International journal of radiation oncology, biology, physics
38246249
PURPOSE: Artificial intelligence (AI)-based auto-segmentation models hold promise for enhanced efficiency and consistency in organ contouring for adaptive radiation therapy and radiation therapy planning. However, their performance on pediatric compu...
Canadian Association of Radiologists journal = Journal l'Association canadienne des radiologistes
38189316
BACKGROUND: Multi-detector contrast-enhanced abdominal computed tomography (CT) allows for the accurate detection and classification of traumatic splenic injuries, leading to improved patient management. Their effective use requires rapid study inter...
Manual delineation of liver segments on computed tomography (CT) images for primary/secondary liver cancer (LC) patients is time-intensive and prone to inter/intra-observer variability. Therefore, we developed a deep-learning-based model to auto-cont...
Journal of imaging informatics in medicine
38366294
Computed tomography (CT) is the most commonly used diagnostic modality for blunt abdominal trauma (BAT), significantly influencing management approaches. Deep learning models (DLMs) have shown great promise in enhancing various aspects of clinical pr...
OBJECTIVES: The objective of our study was to develop a nomogram to predict post-transjugular intrahepatic portosystemic shunt (TIPS) survival in patients with cirrhosis based on CT images.
Accurate multi-organ segmentation in 3D CT images is imperative for enhancing computer-aided diagnosis and radiotherapy planning. However, current deep learning-based methods for 3D multi-organ segmentation face challenges such as the need for labor-...
International journal of radiation oncology, biology, physics
38797500
PURPOSE: Radiation-induced lymphopenia (RIL) is common among patients undergoing radiation therapy (RT)' Severe RIL has been linked to adverse outcomes. The severity and risk of RIL can be predicted from baseline clinical characteristics and dosimetr...
Deep learning abdominal organ segmentation algorithms have shown excellent results in adults; validation in children is sparse. The purpose of this article is to develop and validate deep learning models for liver, spleen, and pancreas segmentation...
Journal of imaging informatics in medicine
38937343
As the adoption of artificial intelligence (AI) systems in radiology grows, the increase in demand for greater bandwidth and computational resources can lead to greater infrastructural costs for healthcare providers and AI vendors. To that end, we de...