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Artificial intelligence-enabled safety monitoring in Alzheimer's disease clinical trials.
J Prev Alzheimers Dis
January 2025
Alzheimer's Therapeutic Research Institute, University of Southern California, San Diego, CA, United States.
Background: Investigators conducting clinical trials have an ethical, scientific, and regulatory obligation to protect the safety of trial participants. Traditionally, safety monitoring includes manual review and coding of adverse event data by expert clinicians.
Objectives: Our study explores the use of natural language processing (NLP) and artificial intelligence (AI) methods to streamline and standardize clinician coding of adverse event data in Alzheimer's disease (AD) clinical trials.
Clinical Application Of Deep Learning-assisted Needles Reconstruction In Prostate Ultrasound Brachytherapy.
Int J Radiat Oncol Biol Phys
January 2025
McGill university, Montreal, Qc, Canada.
Purpose: High dose rate (HDR) prostate brachytherapy (BT) procedure requires image-guided needle insertion. Given that general anesthesia is often employed during the procedure, minimizing overall planning time is crucial. In this study, we explore the clinical feasibility and time-saving potential of artificial intelligence (AI)-driven auto-reconstruction of transperineal needles in the context of US-guided prostate BT planning.
View Article and Find Full Text PDFA pose estimation approach for discarded stacked smartphones recycling: Based on instance segmentation and point cloud registration.
Waste Manag
January 2025
ZheJiang University, Department of Mechanical Engineering, ZheJiang, 310000, China.
With the rapid increase in end-of-life smartphones, enhancing the automation and intelligence of their recycling processes has become an urgent challenge. At present, the disassembly of discarded smartphones predominantly relies on manual labor, which is not only inefficient but also associated with environmental pollution and high labor intensity. In the context of end-of-life smartphone recycling, complex situations such as stacking and occlusion are commonly encountered.
View Article and Find Full Text PDFDevelopment of a model for measuring sagittal plane parameters in 10-18-year old adolescents with idiopathic scoliosis based on RTMpose deep learning technology.
J Orthop Surg Res
January 2025
Department of Human Anatomy, Graduate School, Inner Mongolia Medical University, Hohhot, 010010, Inner Mongolia, China.
Purpose: The study aimed to develop a deep learning model for rapid, automated measurement of full-spine X-rays in adolescents with Adolescent Idiopathic Scoliosis (AIS). A significant challenge in this field is the time-consuming nature of manual measurements and the inter-individual variability in these measurements. To address these challenges, we utilized RTMpose deep learning technology to automate the process.
View Article and Find Full Text PDFImproving 3D deep learning segmentation with biophysically motivated cell synthesis.
Commun Biol
January 2025
Institute for Automation and Applied Informatics, Karlsruhe Institute of Technology, Eggenstein-Leopoldshafen, Germany.
Biomedical research increasingly relies on three-dimensional (3D) cell culture models and artificial-intelligence-based analysis can potentially facilitate a detailed and accurate feature extraction on a single-cell level. However, this requires for a precise segmentation of 3D cell datasets, which in turn demands high-quality ground truth for training. Manual annotation, the gold standard for ground truth data, is too time-consuming and thus not feasible for the generation of large 3D training datasets.
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