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1. Structural Design Through Reinforcement Learning

Author

Rochefort-Beaudoin, Thomas, Vadean, Aurelian, Aage, Niels, and Achiche, Sofiane

Subjects
Computer Science - Artificial Intelligence, 68T07 (Primary), 74P05 (Secondary), J.2, J.6, I.2
Abstract

This paper introduces the Structural Optimization gym (SOgym), a novel open-source Reinforcement Learning (RL) environment designed to advance machine learning in Topology Optimization (TO). SOgym enables RL agents to generate physically viable and structurally robust designs by integrating the physics of TO into the reward function. To enhance scalability, SOgym leverages feature-mapping methods as a mesh-independent interface between the environment and the agent, allowing efficient interaction with the design variables regardless of mesh resolution. Baseline results use a model-free Proximal Policy Optimization agent and a model-based DreamerV3 agent. Three observation space configurations were tested. The TopOpt game-inspired configuration, an interactive educational tool that improves students' intuition in designing structures to minimize compliance under volume constraints, performed best in terms of performance and sample efficiency. The 100M parameter version of DreamerV3 produced structures within 54% of the baseline compliance achieved by traditional optimization methods and a 0% disconnection rate, an improvement over supervised learning approaches that often struggle with disconnected load paths. When comparing the learning rates of the agents to those of engineering students from the TopOpt game experiment, the DreamerV3-100M model shows a learning rate approximately four orders of magnitude lower, an impressive feat for a policy trained from scratch through trial and error. These results suggest RL's potential to solve continuous TO problems and its capacity to explore and learn from diverse design solutions. SOgym provides a platform for developing RL agents for complex structural design challenges and is publicly available to support further research in the field.

Published
2024

2. From Density to Geometry: YOLOv8 Instance Segmentation for Reverse Engineering of Optimized Structures

Author

Rochefort-Beaudoin, Thomas, Vadean, Aurelian, Achiche, Sofiane, and Aage, Niels

Subjects
Computer Science - Computer Vision and Pattern Recognition, Computer Science - Computational Engineering, Finance, and Science, 68T10 (Secondary), 74P05 (Secondary), J.2, J.6, I.4.6, I.5.4
Abstract

This paper introduces YOLOv8-TO, a novel approach for reverse engineering of topology-optimized structures into interpretable geometric parameters using the YOLOv8 instance segmentation model. Density-based topology optimization methods require post-processing to convert the optimal density distribution into a parametric representation for design exploration and integration with CAD tools. Traditional methods such as skeletonization struggle with complex geometries and require manual intervention. YOLOv8-TO addresses these challenges by training a custom YOLOv8 model to automatically detect and reconstruct structural components from binary density distributions. The model is trained on a diverse dataset of both optimized and random structures generated using the Moving Morphable Components method. A custom reconstruction loss function based on the dice coefficient of the predicted geometry is used to train the new regression head of the model via self-supervised learning. The method is evaluated on test sets generated from different topology optimization methods, including out-of-distribution samples, and compared against a skeletonization approach. Results show that YOLOv8-TO significantly outperforms skeletonization in reconstructing visually and structurally similar designs. The method showcases an average improvement of 13.84% in the Dice coefficient, with peak enhancements reaching 20.78%. The method demonstrates good generalization to complex geometries and fast inference times, making it suitable for integration into design workflows using regular workstations. Limitations include the sensitivity to non-max suppression thresholds. YOLOv8-TO represents a significant advancement in topology optimization post-processing, enabling efficient and accurate reverse engineering of optimized structures for design exploration and manufacturing.

Published
2024

3. An Attention-Based Deep Learning Architecture for Real-Time Monocular Visual Odometry: Applications to GPS-free Drone Navigation

Author

Dufour, Olivier Brochu, Mohebbi, Abolfazl, and Achiche, Sofiane

Subjects
Computer Science - Robotics, Computer Science - Computer Vision and Pattern Recognition, Computer Science - Machine Learning, Electrical Engineering and Systems Science - Image and Video Processing
Abstract

Drones are increasingly used in fields like industry, medicine, research, disaster relief, defense, and security. Technical challenges, such as navigation in GPS-denied environments, hinder further adoption. Research in visual odometry is advancing, potentially solving GPS-free navigation issues. Traditional visual odometry methods use geometry-based pipelines which, while popular, often suffer from error accumulation and high computational demands. Recent studies utilizing deep neural networks (DNNs) have shown improved performance, addressing these drawbacks. Deep visual odometry typically employs convolutional neural networks (CNNs) and sequence modeling networks like recurrent neural networks (RNNs) to interpret scenes and deduce visual odometry from video sequences. This paper presents a novel real-time monocular visual odometry model for drones, using a deep neural architecture with a self-attention module. It estimates the ego-motion of a camera on a drone, using consecutive video frames. An inference utility processes the live video feed, employing deep learning to estimate the drone's trajectory. The architecture combines a CNN for image feature extraction and a long short-term memory (LSTM) network with a multi-head attention module for video sequence modeling. Tested on two visual odometry datasets, this model converged 48% faster than a previous RNN model and showed a 22% reduction in mean translational drift and a 12% improvement in mean translational absolute trajectory error, demonstrating enhanced robustness to noise., Comment: 22 Pages, 3 Tables, 9 Figures

