Technology and health care : official journal of the European Society for Engineering and Medicine
39520159
BACKGROUND: Pulmonary nodule, one of the most common clinical phenomena, is an irregular circular lesion with a diameter of ⩽ 3 cm in the lungs, which can be classified as benign or malignant. Differentiating benign and malignant pulmonary nodules ha...
OBJECTIVE: This study aimed to establish a multimodal deep-learning network model to enhance the diagnosis of benign and malignant pulmonary ground glass nodules (GGNs).
BACKGROUND: Pulmonary nodules are a common incidental finding on chest Computed Tomography scans (CT), most of the time outside of lung cancer screening (LCS). We aimed to evaluate the number of incidental pulmonary nodules (IPN) found in 1 year in o...
OBJECTIVES: Evaluating the diagnostic feasibility of accelerated pulmonary MR imaging for detection and characterisation of pulmonary nodules with artificial intelligence-aided compressed sensing.
The study aimed to evaluate the impact of AI assistance on pulmonary nodule detection rates among radiology residents and senior radiologists, along with assessing the effectiveness of two different commercialy available AI software systems in improv...
PURPOSE: Currently, deep learning methods for the classification of benign and malignant lung nodules encounter challenges encompassing intricate and unstable algorithmic models, limited data adaptability, and an abundance of model parameters.To tack...
BACKGROUND: Clinical utility data on pulmonary nodule (PN) risk stratification biomarkers are lacking. We aimed to determine the incremental predictive value and clinical utility of using an artificial intelligence (AI) radiomics-based computer-aided...
BACKGROUND: To retrospectively assess the added value of an artificial intelligence (AI) algorithm for detecting pulmonary nodules on ultra-low-dose computed tomography (ULDCT) performed at the emergency department (ED).
BACKGROUND: Correctly distinguishing between benign and malignant pulmonary nodules can avoid unnecessary invasive procedures. This study aimed to construct a deep learning radiomics clinical nomogram (DLRCN) for predicting malignancy of pulmonary no...
PURPOSE: To evaluate the accuracy of ultra-low dose (ULD) chest computed tomography (CT), with a radiation exposure equivalent to a 2-view chest x-ray, for pulmonary nodule detection using deep learning image reconstruction (DLIR).