Radiology

Application of Machine Learning in the Diagnosis and Prognosis of Mild Traumatic Brain Injury Using Diffusion Tensor Imaging: A Systematic Review.

BACKGROUND: Traumatic Brain Injury (TBI) is a global health concern, with mild TBI (mTBI) being the most common form. Despite its prevalence, accurately diagnosing mTBI remains a significant challenge. While advanced neuroimaging techniques like diffusion tensor imaging (DTI) offer promise for more robust diagnosis, their clinical application is limited by inconsistent and heterogeneous post-injur...

Identification of structural predictors of lung function improvement in adults with cystic fibrosis treated with elexacaftor-tezacaftor-ivacaftor using deep-learning.

PURPOSE: The purpose of this study was to evaluate the relationship between structural abnormalities on CT and lung function prior to and after initiation of elexacaftor-tezacaftor-ivacaftor (ETI) in adults with cystic fibrosis (CF) using a deep learning model. MATERIALS AND METHODS: A deep learning quantification model was developed using 100 chest computed tomography (CT) examinations of patient...

Evaluation of Context-Aware Prompting Techniques for Classification of Tumor Response Categories in Radiology Reports Using Large Language Model.

Radiology reports are essential for medical decision-making, providing crucial data for diagnosing diseases, devising treatment plans, and monitoring ...

Development of a High-Performance Ultrasound Prediction Model for the Diagnosis of Endometrial Cancer: An Interpretable XGBoost Algorithm Utilizing SHAP Analysis.

OBJECTIVES: To develop and validate an ultrasonography-based machine learning (ML) model for predicting malignant endometrial and cavitary lesions. ME...

Optimized T1-weighted MP-RAGE MRI of the brain at 0.55 T using variable flip angle coherent gradient echo imaging and deep learning reconstruction.

PURPOSE: To propose and evaluate an optimized MP-RAGE protocol for rapid T1-weighted imaging of the brain at 0.55 T. METHODS: Incoherent and coherent ...

Artificial Intelligence Deep Learning Ultrasound Discrimination of Cosmetic Fillers: A Multicenter Study.

OBJECTIVES: Despite the growing use of artificial intelligence (AI) in medicine, imaging, and dermatology, to date, there is no information on the use...

A systematic review on deep learning based brain tumor segmentation and detection using MRI: Past insights, present techniques and future trends.

The abnormal growth of cells leads to brain malignancy in humans, which is among the most prevalent causes of fatalities in adults worldwide. Patients...

Modified UNet-enhanced ultrasonic superb microvascular imaging feature extraction and grading of carpal tunnel syndrome.

Carpal tunnel syndrome (CTS) is recognized as the most frequently encountered median nerve (MN) entrapment neuropathy, with a disproportionate burden ...

Automatic Body Region Classification in CT Scans Using Deep Learning.

Accurate classification of anatomical regions in computed tomography (CT) scans is essential for optimizing downstream diagnostic and analytic workflo...

A radiomics nomogram utilizing T2-weighted MRI for accurate diagnosis of rectocele.

BACKGROUND: Rectocele (RC) is a common pelvic organ prolapse (POP) that can cause obstructed defecation and reduced quality of life. Magnetic resonanc...

AI demonstrates comparable diagnostic performance to radiologists in MRI detection of anterior cruciate ligament tears: a systematic review and meta-analysis.

INTRODUCTION: Anterior cruciate ligament (ACL) injuries are among the most common knee injuries, affecting 1 in 3500 people annually. With rising rate...

Photon-counting detector computed tomography in thoracic oncology: revolutionizing tumor imaging through precision and detail.

Photon-counting detector computed tomography (PCD-CT) is an emerging imaging technology that promises to overcome the limitations of conventional ener...

Ethical Considerations in Patient Privacy and Data Handling for AI in Cardiovascular Imaging and Radiology.

The integration of artificial intelligence (AI) into cardiovascular imaging and radiology offers the potential to enhance diagnostic accuracy, streaml...

Localizing Knee Pain via Explainable Bayesian Generative Models and Counterfactual MRI: Data from the Osteoarthritis Initiative.

Osteoarthritis (OA) pain often does not correlate with magnetic resonance imaging (MRI)-detected structural abnormalities, limiting the clinical utili...

Exploiting Cross-modal Collaboration and Discrepancy for Semi-supervised Ischemic Stroke Lesion Segmentation from Multi-sequence MRI Images.

Accurate ischemic stroke lesion segmentation is useful to define the optimal reperfusion treatment and unveil the stroke etiology. Despite the importa...

Interpolation techniques for ultrasonic data.

Many applications where ultrasound is used for diagnostics exist where limited data is preventing a particular approach from being fully exploited; fo...

Muscle Composition as a Novel Prognostic Tool for Pain, Frailty, and Sarcopenia.

A clinical observation common to both sarcopenia and frailty is compositional changes to skeletal muscles. Automatic segmentation and quantification o...

Advancements in ocular blood flow imaging: Clinical applications of laser speckle flowgraphy and optical coherence tomography angiography in retinal and choroidal vascular diseases.

Ocular blood flow imaging techniques have become indispensable in current clinical practice because retinal vascular disturbances have been implicated...

Lightweight Transfer Learning Models for Multi-Class Brain Tumor Classification: Glioma, Meningioma, Pituitary Tumors, and No Tumor MRI Screening.

Glioma, pituitary tumors, and meningiomas constitute the major types of primary brain tumors. The challenge in achieving a definitive diagnosis stem f...

Optimising Generalisable Deep Learning Models for CT Coronary Segmentation: A Multifactorial Evaluation.

Coronary artery disease (CAD) remains a leading cause of morbidity and mortality worldwide, with incidence rates continuing to rise. Automated coronar...