Published
2024

4. Semi-Synthetic Dataset Augmentation for Application-Specific Gaze Estimation

Author

Leblond-Menard, Cedric, Picard-Krashevski, Gabriel, and Achiche, Sofiane

Subjects
Computer Science - Computer Vision and Pattern Recognition, Computer Science - Artificial Intelligence, Computer Science - Human-Computer Interaction, Computer Science - Machine Learning
Abstract

Although the number of gaze estimation datasets is growing, the application of appearance-based gaze estimation methods is mostly limited to estimating the point of gaze on a screen. This is in part because most datasets are generated in a similar fashion, where the gaze target is on a screen close to camera's origin. In other applications such as assistive robotics or marketing research, the 3D point of gaze might not be close to the camera's origin, meaning models trained on current datasets do not generalize well to these tasks. We therefore suggest generating a textured tridimensional mesh of the face and rendering the training images from a virtual camera at a specific position and orientation related to the application as a mean of augmenting the existing datasets. In our tests, this lead to an average 47% decrease in gaze estimation angular error., Comment: 5 pages, 5 figures

Published
2023

5. Development of Machine learning algorithms to identify the Cobb angle in adolescents with idiopathic scoliosis based on lumbosacral joint efforts during gait (Case study)

Author

Samadi, Bahare, Raison, Maxime, Mahaudens, Philippe, Detrembleur, Christine, and Achiche, Sofiane

Subjects
Electrical Engineering and Systems Science - Image and Video Processing, Computer Science - Computer Vision and Pattern Recognition, Computer Science - Machine Learning
Abstract

Objectives: To quantify the magnitude of spinal deformity in adolescent idiopathic scoliosis (AIS), the Cobb angle is measured on X-ray images of the spine. Continuous exposure to X-ray radiation to follow-up the progression of scoliosis may lead to negative side effects on patients. Furthermore, manual measurement of the Cobb angle could lead to up to 10{\deg} or more of a difference due to intra/inter observer variation. Therefore, the objective of this study is to identify the Cobb angle by developing an automated radiation-free model, using Machine learning algorithms. Methods: Thirty participants with lumbar/thoracolumbar AIS (15{\deg} < Cobb angle < 66{\deg}) performed gait cycles. The lumbosacral (L5-S1) joint efforts during six gait cycles of participants were used as features to feed training algorithms. Various regression algorithms were implemented and run. Results: The decision tree regression algorithm achieved the best result with the mean absolute error equal to 4.6{\deg} of averaged 10-fold cross-validation. Conclusions: This study shows that the lumbosacral joint efforts during gait as radiation-free data are capable to identify the Cobb angle by using Machine learning algorithms. The proposed model can be considered as an alternative, radiation-free method to X-ray radiography to assist clinicians in following-up the progression of AIS., Comment: 30 pages, 2 Figures, 4 tables

Published
2023

8. Concurrent product layout design optimization and dependency management using a modified NSGA-III approach

Author

Yann-Seing Law-Kam Cio, Giovanni Beltrame, Aurelian Vadean, Abolfazl Mohebbi, and Sofiane Achiche

Subjects
genetic algorithms, modularization algorithm, product-related dependencies, greenhouse design, design optimization, design structure matrix, Mechanical engineering and machinery, TJ1-1570
Abstract

The complexity of mechatronic systems has increased with the significant advancements of technology in their components which makes their design more challenging. This is due to the need for incorporating expertise from different domains as well as the increased number and complexity of components integrated into the product. To alleviate the burden of designing such products, many industries and researchers are attracted to the concept of modularization which is to identify a subset of system components that can form a module. To achieve this, a novel product-related dependency management approach is proposed in this paper with the support of an augmented design structure matrix. This approach makes it possible to model positive and negative dependencies and to compute the combination potency between components to form modules. This approach is then integrated into a modified non-dominated sorting genetic algorithm III to concurrently optimize the design and identify the modules. The methodology is exemplified through the case study of a layout design of an automatic greenhouse. By applying the proposed methodology to the case study, it was possible to generate concepts that decreased the number of modules from 9 down to 4 while ensuring the optimization of the design performance.

Published
2024
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9. A Test Bench For Evaluating Exoskeletons For Upper Limb Rehabilitation

Author

Nguiadem, Clautilde, Raison, Maxime, and Achiche, Sofiane

Subjects
Computer Science - Robotics, I.6.4, I.6.3
Abstract

The potential of wearable robotics technology is undeniable. However, quantifying its value is difficult. Various types of exoskeleton robots have already been developed and tested for upper limb rehabilitation but, evaluations are not standardized, particularly in pediatric rehabilitation. This paper proposes a methodology for the quantitative evaluation of upper limb exoskeletons that, like a test bench, would serve for replicable testing. We determined the range of motion (ROM) and joint torques using both kinematic modeling and experimental measurements (using sensors integrated into Dynamixel actuators). The proposed test bench can provide an accurate range of motion (ROM) and joint torques during the pronation-supination (PS) task. The range of motion obtained with the physical prototype was approximately 156.26 +- 4.71{\deg} during the PS task, while it was approximately 146.84 +- 14.32{\deg} for the multibody model. The results show that the average range of experimental torques (0.28 +- 0.06 N.m) was overestimated by 40% and just 3.4%, respectively, when compared to the average range of simulated torques (0.2 +- 0.05 N.m) and to the highest range of simulated torques (0.29 N.m). For the experimental measurements, test-retest reliability was excellent (0.96-0.98) within sessions and excellent (0.93) or good (0.81-0.86) between sessions. Finally, the suggested approach provides a ROM close to the normal ROM necessary during PS tasks. These results validate the measurements' accuracy and underline the proposed methodology's relevance. The proposed test bench could become a reference standard for evaluating exoskeletons. This study also addresses a methodological aspect on the accurate assessment of joint torques that can serve in applications such as the sizing of actuators in exoskeletons or the non-invasive evaluation of muscle forces in the human body., Comment: 35 pages and 7 figures

