AIMC Topic: Feasibility Studies
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Deep learning-based quick MLC sequencing for MRI-guided online adaptive radiotherapy: a feasibility study for pancreatic cancer patients.
Physics in medicine and biology
Feb 12, 2025
One bottleneck of magnetic resonance imaging (MRI)-guided online adaptive radiotherapy is the time-consuming daily online replanning process. The current leaf sequencing method takes up to 10 min, with potential dosimetric degradation and small segme...
Feasibility of remote robot empowered teleultrasound scanning for radioactive patients.
Scientific reports
Feb 3, 2025
To investigate the feasibility of robot-assisted teleultrasound diagnosis for radioactive patients compared with conventional ultrasound diagnosis. In this prospective study (ChineseClinicalTrials.gov identifier, ChiCTR2200057253), 32 radioactive pat...
The potential use of deep learning in performing autocorrection of setup errors in patients receiving radiotherapy.
Radiography (London, England : 1995)
Jan 31, 2025
INTRODUCTION: Modern radiotherapy practice relies on multiple approaches for verification of patient positioning. All of these techniques require experienced radiotherapists who understand the anatomical landmarks and the limitations of the used veri...
Feasibility of using Gramian angular field for preprocessing MR spectroscopy data in AI classification tasks: Differentiating glioblastoma from lymphoma.
European journal of radiology
Jan 29, 2025
OBJECTIVES: To convert 1D spectra into 2D images using the Gramian angular field, to be used as input for convolutional neural network for classification tasks such as glioblastoma versus lymphoma.
Feasibility of generating sagittal radiographs from coronal views using GAN-based deep learning framework in adolescent idiopathic scoliosis.
European radiology experimental
Jan 29, 2025
BACKGROUND: Minimizing radiation exposure is crucial in monitoring adolescent idiopathic scoliosis (AIS). Generative adversarial networks (GANs) have emerged as valuable tools being able to generate high-quality synthetic images. This study explores ...
Deep learning-based Monte Carlo dose prediction for heavy-ion online adaptive radiotherapy and fast quality assurance: A feasibility study.
Medical physics
Jan 27, 2025
BACKGROUND: Online adaptive radiotherapy (OART) and rapid quality assurance (QA) are essential for effective heavy ion therapy (HIT). However, there is a shortage of deep learning (DL) models and workflows for predicting Monte Carlo (MC) doses in suc...
Preliminary investigation of an artificial intelligence-based cognitive behavioral therapy training tool.
Psychotherapy (Chicago, Ill.)
Jan 27, 2025
We developed an asynchronous online cognitive behavioral therapy (CBT) training tool that provides artificial intelligence- (AI-) enabled feedback to learners across eight CBT skills. We sought to evaluate the technical reliability and to ascertain h...
Brachytherapy Seed Placement by Robotic Bronchoscopy with Cone Beam Computed Tomography Guidance for Peripheral Lung Cancer: A Human Cadaveric Feasibility Pilot.
International journal of radiation oncology, biology, physics
Jan 25, 2025
PURPOSE: This study evaluates the feasibility of using robotic-assisted bronchoscopy with cone beam computed tomography (RB-CBCT) platform to perform low-dose-rate brachytherapy (LDR-BT) implants in a mechanically ventilated human cadaveric model. Po...
The feasibility of using machine learning to predict COVID-19 cases.
International journal of medical informatics
Jan 23, 2025
BACKGROUND: Coronavirus Disease 2019 (COVID-19), caused by the SARS-CoV-2 virus, emerged as a global health crisis in 2019, resulting in widespread morbidity and mortality. A persistent challenge during the pandemic has been the accuracy of reported ...
Diagnostic Performance of Different Examination Types and Learning Curves of Radiologists for 5G-Based Robot-Assisted Tele-Ultrasonography: A Prospective and Large-Scale Study.
Journal of ultrasound in medicine : official journal of the American Institute of Ultrasound in Medicine
Jan 22, 2025
OBJECTIVE: To investigate the feasibility of remotely providing routine ultrasound (US) examinations to patients using a fifth-generation-based robot-assisted tele-ultrasonography (RATU) system in a real-world setting.
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