Radiology

Artificial Intelligence in Dental Imaging Practice among Oral Radiologists from the City of Chennai: A Qualitative Analysis.

BACKGROUND: Artificial intelligence (AI) has the potential to significantly transform the role of the doctor and revolutionize the practice of medicine and radiology. Studies have documented the clinical potentials of AI in medical imaging practice to improving patient care. There are no studies done in India that assess the attitude and perspectives of oral radiologists toward AI in dental radiol...

Diagnostic accuracy of machine learning-based magnetic resonance imaging models in breast cancer classification: a systematic review and meta-analysis.

OBJECTIVE: This meta-analysis evaluates the diagnostic accuracy of machine learning (ML)-based magnetic resonance imaging (MRI) models in distinguishing benign from malignant breast lesions and explores factors influencing their performance.

Uncovering Image-Driven Subtypes with Distinct Pathology and Clinical Course in Autopsy-Confirmed Four Repeat Tauopathies.

OBJECTIVES: The four-repeat (4R) tauopathies are a group of neurodegenerative diseases, including progressive supranuclear palsy (PSP), corticobasal d...

Widespread distribution of bacteria containing PETases with a functional motif across global oceans.

Accumulating evidence indicates that microorganisms respond to the ubiquitous plastic pollution by evolving plastic-degrading enzymes. However, the fu...

Arthroscopy-validated diagnostic performance of sub-5-min deep learning super-resolution 3T knee MRI in children and adolescents.

OBJECTIVE: This study aims to determine the diagnostic performance of sub-5-min combined sixfold parallel imaging (PIx3)-simultaneous multislice (SMSx...

Advancements and Applications of Hyperpolarized Xenon MRI for COPD Assessment in China.

Chronic obstructive pulmonary disease (COPD) is one of the leading causes of morbidity and mortality in China, highlighting the importance of early di...

Uncertainty estimation for trust attribution to speed-of-sound reconstruction with variational networks.

PURPOSE: Speed-of-sound (SoS) is a biomechanical characteristic of tissue, and its imaging can provide a promising biomarker for diagnosis. Reconstruc...

Challenges and Advances in Classifying Brain Tumors: An Overview of Machine, Deep Learning, and Hybrid Approaches with Future Perspectives in Medical Imaging.

Accurate brain tumor classification is essential in neuro-oncology, as it directly informs treatment strategies and influences patient outcomes. This ...

ACOUSLIC-AI challenge report: Fetal abdominal circumference measurement on blind-sweep ultrasound data from low-income countries.

Fetal growth restriction, affecting up to 10% of pregnancies, is a critical factor contributing to perinatal mortality and morbidity. Ultrasound measu...

Artery fragment guided approach for enhancing cerebral aneurysm detection in TOF-MRA imaging.

BACKGROUND: Cerebral aneurysms are a type of cerebrovascular disease that poses a severe threat to life and health. Early screening using Time-of-Flig...

MRISeqClassifier: A Deep Learning Toolkit for Precise MRI Sequence Classification.

Magnetic Resonance Imaging (MRI) is a crucial diagnostic tool in medicine, widely used to detect and assess various health conditions. Different MRI s...

Towards large nuclear imaging system optical simulations with optiGAN, a generative adversarial network.

Optical Monte Carlo (MC) simulations are essential for modeling light transport in radiation detectors used in nuclear imaging and high-energy physics...

Deep Learning in Digital Breast Tomosynthesis: Current Status, Challenges, and Future Trends.

The high-resolution three-dimensional (3D) images generated with digital breast tomosynthesis (DBT) in the screening of breast cancer offer new possib...

AI-powered remote monitoring of brain responses to clear and incomprehensible speech via speckle pattern analysis.

SIGNIFICANCE: Functional magnetic resonance imaging provides high spatial resolution but is limited by cost, infrastructure, and the constraints of an...

Investigating methods to enhance interpretability and performance in cardiac MRI for myocardial scarring diagnosis using convolutional neural network classification and One Match.

Machine learning (ML) classification of myocardial scarring in cardiac MRI is often hindered by limited explainability, particularly with convolutiona...

Multimodal Artificial Intelligence Using Endoscopic USG, CT, and MRI to Differentiate Between Serous and Mucinous Cystic Neoplasms.

Introduction Serous cystic neoplasms (SCN) and mucinous cystic neoplasms (MCN) often exhibit similar imaging features when evaluated with a single ima...

Contribution of Labrum and Cartilage to Joint Surface in Different Hip Deformities: An Automatic Deep Learning-Based 3-Dimensional Magnetic Resonance Imaging Analysis.

BACKGROUND: Multiple 2-dimensional magnetic resonance imaging (MRI) studies have indicated that the size of the labrum adjusts in response to altered ...

Patient-specific deep learning tracking for real-time 2D pancreas localisation in kV-guided radiotherapy.

BACKGROUND AND PURPOSE: In pancreatic stereotactic body radiotherapy (SBRT), accurate motion management is crucial for the safe delivery of high doses...

The value of intratumoral and peritumoral ultrasound radiomics model constructed using multiple machine learning algorithms for non-mass breast cancer.

To investigate the diagnostic capability of multiple machine learning algorithms combined with intratumoral and peritumoral ultrasound radiomics model...

Application of Mask R-CNN for automatic recognition of teeth and caries in cone-beam computerized tomography.

OBJECTIVES: Deep convolutional neural networks (CNNs) are advancing rapidly in medical research, demonstrating promising results in diagnosis and pred...