Published
2021

11. A Fuzzy-based Framework to Support Multicriteria Design of Mechatronic Systems

Author

Mohebbi, Abolfazl, Achiche, Sofiane, and Baron, Luc

Subjects
Computer Science - Robotics, Electrical Engineering and Systems Science - Systems and Control
Abstract

Designing a mechatronic system is a complex task since it deals with a high number of system components with multi-disciplinary nature in the presence of interacting design objectives. Currently, the sequential design is widely used by designers in industries that deal with different domains and their corresponding design objectives separately leading to a functional but not necessarily an optimal result. Consequently, the need for a systematic and multi-objective design methodology arises. A new conceptual design approach based on a multi-criteria profile for mechatronic systems has been previously presented by the authors which uses a series of nonlinear fuzzy-based aggregation functions to facilitate decision-making for design evaluation in the presence of interacting criteria. Choquet fuzzy integrals are one of the most expressive and reliable preference models used in decision theory for multicriteria decision making. They perform a weighted aggregation by the means of fuzzy measures assigning a weight to any coalition of criteria. This enables the designers to model importance and also interactions among criteria thus covering an important range of possible decision outcomes. However, specification of the fuzzy measures involves many parameters and is very difficult when only relying on the designer's intuition. In this paper, we discuss three different methods of fuzzy measure identification tailored for a mechatronic design process and exemplified by a case study of designing a vision-guided quadrotor drone. The results obtained from each method are discussed in the end.

Published
2020
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18. Adapting and Evaluating an AI-Based Chatbot Through Patient and Stakeholder Engagement to Provide Information for Different Health Conditions: Master Protocol for an Adaptive Platform Trial (the MARVIN Chatbots Study)

Author

Yuanchao Ma, Sofiane Achiche, Marie-Pascale Pomey, Jesseca Paquette, Nesrine Adjtoutah, Serge Vicente, Kim Engler, Moustafa Laymouna, David Lessard, Benoît Lemire, Jamil Asselah, Rachel Therrien, Esli Osmanlliu, Ma'n H Zawati, Yann Joly, and Bertrand Lebouché

Subjects
Medicine, Computer applications to medicine. Medical informatics, R858-859.7
Abstract

BackgroundArtificial intelligence (AI)–based chatbots could help address some of the challenges patients face in acquiring information essential to their self-health management, including unreliable sources and overburdened health care professionals. Research to ensure the proper design, implementation, and uptake of chatbots is imperative. Inclusive digital health research and responsible AI integration into health care require active and sustained patient and stakeholder engagement, yet corresponding activities and guidance are limited for this purpose. ObjectiveIn response, this manuscript presents a master protocol for the development, testing, and implementation of a chatbot family in partnership with stakeholders. This protocol aims to help efficiently translate an initial chatbot intervention (MARVIN) to multiple health domains and populations. MethodsThe MARVIN chatbots study has an adaptive platform trial design consisting of multiple parallel individual chatbot substudies with four common objectives: (1) co-construct a tailored AI chatbot for a specific health care setting, (2) assess its usability with a small sample of participants, (3) measure implementation outcomes (usability, acceptability, appropriateness, adoption, and fidelity) within a large sample, and (4) evaluate the impact of patient and stakeholder partnerships on chatbot development. For objective 1, a needs assessment will be conducted within the setting, involving four 2-hour focus groups with 5 participants each. Then, a co-construction design committee will be formed with patient partners, health care professionals, and researchers who will participate in 6 workshops for chatbot development, testing, and improvement. For objective 2, a total of 30 participants will interact with the prototype for 3 weeks and assess its usability through a survey and 3 focus groups. Positive usability outcomes will lead to the initiation of objective 3, whereby the public will be able to access the chatbot for a 12-month real-world implementation study using web-based questionnaires to measure usability, acceptability, and appropriateness for 150 participants and meta-use data to inform adoption and fidelity. After each objective, for objective 4, focus groups will be conducted with the design committee to better understand their perspectives on the engagement process. ResultsFrom July 2022 to October 2023, this master protocol led to four substudies conducted at the McGill University Health Centre or the Centre hospitalier de l’Université de Montréal (both in Montreal, Quebec, Canada): (1) MARVIN for HIV (large-scale implementation expected in mid-2024), (2) MARVIN-Pharma for community pharmacists providing HIV care (usability study planned for mid-2024), (3) MARVINA for breast cancer, and (4) MARVIN-CHAMP for pediatric infectious conditions (both in preparation, with development to begin in early 2024). ConclusionsThis master protocol offers an approach to chatbot development in partnership with patients and health care professionals that includes a comprehensive assessment of implementation outcomes. It also contributes to best practice recommendations for patient and stakeholder engagement in digital health research. Trial RegistrationClinicalTrials.gov NCT05789901; https://classic.clinicaltrials.gov/ct2/show/NCT05789901 International Registered Report Identifier (IRRID)PRR1-10.2196/54668

