Radiology

Improving brain atrophy quantification with deep learning from automated labels using tissue similarity priors.

Brain atrophy measurements derived from magnetic resonance imaging (MRI) are a promising marker for the diagnosis and prognosis of neurodegenerative pathologies such as Alzheimer's disease or multiple sclerosis. However, its use in individualized assessments is currently discouraged due to a series of technical and biological issues. In this work, we present a deep learning pipeline for segmentati...

CapNet: An Automatic Attention-Based with Mixer Model for Cardiovascular Magnetic Resonance Image Segmentation.

Deep neural networks have shown excellent performance in medical image segmentation, especially for cardiac images. Transformer-based models, though having advantages over convolutional neural networks due to the ability of long-range dependence learning, still have shortcomings such as having a large number of parameters and and high computational cost. Additionally, for better results, they are ...

Evaluation of fragility fracture risk using deep learning based on ultrasound radio frequency signal.

BACKGROUND: It was essential to identify individuals at high risk of fragility fracture and prevented them due to the significant morbidity, mortality...

Applications of artificial intelligence in computed tomography imaging for phenotyping pulmonary hypertension.

PURPOSE OF REVIEW: Pulmonary hypertension is a heterogeneous condition with significant morbidity and mortality. Computer tomography (CT) plays a cent...

Radiological characterization of the tailings of an abandoned copper mine using a neural network and geostatistical analysis through the Co-Kriging method.

The radiological characterization of soil contaminated with natural radionuclides enables the classification of the area under investigation, the opti...

Developing a deep learning model for predicting ovarian cancer in Ovarian-Adnexal Reporting and Data System Ultrasound (O-RADS US) Category 4 lesions: A multicenter study.

PURPOSE: To develop a deep learning (DL) model for differentiating between benign and malignant ovarian tumors of Ovarian-Adnexal Reporting and Data S...

Three-dimensional convolutional neural network-based classification of chronic kidney disease severity using kidney MRI.

A three-dimensional convolutional neural network model was developed to classify the severity of chronic kidney disease (CKD) using magnetic resonance...

Intelligent Bladder Volume Monitoring for Wearable Ultrasound Devices: Enhancing Accuracy Through Deep Learning-Based Coarse-to-Fine Shape Estimation.

Accurate and continuous bladder volume monitoring is crucial for managing urinary dysfunctions. Wearable ultrasound (US) devices offer a solution by e...

Displacement Tracking Techniques in Ultrasound Elastography: From Cross Correlation to Deep Learning.

Ultrasound elastography is a noninvasive medical imaging technique that maps viscoelastic properties to characterize tissues and diseases. Elastograph...

Simplifying risk stratification for thyroid nodules on ultrasound: validation and performance of an artificial intelligence thyroid imaging reporting and data system.

PURPOSE: To validate the performance of a recently created risk stratification system (RSS) for thyroid nodules on ultrasound, the Artificial Intellig...

Applications of spatial transcriptomics and artificial intelligence to develop integrated management of pancreatic cancer.

Cancer is a complex disease intrinsically associated with cellular processes and gene expression. With the development of techniques such as single-ce...

CFINet: Cross-Modality MRI Feature Interaction Network for Pseudoprogression Prediction of Glioblastoma.

Pseudoprogression (PSP) is a related reaction of glioblastoma treatment, and misdiagnosis can lead to unnecessary intervention. Magnetic resonance ima...

AI-assisted Segmentation Tool for Brain Tumor MR Image Analysis.

TumorPrism3D software was developed to segment brain tumors with a straightforward and user-friendly graphical interface applied to two- and three-dim...

From vision to text: A comprehensive review of natural image captioning in medical diagnosis and radiology report generation.

Natural Image Captioning (NIC) is an interdisciplinary research area that lies within the intersection of Computer Vision (CV) and Natural Language Pr...

Integrating Radiomics and Neural Networks for Knee Osteoarthritis Incidence Prediction.

OBJECTIVE: Accurately predicting knee osteoarthritis (KOA) is essential for early detection and personalized treatment. We aimed to develop and test a...

F-FDG PET/CT Radiomics-Based Multimodality Fusion Model for Preoperative Individualized Noninvasive Prediction of Peritoneal Metastasis in Advanced Gastric Cancer.

PURPOSE: This study was designed to develop and validate a machine learning-based, multimodality fusion (MMF) model using F-fluorodeoxyglucose (FDG) P...

AI-based selection of individuals for supplemental MRI in population-based breast cancer screening: the randomized ScreenTrustMRI trial.

Screening mammography reduces breast cancer mortality, but studies analyzing interval cancers diagnosed after negative screens have shown that many ca...

A comprehensive approach for evaluating lymphovascular invasion in invasive breast cancer: Leveraging multimodal MRI findings, radiomics, and deep learning analysis of intra- and peritumoral regions.

PURPOSE: To evaluate lymphovascular invasion (LVI) in breast cancer by comparing the diagnostic performance of preoperative multimodal magnetic resona...

From Diagnosis to Precision Surgery: The Transformative Role of Artificial Intelligence in Urologic Imaging.

The multidisciplinary nature of artificial intelligence (AI) has allowed for rapid growth of its application in medical imaging. Artificial intelligen...

Cross-attention enables deep learning on limited omics-imaging-clinical data of 130 lung cancer patients.

Deep-learning tools that extract prognostic factors derived from multi-omics data have recently contributed to individualized predictions of survival ...