A5085237507- Radiomics and Machine Learning in Medical Imaging
- Pituitary Gland Disorders and Treatments
- Glioma Diagnosis and Treatment
- Medical Imaging and Analysis
- Medical Imaging Techniques and Applications
- Cerebrovascular and Carotid Artery Diseases
- Medical Image Segmentation Techniques
- Meningioma and schwannoma management
- Retinal Imaging and Analysis
- Spinal Fractures and Fixation Techniques
- Spine and Intervertebral Disc Pathology
- Advanced Neural Network Applications
- TGF-β signaling in diseases
- Meta-analysis and systematic reviews
- Machine Learning in Materials Science
- Artificial Intelligence in Healthcare and Education
- Surgical Simulation and Training
- Acute Ischemic Stroke Management
- AI in cancer detection
- Hemodynamic Monitoring and Therapy
- Intracranial Aneurysms: Treatment and Complications
- Anatomy and Medical Technology
University Hospital of Zurich
2021-2025
University of Zurich
2021-2025
Computed tomography (CT) imaging is a cornerstone in the assessment of patients with spinal trauma and planning interventions. However, CT studies are associated logistical problems, acquisition costs, radiation exposure. In this proof-of-concept study, feasibility generating synthetic images using biplanar radiographs was explored. This could expand potential applications x-ray machines pre-, post-, even intraoperatively.
Assessment of pituitary adenoma (PA) volume and extent resection (EOR) through manual segmentation is time-consuming likely suffers from poor interrater agreement, especially postoperatively. Automated tumor volumetry by use deep learning techniques may provide more objective quick volumetry.
Abstract Purpose Biochemical remission (BR), gross total resection (GTR), and intraoperative cerebrospinal fluid (CSF) leaks are important metrics in transsphenoidal surgery for acromegaly, prediction of their likelihood using machine learning would be clinically advantageous. We aim to develop externally validate clinical models outcomes after acromegaly. Methods Using data from two registries, we GTR, BR, CSF endoscopic acromegalic patients. For the model development a registry Bologna,...
Abstract Purpose Volumetric assessments, such as extent of resection (EOR) or residual tumor volume, are essential criterions in glioma surgery. Our goal is to develop and validate segmentation machine learning models for pre- postoperative magnetic resonance imaging scans, allowing us assess the percentagewise reduction after intracranial surgery gliomas. Methods For development preoperative model (U-Net), MRI scans 1053 patients from Multimodal Brain Tumor Segmentation Challenge (BraTS)...
OBJECTIVE Contemporary oncological paradigms for adjuvant treatment of low- and intermediate-grade gliomas are often guided by a limited array parameters, overlooking the dynamic nature disease. The authors’ aim was to develop comprehensive multivariate glioma growth model based on multicentric data, facilitate more individualized therapeutic strategies. METHODS Random slope models with subject-specific random intercepts were fitted retrospective cohort grade II III from database at Kepler...
Virtual and augmented reality have enjoyed increased attention in spine surgery. Preoperative planning, pedicle screw placement, surgical training are among the most studied use cases. Identifying osseous structures is a key aspect of navigating 3-dimensional virtual reconstruction. To automate otherwise time-consuming process labeling vertebrae on each slice individually, we propose fully automated pipeline that automates segmentation computed tomography (CT) which can form basis for...
The pursuit of automated methods to assess the extent resection (EOR) in glioblastomas is challenging, requiring precise measurement residual tumor volume. Many algorithms focus on preoperative scans, making them unsuitable for postoperative studies. Our objective was develop a deep learning-based model segmentation using magnetic resonance imaging (MRI). We also compared our model's performance with other available algorithms.
Gross total resection (GTR), Biochemical Remission (BR) and restitution of a priorly disrupted hypothalamus pituitary axis (new improvement, IMP) are important factors in adenoma (PA) surgery. Prediction these metrics using simple preoperatively available data might help improve patient care contribute to more personalized medicine.This study aims develop machine learning models predicting GTR, BR, IMP PA surgery, data.With from patients undergoing endoscopic transsphenoidal surgery for PAs...