Published
2024
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19. Improving Convolutional Neural Networks Via Conservative Field Regularisation and Integration

Author

Beaini, Dominique, Achiche, Sofiane, and Raison, Maxime

Subjects
Computer Science - Computer Vision and Pattern Recognition, Computer Science - Artificial Intelligence
Abstract

Current research in convolutional neural networks (CNN) focuses mainly on changing the architecture of the networks, optimizing the hyper-parameters and improving the gradient descent. However, most work use only 3 standard families of operations inside the CNN, the convolution, the activation function, and the pooling. In this work, we propose a new family of operations based on the Green's function of the Laplacian, which allows the network to solve the Laplacian, to integrate any vector field and to regularize the field by forcing it to be conservative. Hence, the Green's function (GF) is the first operation that regularizes the 2D or 3D feature space by forcing it to be conservative and physically interpretable, instead of regularizing the norm of the weights. Our results show that such regularization allows the network to learn faster, to have smoother training curves and to better generalize, without any additional parameter. The current manuscript presents early results, more work is required to benchmark the proposed method., Comment: 11 pages, 3 figures

Published
2020

20. Saliency Enhancement using Gradient Domain Edges Merging

Author

Beaini, Dominique, Achiche, Sofiane, Duperre, Alexandre, and Raison, Maxime

Subjects
Computer Science - Computer Vision and Pattern Recognition
Abstract

In recent years, there has been a rapid progress in solving the binary problems in computer vision, such as edge detection which finds the boundaries of an image and salient object detection which finds the important object in an image. This progress happened thanks to the rise of deep-learning and convolutional neural networks (CNN) which allow to extract complex and abstract features. However, edge detection and saliency are still two different fields and do not interact together, although it is intuitive for a human to detect salient objects based on its boundaries. Those features are not well merged in a CNN because edges and surfaces do not intersect since one feature represents a region while the other represents boundaries between different regions. In the current work, the main objective is to develop a method to merge the edges with the saliency maps to improve the performance of the saliency. Hence, we developed the gradient-domain merging (GDM) which can be used to quickly combine the image-domain information of salient object detection with the gradient-domain information of the edge detection. This leads to our proposed saliency enhancement using edges (SEE) with an average improvement of the F-measure of at least 3.4 times higher on the DUT-OMRON dataset and 6.6 times higher on the ECSSD dataset, when compared to competing algorithm such as denseCRF and BGOF. The SEE algorithm is split into 2 parts, SEE-Pre for preprocessing and SEE-Post pour postprocessing.

Published
2020

21. Design of an assistive trunk exoskeleton based on multibody dynamic modelling

Author

Li, Pierre Lifeng, Achiche, Sofiane, Blanchet, Laurent, Lecours, Samuel, and Raison, Maxime

Subjects
Physics - Medical Physics, Computer Science - Robotics
Abstract

Low back pain is one of the most common musculoskeletal disorder. To reduce its incidences, some back exoskeletons have been designed and already commercialized. However, there is a gap between the phases of device and testing on subjects. In fact, the main unsolved problem is the lack of realistic simulation of human-device interaction. Consequently, the objective of this paper is to design a 3D multibody model of the human body that includes the full thoracic and lumbar spine combined with a low back exoskeleton, enabling to analyze the interactions between them. The results highlight that the use of the exoskeleton reduces the net torque in the lower lumbar spine but creates normal forces transmitted through the thighs and the pelvis, which should also be considered for low back pain. As a perspective, this model would enable to redesign low back exoskeletons reducing both torques and forces in the human joints in a realistic dynamic context., Comment: 7 pages, 6 figures

Published
2019

22. Deep Green Function Convolution for Improving Saliency in Convolutional Neural Networks

Author

Beaini, Dominique, Achiche, Sofiane, Duperré, Alexandre, and Raison, Maxime

Subjects
Computer Science - Computer Vision and Pattern Recognition, Computer Science - Machine Learning
Abstract

Current saliency methods require to learn large scale regional features using small convolutional kernels, which is not possible with a simple feed-forward network. Some methods solve this problem by using segmentation into superpixels while others downscale the image through the network and rescale it back to its original size. The objective of this paper is to show that saliency convolutional neural networks (CNN) can be improved by using a Green's function convolution (GFC) to extrapolate edges features into salient regions. The GFC acts as a gradient integrator, allowing to produce saliency features by filling thin edges directly inside the CNN. Hence, we propose the gradient integration and sum (GIS) layer that combines the edges features with the saliency features. Using the HED and DSS architecture, we demonstrated that adding a GIS layer near the network's output allows to reduce the sensitivity to the parameter initialization, to reduce the overfitting and to improve the repeatability of the training. By simply adding a GIS layer to the state-of-the-art DSS model, there is an absolute increase of 1.6% for the F-measure on the DUT-OMRON dataset, with only 10ms of additional computation time. The GIS layer further allows the network to perform significantly better in the case of highly noisy images or low-brightness images. In fact, we observed an F-measure improvement of 5.2% when noise was added to the dataset and 2.8% when the brightness was reduced. Since the GIS layer is model agnostic, it can be implemented into different fully convolutional networks. A major contribution of the current work is the first implementation of Green's function convolution inside a neural network, which allows the network to operate in the feature domain and in the gradient domain at the same time, thus improving the regional representation via edge filling., Comment: 15 pages, 11 figures

