Publications by authors named "M Segala"
Article Synopsis
- A new technique using Total Cardiac Volume (TCV) and CT scans aims to improve pediatric heart transplant outcomes by allowing better size matching of donor and recipient hearts, but current methods require extensive manual work.
- This study explores a Deep Learning approach, specifically a 3D Convolutional Neural Network (3D-CNN), to automatically measure TCV, demonstrating high accuracy in estimating heart size quickly.
- With a strong validation performance (average Dice coefficient of 0.94 and mean absolute percent error of 5.5%), the study emphasizes the need for future multicenter trials to enhance the model's applicability across different patient demographics and heart conditions.
Article Synopsis
- Total Cardiac Volume (TCV) size matching using CT scans can help match donor and recipient hearts for pediatric transplants, but current manual methods are time-consuming and require specialized training.
- This study investigates the effectiveness of a Deep Learning method using 3D Convolutional Neural Networks (3D-CNN) to quickly and accurately measure TCV, aiming to improve transplant matching across various centers.
- Results show that the deep learning model achieved a high accuracy (Dice coefficient of 0.94) and a low average error (5.5%) in estimating TCV, making it a promising tool for future pediatric heart transplants.
In the present work, we study an electrolyte solution confined between planar surfaces with nanopatterned charged domains, which has been connected to a bulk ionic reservoir. The system is investigated through an improved Monte Carlo (MC) simulation method, suitable for simulation of electrolytes in the presence of modulated surface charge distributions. We also employ a linear approach in the spirit of the classical Debye-Hückel approximation, which allows one to obtain explicit expressions for the averaged potentials, ionic profiles, effective surface interactions and the net ionic charge confined between the walls.
View Article and Find Full Text PDFCutting-Edge Implant Rehabilitation Design and Management: A Tapered Abutment Approach.
Compend Contin Educ Dent
July 2017
Soft-tissue recession around an implant rehabilitation over time has been considered a physiologic phenomenon. The divergent profile of the abutment and the abutment's dis/reconnections are the most critical predisposing and precipitating factors regarding such gingival recession. Recent publications have discussed how tapered and marginless abutments that allow no disconnections and increase soft-tissue thickness could prevent implant rehabilitations from experiencing gingival recession.
View Article and Find Full Text PDFModels based on Au(111) face have been extensively used to describe self-assembled monolayers, as well nanoparticles and nanoclusters. However, for very small clusters (<2 nm), the chemisorption of ligands leads to surface reconstruction, making necessary the use of a more reliable model that is able to simulate the main electronic and geometrical features of these small systems. In this work, a simple model to describe the geometries and the metal-ligand bonding in chalcogenate-protected gold nanoclusters is proposed.
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