OBJECTIVE: To demonstrate the clinical advantages of a deep-learning image reconstruction (DLIR) in low-dose dual-energy computed tomography enterography (DECTE) by comparing images with standard-dose adaptive iterative reconstruction-Veo (ASIR-V) im...
Journal of imaging informatics in medicine
Mar 14, 2024
Radiology narrative reports often describe characteristics of a patient's disease, including its location, size, and shape. Motivated by the recent success of multimodal learning, we hypothesized that this descriptive text could guide medical image a...
Journal of imaging informatics in medicine
Mar 14, 2024
Deep learning techniques have recently yielded remarkable results across various fields. However, the quality of these results depends heavily on the quality and quantity of data used during the training phase. One common issue in multi-class and mul...
Soft robotic grippers excel at achieving conformal and reliable contact with objects without the need for complex control algorithms. However, they still lack in grasp and manipulation abilities compared with human hands. In this study, we present a ...
Journal of imaging informatics in medicine
Mar 14, 2024
The application of deep learning (DL) in medicine introduces transformative tools with the potential to enhance prognosis, diagnosis, and treatment planning. However, ensuring transparent documentation is essential for researchers to enhance reproduc...
European journal of internal medicine
Mar 14, 2024
OBJECTIVES: Steatotic liver disease is the most frequent chronic liver disease worldwide. Ultrasonography (US) is commonly employed for the assessment and diagnosis. Few information is available on the possible use of artificial intelligence (AI) to ...
Mobile robotic telepresence systems require that information about the environment, the task, and the robot be presented to a remotely located user (operator) who controls the robot for a specific task. In this study, two interaction modes, proactive...
BACKGROUND: Machine learning may assist with the identification of potentially inappropriate penicillin allergy labels. Strategies to improve the performance of existing models for this task include the use of additional training data, synthetic data...
PURPOSE: We aimed to evaluate the effect of deep learning-based reconstruction (DLR) on high-spatial-resolution three-dimensional T2-weighted fast asymmetric spin-echo (HR-3D T2-FASE) imaging in the preoperative evaluation of cerebellopontine angle (...
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