Publications by authors named "Emilia Gryska"
Article Synopsis
- - High-resolution CT images are crucial for accurate 3D virtual surgical planning and creating patient-specific surgical guides, particularly in procedures like corrective osteotomy of the distal radius.
- - The study assessed variations among experienced raters regarding the surface contact of virtual radius models and surgical guides, discovering that guides designed with 1mm slice thickness CT images reliably matched the reference radius, while thicker slices showed greater discrepancies.
- - Overall, while the average discrepancies in radius models from CT images with thicker slice thicknesses were still below 1mm, the study highlights that using 1mm slices is optimal for surgical guide design to maintain clinical relevance.
Unlabelled: Out-of-hospital cardiac arrest (OHCA) is a critical condition with low survival rates. In patients with a return of spontaneous circulation, brain injury is a leading cause of death. In this study, we propose an interpretable machine learning approach for predicting neurologic outcome after OHCA, using information available at the time of hospital admission.
View Article and Find Full Text PDFArticle Synopsis
- Standard volar plates often don't fit well on malunited distal radius bones after surgery, leading to the need for an offset angle for proper volar tilt correction.
- A new shim instrument, developed using 3D surgical planning, helps surgeons hold plates at the correct angle while locking screws, and it was tested in surgery on five older women with specific wrist deformities.
- Post-surgery results showed an average correction of tilt issues, but there were some errors and loss of correction due to tension in the bone, indicating that while the shim is helpful, further improvements are needed to address these challenges.
Objectives: To define requirements that condition trust in artificial intelligence (AI) as clinical decision support in radiology from the perspective of various stakeholders and to explore ways to fulfil these requirements.
Methods: Semi-structured interviews were conducted with twenty-five respondents-nineteen directly involved in the development, implementation, or use of AI applications in radiology and six working with AI in other areas of healthcare. We designed the questions to explore three themes: development and use of AI, professional decision-making, and management and organizational procedures connected to AI.
Objectives: To determine the reproducibility and replicability of studies that develop and validate segmentation methods for brain tumours on MRI and that follow established reproducibility criteria; and to evaluate whether the reporting guidelines are sufficient.
Methods: Two eligible validation studies of distinct deep learning (DL) methods were identified. We implemented the methods using published information and retraced the reported validation steps.
Objectives: Medical image analysis practices face challenges that can potentially be addressed with algorithm-based segmentation tools. In this study, we map the field of automatic MR brain lesion segmentation to understand the clinical applicability of prevalent methods and study designs, as well as challenges and limitations in the field.
Design: Scoping review.
Introduction: Automatic brain lesion segmentation from medical images has great potential to support clinical decision making. Although numerous methods have been proposed, significant challenges must be addressed before they will become established in clinical and research practice. We aim to elucidate the state of the art, to provide a synopsis of competing approaches and identify contrasts between them.
View Article and Find Full Text PDF