Investigating the Effect of Providing Required Training to Mothers of Children with Surgery and Its Effect on Mothers' Anxiety
Julia Ferreira
Nadia Safa
Fabio Botelho
Robin Petroze
Hussein Wissanji
Pramod Puligandla
Kenneth Shaw
Maeve Trudeau
Elena Guadagno
Jean-Martin Laberge
Sherif Emil
Investigating the Effect of Providing Required Training to Mothers of Children with Surgery and Its Effect on Mothers' Anxiety
Julia Ferreira
Nadia Safa
Fabio Botelho
Robin Petroze
Hussein Wissanji
Pramod Puligandla
Kenneth Shaw
Maeve Trudeau
Elena Guadagno
Jean Martin Laberge
Sherif Emil
Longitudinal reproducibility of brain and spinal cord quantitative MRI biomarkers
Mathieu Boudreau
Agah Karakuzu
Arnaud Boré
Basile Pinsard
Kiril Zelenkovski
Eva Alonso‐Ortiz
Julie Boyle
Lune Bellec
Abstract Quantitative MRI (qMRI) promises better specificity, accuracy, repeatability, and reproducibility relative to its clinically-used q… (see more)ualitative MRI counterpart. Longitudinal reproducibility is particularly important in qMRI. The goal is to reliably quantify tissue properties that may be assessed in longitudinal clinical studies throughout disease progression or during treatment. In this work, we present the initial data release of the quantitative MRI portion of the Courtois project on neural modelling (CNeuroMod), where the brain and cervical spinal cord of six participants were scanned at regular intervals over the course of several years. This first release includes 3 years of data collection and up to 10 sessions per participant using quantitative MRI imaging protocols (T1, magnetization transfer (MTR, MTsat), and diffusion). In the brain, T1MP2RAGE, fractional anisotropy (FA), mean diffusivity (MD), and radial diffusivity (RD) all exhibited high longitudinal reproducibility (intraclass correlation coefficient – ICC ≃ 1 and within-subject coefficient of variations – wCV 1%). The spinal cord cross-sectional area (CSA) computed using T2w images and T1MTsat exhibited the best longitudinal reproducibility (ICC ≃ 1 and 0.7 respectively, and wCV 2.4% and 6.9%). Results from this work show the level of longitudinal reproducibility that can be expected from qMRI protocols in the brain and spinal cord in the absence of hardware and software upgrades, and could help in the design of future longitudinal clinical studies.
Longitudinal reproducibility of brain and spinal cord quantitative MRI biomarkers
Mathieu Boudreau
Agah Karakuzu
Arnaud Boré
Basile Pinsard
Kiril Zelenkovski
Eva Alonso‐Ortiz
Julie Boyle
Lune Bellec
Abstract Quantitative MRI (qMRI) promises better specificity, accuracy, repeatability, and reproducibility relative to its clinically-used q… (see more)ualitative MRI counterpart. Longitudinal reproducibility is particularly important in qMRI. The goal is to reliably quantify tissue properties that may be assessed in longitudinal clinical studies throughout disease progression or during treatment. In this work, we present the initial data release of the quantitative MRI portion of the Courtois project on neural modelling (CNeuroMod), where the brain and cervical spinal cord of six participants were scanned at regular intervals over the course of several years. This first release includes 3 years of data collection and up to 10 sessions per participant using quantitative MRI imaging protocols (T1, magnetization transfer (MTR, MTsat), and diffusion). In the brain, T1MP2RAGE, fractional anisotropy (FA), mean diffusivity (MD), and radial diffusivity (RD) all exhibited high longitudinal reproducibility (intraclass correlation coefficient – ICC ≃ 1 and within-subject coefficient of variations – wCV 1%). The spinal cord cross-sectional area (CSA) computed using T2w images and T1MTsat exhibited the best longitudinal reproducibility (ICC ≃ 1 and 0.7 respectively, and wCV 2.4% and 6.9%). Results from this work show the level of longitudinal reproducibility that can be expected from qMRI protocols in the brain and spinal cord in the absence of hardware and software upgrades, and could help in the design of future longitudinal clinical studies.
Machine-learning-assisted Preoperative Prediction of Pediatric Appendicitis Severity.
Aylin Erman
Julia Ferreira
Waseem Abu Ashour
Elena Guadagno
Etienne St-Louis
Sherif Emil
Jackie Cheung
Machine-learning-assisted preoperative prediction of pediatric appendicitis severity
Aylin Erman
Julia Ferreira
Waseem Abu Ashour
Elena Guadagno
Etienne St-Louis
Sherif Emil
Jackie Cheung
MLOps, LLMOps, FMOps, and Beyond
Chakkrit Tantithamthavorn
Fabio Palomba
Joselito Joey Chua
MLOps, LLMOps, FMOps, and Beyond
Chakkrit Tantithamthavorn
Fabio Palomba
Joselito Joey Chua
MLOps, LLMOps, FMOps, and Beyond
Chakkrit Kla Tantithamthavorn
Fabio Palomba
Joselito Joey Chua
Multimodal and Force-Matched Imitation Learning with a See-Through Visuotactile Sensor
Trevor Ablett
Oliver Limoyo
Adam Sigal
Affan Jilani
Jonathan Kelly
Francois Hogan
Kinesthetic Teaching is a popular approach to collecting expert robotic demonstrations of contact-rich tasks for imitation learning (IL), bu… (see more)t it typically only measures motion, ignoring the force placed on the environment by the robot. Furthermore, contact-rich tasks require accurate sensing of both reaching and touching, which can be difficult to provide with conventional sensing modalities. We address these challenges with a See-Through-your-Skin (STS) visuotactile sensor, using the sensor both (i) as a measurement tool to improve kinesthetic teaching, and (ii) as a policy input in contact-rich door manipulation tasks. An STS sensor can be switched between visual and tactile modes by leveraging a semi-transparent surface and controllable lighting, allowing for both pre-contact visual sensing and during-contact tactile sensing with a single sensor. First, we propose tactile force matching, a methodology that enables a robot to match forces read during kinesthetic teaching using tactile signals. Second, we develop a policy that controls STS mode switching, allowing a policy to learn the appropriate moment to switch an STS from its visual to its tactile mode. Finally, we study multiple observation configurations to compare and contrast the value of visual and tactile data from an STS with visual data from a wrist-mounted eye-in-hand camera. With over 3,000 test episodes from real-world manipulation experiments, we find that the inclusion of force matching raises average policy success rates by 62.5%, STS mode switching by 30.3%, and STS data as a policy input by 42.5%. Our results highlight the utility of see-through tactile sensing for IL, both for data collection to allow force matching, and for policy execution to allow accurate task feedback.
A Multi-Robot Exploration Planner for Space Applications
Vivek Shankar Vardharajan
A Multi-Robot Exploration Planner for Space Applications
Vivek Shankar Vardharajan
We propose a distributed multi-robot exploration planning method designed for complex, unconstrained environments featuring steep elevation … (see more)changes. The method employs a two-tiered approach: a local exploration planner that constructs a grid graph to maximize exploration gain and a global planner that maintains a sparse navigational graph to track visited locations and frontier information. The global graphs are periodically synchronized among robots within communication range to maintain an updated representation of the environment. Our approach integrates localization loop closure estimates to correct global graph drift. In simulation and field tests, the proposed method achieves 50% lower computational runtime compared to state-of-the-art methods while demonstrating superior exploration coverage. We evaluate its performance in two simulated subterranean environments and in field experiments at a Mars-analog terrain.