Published
2019
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24. Non-Intrusive Real Time Eye Tracking Using Facial Alignment for Assistive Technologies

Author

C. Leblond-Menard and S. Achiche

Subjects
Gaze estimation, human computer interaction, assistive technology, neural networks, Medical technology, R855-855.5, Therapeutics. Pharmacology, RM1-950
Abstract

Most affordable eye tracking systems use either intrusive setup such as head-mounted cameras or use fixed cameras with infrared corneal reflections via illuminators. In the case of assistive technologies, using intrusive eye tracking systems can be a burden to wear for extended periods of time and infrared based solutions generally do not work in all environments, especially outside or inside if the sunlight reaches the space. Therefore, we propose an eye-tracking solution using state-of-the-art convolutional neural network face alignment algorithms that is both accurate and lightweight for assistive tasks such as selecting an object for use with assistive robotics arms. This solution uses a simple webcam for gaze and face position and pose estimation. We achieve a much faster computation time than the current state-of-the-art while maintaining comparable accuracy. This paves the way for accurate appearance-based gaze estimation even on mobile devices, giving an average error of around 4.5° on the MPIIGaze dataset (Zhang et al., 2019) and state-of-the-art average errors of 3.9° and 3.3° on the UTMultiview (Sugano et al., 2014) and GazeCapture (Krafka et al., 2016; Park et al., 2019) datasets respectively, while achieving a decrease in computation time of up to 91%.

Published
2023
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25. Fast and Optimal Laplacian Solver for Gradient-Domain Image Editing using Green Function Convolution

Author

Beaini, Dominique, Achiche, Sofiane, Nonez, Fabrice, Dufour, Olivier Brochu, Leblond-Ménard, Cédric, Asaadi, Mahdis, and Raison, Maxime

Subjects
Computer Science - Computer Vision and Pattern Recognition, Computer Science - Discrete Mathematics
Abstract

In computer vision, the gradient and Laplacian of an image are used in different applications, such as edge detection, feature extraction, and seamless image cloning. Computing the gradient of an image is straightforward since numerical derivatives are available in most computer vision toolboxes. However, the reverse problem is more difficult, since computing an image from its gradient requires to solve the Laplacian equation, also called Poisson equation. Current discrete methods are either slow or require heavy parallel computing. The objective of this paper is to present a novel fast and robust method of solving the image gradient or Laplacian with minimal error, which can be used for gradient domain editing. By using a single convolution based on a numerical Green's function, the whole process is faster and straightforward to implement with different computer vision libraries. It can also be optimized on a GPU using fast Fourier transforms and can easily be generalized for an n dimension image. The tests show that, for images of resolution 801x1200, the proposed GFC can solve 100 Laplacian in parallel in around 1.0 milliseconds ms. This is orders of magnitude faster than our nearest competitor which requires 294ms for a single image. Furthermore, we prove mathematically and demonstrate empirically that the proposed method is the least error solver for gradient domain editing. The developed method is also validated with examples of Poisson blending, gradient removal, and the proposed gradient domain merging GDM. Finally, we present how the GDM can be leveraged in future works for convolutional neural networks CNN., Comment: 17 pages, single column scientific paper. Patent submitted

Published
2019

26. Novel Convolution Kernels for Computer Vision and Shape Analysis based on Electromagnetism

Author

Beaini, Dominique, Achiche, Sofiane, Cio, Yann-Seing Law-Kam, and Raison, Maxime

Subjects
Computer Science - Computer Vision and Pattern Recognition
Abstract

Computer vision is a growing field with a lot of new applications in automation and robotics, since it allows the analysis of images and shapes for the generation of numerical or analytical information. One of the most used method of information extraction is image filtering through convolution kernels, with each kernel specialized for specific applications. The objective of this paper is to present a novel convolution kernels, based on principles of electromagnetic potentials and fields, for a general use in computer vision and to demonstrate its usage for shape and stroke analysis. Such filtering possesses unique geometrical properties that can be interpreted using well understood physics theorems. Therefore, this paper focuses on the development of the electromagnetic kernels and on their application on images for shape and stroke analysis. It also presents several interesting features of electromagnetic kernels, such as resolution, size and orientation independence, robustness to noise and deformation, long distance stroke interaction and ability to work with 3D images, Comment: Keywords: Shape analysis; Stroke analysis; Computer vision; Electromagnetic potential field; Feature extraction; Image filtering; Image convolution Published in PolyPublie: https://publications.polymtl.ca/3162/

Published
2018

27. Computing the Spatial Probability of Inclusion inside Partial Contours for Computer Vision Applications

Author

Beaini, Dominique, Achiche, Sofiane, Nonez, Fabrice, and Raison, Maxime

Subjects
Computer Science - Computer Vision and Pattern Recognition, Mathematics - Numerical Analysis
Abstract

