Latest AI and machine learning research in radiology for healthcare professionals.
OBJECTIVE: To explore the utility of natural language processing (NLP) and machine learning (ML) techniques to identify unsafe conditions leading to cardiovascular diagnostic errors using patient safety event (PSE) reports data. METHODS: PSE reports from January 2016 to August 2021 from a multi-hospital healthcare system in the mid-Atlantic region of the United States were included in this study. ...
BACKGROUND: Idiopathic normal pressure hydrocephalus (iNPH) is characterized by a clinical triad of symptoms: abnormal gait, memory problems, and urinary incontinence. Neuroimaging plays a crucial role in diagnosing iNPH. However, current radiological markers, though indicative, are not definitive, suggesting the limited capacity of these indices to capture mechanisms associated with iNPH and the ...
This study investigates practical design choices for Bayesian uncertainty quantification (UQ) in model-based deep learning (MoDL) for accelerated MRI ...
We developed an automatic self-enhancement-based perfusion mapping (SEPM) method to relatively map the microvascular perfusion level in contrast-enhan...
Thyroid nodule ultrasound (US) images and their features are of great importance in thyroid nodule diagnosis, and can be helpful for radiologists' cli...
BACKGROUND: ST-segment elevation myocardial infarction (STEMI) and its equivalents describe the electrocardiogram (ECG) findings of acute coronary occ...
INTRODUCTION: Artificial intelligence (AI)-based imaging modalities are next-generation diagnostic devices for abdominal infections that promise to pr...
Intracranial vessel wall imaging (VWI) has emerged as a critical tool in neurovascular diagnostics, enabling direct assessment of the vessel wall, whi...
BACKGROUND: Cervical squamous cell carcinoma is a major global health burden, with many patients presenting with locally advanced disease requiring co...
PURPOSE: Preoperative identification of lymph node metastasis (LNM) in cervical cancer is crucial for guiding therapeutic strategies but remains clini...
PURPOSE: Evaluation of the impact of Deep Learning Image Reconstruction (DLIR) compared to Adaptive Statistical Iterative Reconstruction-Veo (ASIR-V) ...
Despite the remarkable success of deep brain stimulation (DBS) in alleviating Parkinson's disease (PD) symptoms, complexities arising from inherent in...
Convolutional Neural Networks (CNNs) have become a cornerstone of medical image analysis due to their proficiency in learning hierarchical spatial fea...
Lumbar spine disorders represent one of the most prevalent musculoskeletal conditions worldwide, particularly among the elderly population. Magnetic R...
Precise staging of endometriosis remains a clinical challenge, as current diagnosis depends almost entirely on laparoscopic visualization-an invasive ...
Reconstructing functional brain networks from functional Magnetic Resonance Imaging (fMRI) data typically relies on statistical pruning, where pairwis...
Pancreatic ductal adenocarcinoma (PDAC) remains one of the most lethal malignancies, with prognosis strongly influenced by the presence of lymph node ...
BACKGROUND: Minimizing radiation exposure during pediatric spinal deformity correction is critical due to the cumulative lifetime effects of ionizing ...
BACKGROUND: Epicardial adipose tissue (EAT) is a metabolically active visceral fat depot that is both a sensor and a modulator of myocardial biology a...
Cystic breast lesions are commonly encountered on breast ultrasound, encompassing a spectrum from simple benign cysts to complex mixed solid and cysti...