges. These issues, including perce...

Modeling Functional Brain Networks for ADHD via Spatial Preservation-Based Neural Architecture Search.

Modeling functional brain networks (FBNs) for attention deficit hyperactivity disorder (ADHD) has sparked significant interest since the abnormal func...

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

Multi-parametric magnetic resonance imaging (mpMRI) exams have various series types acquired with different imaging protocols. The DICOM headers of th...

Autonomous mobile robots for exploratory synthetic chemistry.

Autonomous laboratories can accelerate discoveries in chemical synthesis, but this requires automated measurements coupled with reliable decision-maki...

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 rely on their findings. Saliency maps can enhance DNN...

Human-Artificial Intelligence Symbiotic Reporting for Theranostic Cancer Care.

Reporting of diagnostic nuclear images in clinical cancer management is generally qualitative. Theranostic treatment with Lu radioligands for prostate...

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 rapid and accurate diagnostic solutions for effective...

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 elderly population. KOA is a persistent condition,...