In Computer Vision, edge detection is one of the favored approaches for feature and object detection in images since it provides information about their objects boundaries. Other region-based approaches use probabilistic analysis such as clustering and Markov random fields, but those methods cannot be used to analyze edges and their interaction. In fact, only image segmentation can produce regions based on edges, but it requires thresholding by simply separating the regions into binary in-out information. Hence, there is currently a gap between edge-based and region-based algorithms, since edges cannot be used to study the properties of a region and vice versa. The objective of this paper is to present a novel spatial probability analysis that allows determining the probability of inclusion inside a set of partial contours (strokes). To answer this objective, we developed a new approach that uses electromagnetic convolutions and repulsion optimization to compute the required probabilities. Hence, it becomes possible to generate a continuous space of probability based only on the edge information, thus bridging the gap between the edge-based methods and the region-based methods. The developed method is consistent with the fundamental properties of inclusion probabilities and its results are validated by comparing an image with the probability-based estimation given by our algorithm. The method can also be generalized to take into consideration the intensity of the edges or to be used for 3D shapes. This is the first documented method that allows computing a space of probability based on interacting edges, which opens the path to broader applications such as image segmentation and contour completion., Comment: Keywords: Computer vision; Stroke analysis; Partial contour; Probability of inclusion; Edge interaction; Image convolution; Electromagnetic potential field

Published
2018

31. Complexity-driven layout exploration for aircraft structures

Author

Jean-François Gamache, Aurelian Vadean, Mario Capo, Thomas Rochefort-Beaudoin, Nicolas Dodane, and Sofiane Achiche

Subjects
Aircraft Design, Layout Optimization, Generative Design, Complexity Metric, Axiomatic Design, Drawing. Design. Illustration, NC1-1940, Engineering design, TA174
Abstract

Topology optimization has been identified as a powerful tool to improve aircraft structures for many years. Yet, innovative layouts have not been successfully implemented in commercial aircraft for several reasons. One reason identified by our research group is the lack of design constraints during topology optimization, such as buckling stability, which yields complex solutions that are not easily manufacturable. Second, the complexity of the resulting layouts makes integration with other systems highly challenging. With respect to these challenges, we propose a new heuristic layout optimization process: complexity-driven layout exploration for aircraft structures (CD-LEAS). The new process addresses the challenges of complexity and nonlinear constraints, such as buckling, in aircraft structure layout optimization. The novelty of CD-LEAS comes from the integration of a relative complexity metric as a driver to navigate the design space efficiently. Two case studies of commonly used stiffened panels are carried out to showcase the performance of the process. The results show that using complexity to navigate an explicit design space allows our process to quickly output a family of simple, light, stiff and buckling-resistant layouts.

Published
2023
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41. The first AI‐based Chatbot to promote HIV self‐management: A mixed methods usability study.

Author

Ma, Yuanchao, Achiche, Sofiane, Tu, Gavin, Vicente, Serge, Lessard, David, Engler, Kim, Lemire, Benoît, Laymouna, Moustafa, Pokomandy, Alexandra, Cox, Joseph, and Lebouché, Bertrand

Subjects
*CHATBOTS, *ARTIFICIAL intelligence, *INTELLIGENT personal assistants, *DIGITAL health, *CELL phones
Abstract

Background Methods Results Conclusions We developed MARVIN, an artificial intelligence (AI)‐based chatbot that provides 24/7 expert‐validated information on self‐management‐related topics for people with HIV. This study assessed (1) the feasibility of using MARVIN, (2) its usability and acceptability, and (3) four usability subconstructs (perceived ease of use, perceived usefulness, attitude towards use, and behavioural intention to use).In a mixed‐methods study conducted at the McGill University Health Centre, enrolled participants were asked to have 20 conversations within 3 weeks with MARVIN on predetermined topics and to complete a usability questionnaire. Feasibility, usability, acceptability, and usability subconstructs were examined against predetermined success thresholds. Qualitatively, randomly selected participants were invited to semi‐structured focus groups/interviews to discuss their experiences with MARVIN. Barriers and facilitators were identified according to the four usability subconstructs.From March 2021 to April 2022, 28 participants were surveyed after a 3‐week testing period, and nine were interviewed. Study retention was 70% (28/40). Mean usability exceeded the threshold (69.9/68), whereas mean acceptability was very close to target (23.8/24). Ratings of attitude towards MARVIN's use were positive (+14%), with the remaining subconstructs exceeding the target (5/7). Facilitators included MARVIN's reliable and useful real‐time information support, its easy accessibility, provision of convivial conversations, confidentiality, and perception as being emotionally safe. However, MARVIN's limited comprehension and the use of Facebook as an implementation platform were identified as barriers, along with the need for more conversation topics and new features (e.g., memorization).The study demonstrated MARVIN's global usability. Our findings show its potential for HIV self‐management and provide direction for further development. [ABSTRACT FROM AUTHOR]

Published
2024
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42. Low incidence of SARS-CoV-2, risk factors of mortality and the course of illness in the French national cohort of dialysis patients

Author

Abbassi, Abdelhamid, Debure, Alain, Guerraoui, Abdallah, Benmoussa, Abdelatif, Hamani, Abdelaziz, Ziane, Abdelaziz, Nefti, Abdelhamid, Hadj, Abdelkader, El Amari, Abderrahim, Ghazali, Abderrahmane, Abd El Fatah Mohamed, Abo Bakr, Laradi, Achour, Ben Ahmed, Adel, Sahar, Adel, Pillet, Adele, Lacraz, Adeline, Moinat, Adnan, Massoumi, Afshin, Pardon, Agathe, Beaudoin, Agnes Caillette, Debout, Agnes Chapelet, Mariot, Agnes, Rachi, Ahmed, Afiani, Aida, Boula, Aime Remy, Jalaby, Al, Cremault, Alain, Fournier, Alain, Jeanson, Alain, Lyon, Alain, Nony, Alain, Robert, Alain, Slingeneyer, Alain, Labatide, Alanor Agnes, Sartorius, Albane Brodin, Bensman, Albert, Fournier, Albert, Ranlin, Alex, Sandor, Alex Vido, Colombo, Alexandra, Duhem, Alexandra, Stancu, Alexandra, Dufay, Alexandre, Dumoulin, Alexandre, Ebel, Alexandre, Klein, Alexandre, Martin, Alexandre, Mouneimne, Alexandre, Seidowsky, Alexandre, De Martin, Alfio, Zannier, Alfredo, Aizel, Ali, Hafi, Ali, Diddaoui, Ali Zineddine, Heyani, Alim, Mocanu, Alina, Preda, Alina, Hafi, Aline, Talaszka, Aline, Duquesne, Alyette, Amaouche, Amar, Ghemmour, Amel, Simon, Amelie, Skalli, Amina, Boukadida, Amine, Ragab Eid, Amr Ekhlas, Fedorca, Ana, Baillet, Anabelle, Poyet, Anais, Giorgita, Ancuta Bouffandeau, Ratsimbazafy, Anderson, Pruna, Andre, Argiles, Angel, Testa, Angelo, Vandooren, Ann Karolien, Jolivot, Anne, Labadens, Anne Kolko, Lataste, Anne, Maisin, Anne, Paris, Anne, Sechet, Anne, Wuillai, Anne, Heng, Anne Elisabeth, Josse, Anne Gaelle, Querard, Anne Helene, Reboux, Anne Helene, Adra, Anne Laure, Faller, Anne Laure, Leclerc, Anne Laure, Poitou, Anne Laure, Manucci, Annie Lahoche, Jacquet, Antoine, Pommereau, Antoine, Thierry, Antoine, Adem, Arezki, Chapelet, Arielle, Del Bello, Arnaud, Delezire, Arnaud, Garnier, Arnaud, Guerard, Arnaud, Klisnick, Arnaud, Lionet, Arnaud, Roccabianca, Arnaud, Stolz, Arnaud, Capdeville, Arthur, Allal, Asma, Alrifai, Assem, Diarrassouba, Assetou, Djema, Assia, Carre, Assia Ferhat, Dubrasquet, Astrid Godron, Elmrabet, Atman Haddj, Jegado, Audrey, Thomas, Aurelia Bertholet, Salandre, Aurelie Davourie, Pajot, Aurelie, Lorthioir, Aurelien, Tiple, Aurelien, Sury, Aurore, Abokasem, Ayman, Sarraj, Ayman, Henaoui, Bachir, Chaghouri, Baher, Wehbe, Bassem, Ball, Beatrice, Viron, Beatrice, Issad, Belkassem, Corne, Benedicte Hodemon, Janbon, Benedicte, Deroure, Benjamin, Savenkoff, Benjamin, Jonon, Benoit, Vendrely, Benoit, Djelaleddine, Benyakoub, Ohry, Bernard, Painchart, Bernard, Strullu, Bernard, Temperville, Bernard, Ebikili, Bertin, Hacq, Bertrand, Morel, Bertrand, Aoun, Bilal, Muniz, Blanca, Chlih, Bouchra, Amara, Brahim, Mayor, Brice, Gilson, Brigitte, Llanas, Brigitte, Zins, Brigitte, Bourgeon, Bruno, Coevoet, Bruno, Guery, Bruno, Legallicier, Bruno, Paris, Bruno, Ranchin, Bruno, Seigneuric, Bruno, Dita, Camelia Ghiciuc, Prelipcean, Camelia, Hottelart, Carine Achard, Diet, Carine, Frangie, Carlos, Vela, Carlos, Muresan, Carmina, Deprele, Carole, Araujo, Caroline, Bidault, Caroline, Creput, Caroline, Delclaux, Caroline, Du Halgouet, Caroline, Favennec, Caroline, Freguin, Caroline, Vercel, Caroline Gourraud, Mesguen, Caroline, Obama, Caroline Ndomo, Poitou, Caroline, Dirhold, Caroline Preissig, Roubiou, Caroline, Albert, Catherine, Bessin, Catherine, De Marion Gaja, Catherine, Godart, Catherine, Lasseur, Catherine, Leocardi, Catherine, Lumbroso, Catherine, Melander, Catherine, Michel, Catherine, Maurouard, Catherine Quere, Rouannet, Catherine, Taddei, Catherine, Verove, Cathy, Guiraud, Cecile, Tafelin, Cecile, Baron, Cecile Turc, Formet, Cedric, Pinier, Cedric, De Ste Foy, Celia Lessore, Granolleras, Celine, Bennini, Chaouki, Cartou, Charles, Chazot, Charles, Jouzel, Charlotte, Badid, Cherif, Roubicek, Christa, Viaud, Christel, Verrier, Christelle, Chuet, Christian, Combe, Christian, Dabot, Christian, Duvic, Christian, Emond, Christian, Lagarde, Christian, Lamotte, Christian, Pain, Christian, Mousson, Christiane, Lorriaux, Christie, Beauchamp, Christine, Fumeron, Christine, Le Gurun, Christine, Leroy, Christine, Pietrement, Christine, Richer, Christine, Bouaka, Christophe, Charasse, Christophe, Goupy, Christophe, Ridel, Christophe, Castrale, Cindy, Detourne, Cindy, Francois, Clair, Presne, Claire, Trivin, Claire, Von Kotze, Clarissa, Bernard, Claude, Bonniol, Claude, Desvergnes, Claude, Raharivelina, Claude, Nistor, Claudia, Gueret, Claudine, Lloret, Claudine, Saltiel, Claudine, Rosati, Clelia, Rabate, Clementine, Stanescu, Corina, Ferrandini, Corinne, Guibergia, Corinne, Lemoine, Corinne, Passeron, Corinne, Kahil, Cynthia, Garrouste, Cyril, Van, Cyril Vo, Jolimoy, Cyrille, Kesraoui, Dalila, Jolly, Damien, Thibaudin, Damien, Teboulle, Dan, Daubresse, Daniel, Louvet, Daniel, Rasamimanantsoa, Daniel, Toledano, Daniel, Babici, Daniela, David, Daniela, Dincu, Daniela, Bruno, Danielle, May, Delia, Haussaire, Delphine, Viprey, Delphine Henriet, Bugnon, Denis, Fouque, Denis, Morin, Denis, Nour, Derradji, Mahmoud, Diab Mohamed, Cristescu, Diana Istrati, Aguilera, Didier, Coste, Didier, Hamel, Didier, Le Chapois, Didier, Testou, Didier, Erbilgin, Dilaver, Dahmane, Djamal, Quang, Doan Bui, Bertrand, Dominique, Besnier, Dominique, Blanchier, Dominique, Briffa, Dominique, Caux, Dominique, Durand, Dominique, Fleury, Dominique, Guerrot, Dominique, Hestin, Dominique, Jaubert, Dominique, Joly, Dominique, Lombart, Dominique, Pagniez, Dominique, Pierre, Dominique, Schohn, Dominique, Ikonga, Donatien, Visanica, Dorina, Bazin, Dorothee, Boury, Edouard, Maksour, Edouard, Agbonon, Ekoue, Harrami, Elarbi, Marcu, Elena, Tudorache, Elena, Caniot, Elisabeth, Semjen, Elisabeth, Tomkiewicz, Elisabeth, Scheidt, Elise, Gaboriau, Elke, Lamouroux, Elodie, Guiard, Elsa, Passos, Elsa Martin, Nsembani, Emerson, Fache, Emilie, Kalbacher, Emilie, Pambrun, Emilie, Pincon, Emilie, Launay, Emma Allain, Baron, Emmanuel, Dupuis, Emmanuel, Villar, Emmanuel, Charlin, Emmanuelle, Hecquet, Emmanuelle, Kohler, Emmanuelle, Laurain, Emmanuelle, Rosier, Emmanuelle, Figueroa, Enrique, Azoulay, Eric, Canivet, Eric, Daugas, Eric, Gauthier, Eric, Laruelle, Eric, Le Guen, Eric, Legrand, Eric, Moumas, Eric, Postec, Eric, Prinz, Eric, Renaudineau, Eric, Desport, Estelle, Sutra, Estelle Ricard, Berard, Etienne, Ged, Etienne, Robin, Etienne, Vilaine, Eve, Bargas, Evelyne, Namara, Evelyne Mac, Combarnous, François, Yazbeck, Fatima, Gerard, Fabien, Metivier, Fabien, Parazols, Fabien, Soulis, Fabien, Garnier, Fabrice, Messaoudene, Fadhila Pech, Haidar, Fadi, Boullenger, Fanny, Lepeytre, Fanny, Leroy, Fanny, Frejate, Fares, Bellahsene, Farid, Bellhasene, Farid, Saidani, Farid, Toure, Fatouma, Kriaa, Faycal, Nemmar, Fazia, Vetromile, Fernando, Chalmin, Florence, Lucats, Florence, Sens, Florence, Villemain, Florence, Plasse, Florent, Lebhour, Fouad, Schillinger, Francis, Berge, Franck, Bourdon, Franck, Bridoux, Franck, Reynaud, Franck, Babinet, Francois, Basse, Francois, Chantrel, Francois, Clair, Francois, Coulomb, Francois, De Cornelissen, Francois, Glowacki, Francois, Marchal, Francois, Maurice, Francois, Nobili, Francois, Pourreau, Francois, Provot, Francois, Amani, Francois Roux, Broux, Francoise, Bulte, Francoise, Heibel, Francoise, Leonetti, Francoise, Schott, Francoise Moussion, Le Roy, Frank, Besson, Frederic, Lavainne, Frederic, Tollis, Frederic, Bocquentin, Frederique, Meeus, Frederique, Vecina, Frederique, Von Ey, Friederike, Balit, Gabriel, Choukroun, Gabriel, Gruget, Gabriel, Huchard, Gabriel, Golea, Gabriella, Duneau, Gabrielle, Lefrancois, Gaelle, Pelle, Gaelle, Lebrun, Gaetan, