Radiology

Combining lipidomics and machine learning to identify lipid biomarkers for nonsyndromic cleft lip with palate.

Nonsyndromic cleft lip with palate (nsCLP) is a common birth defect disease. Current diagnostic methods comprise fetal ultrasound images, which are mainly limited by fetal position and technician skills. We aimed to identify reliable maternal serum lipid biomarkers to diagnose nsCLP. Eight-feature selection methods were used to assess the dysregulated lipids from untargeted lipidomics in a discove...

Simulating mismatch between calibration and target population in AI for mammography the retrospective VAIB study.

AI cancer detection models require calibration to attain the desired balance between cancer detection rate (CDR) and false positive rate. In this study, we simulate the impact of six types of mismatches between the calibration population and the clinical target population, by creating purposefully non-representative datasets to calibrate AI for clinical settings. Mismatching the acquisition year b...

Cross-Institutional Evaluation of Large Language Models for Radiology Diagnosis Extraction: A Prompt-Engineering Perspective.

The rapid evolution of large language models (LLMs) offers promising opportunities for radiology report annotation, aiding in determining the presence...

Advances in the application of 18F-sodium fluoride PET in the assessment of atherosclerosis.

Atherosclerosis serves as the primary cause of cardiovascular diseases (CVDs), with its pathological processes encompassing lipid deposition, inflamma...

Deep learning segmentation of periarterial and perivenous capillary-free zones in optical coherence tomography angiography.

SIGNIFICANCE: Automated segmentation of periarterial and perivenous capillary-free zones (CFZs) in optical coherence tomography angiography (OCTA) can...

Interpretable MRI-Based Deep Learning for Alzheimer's Risk and Progression.

Timely intervention for Alzheimer's disease (AD) requires early detection. The development of immunotherapies targeting amyloid-beta and tau underscor...

Enhancing efficient deep learning models with multimodal, multi-teacher insights for medical image segmentation.

The rapid evolution of deep learning has dramatically enhanced the field of medical image segmentation, leading to the development of models with unpr...

Deep learning approaches for classification tasks in medical X-ray, MRI, and ultrasound images: a scoping review.

Medical images occupy the largest part of the existing medical information and dealing with them is challenging not only in terms of management but al...

The added value of artificial intelligence using Quantib Prostate for the detection of prostate cancer at multiparametric magnetic resonance imaging.

PURPOSE: Artificial intelligence (AI) has been proposed to assist radiologists in reporting multiparametric magnetic resonance imaging (mpMRI) of the ...

Prompt Engineering for Large Language Models in Interventional Radiology.

Prompt engineering plays a crucial role in optimizing artificial intelligence (AI) and large language model (LLM) outputs by refining input structure,...

Automated Detection of Black Hole Sign for Intracerebral Hemorrhage Patients Using Self-Supervised Learning.

BACKGROUND AND PURPOSE: Intracerebral Hemorrhage (ICH) is a devastating form of stroke. Hematoma expansion (HE), growth of the hematoma on interval sc...

Impact of the recent advances in coronary artery disease imaging on pilot medical certification and aviation safety: current state and future perspective.

Coronary artery disease (CAD) is highly prevalent among pilots due to the nature of their lifestyle, and occupational stresses. CAD is one the most co...

Enhancing Obstetric Decision-Making With AI: A Systematic Review of AI Models for Predicting Mode of Delivery.

Accurate prediction of the mode of delivery is critical for optimizing maternal and neonatal outcomes and reducing unnecessary cesarean sections. In r...

BentRay-NeRF: Bent-Ray Neural Radiance Fields for Robust Speed-of-Sound Imaging in Ultrasound Computed Tomography.

Ultrasound computed tomography (USCT) is a promising technique for breast cancer detection because of its quantitative imaging capability of the speed...

Unsupervised Test-Time Adaptation for Hepatic Steatosis Grading Using Ultrasound B-Mode Images.

Ultrasound (US) is considered a key modality for the clinical assessment of hepatic steatosis (i.e., fatty liver) due to its noninvasiveness and avail...

Synthetic Lung Ultrasound Data Generation Using Autoencoder With Generative Adversarial Network.

Class imbalance is a significant challenge in medical image analysis, particularly in lung ultrasound (LUS), where severe patterns are often underrepr...

Speckle pattern analysis with deep learning for low-cost stroke detection: a phantom-based feasibility study.

SIGNIFICANCE: Stroke is a leading cause of disability worldwide, necessitating rapid and accurate diagnosis to limit irreversible brain damage. Howeve...

Two distinct trajectories of brain volume loss in myotonic dystrophy type 1 via machine learning.

Myotonic dystrophy Type 1 is a disorder that affects multiple systems, including the muscles and the CNS. Previous studies have primarily used voxel-b...

OA-HybridCNN (OHC): An advanced deep learning fusion model for enhanced diagnostic accuracy in knee osteoarthritis imaging.

Knee osteoarthritis (KOA) is a leading cause of disability globally. Early and accurate diagnosis is paramount in preventing its progression and impro...

A Deep Learning Approach for Mandibular Condyle Segmentation on Ultrasonography.

Deep learning techniques have demonstrated potential in various fields, including segmentation, and have recently been applied to medical image proces...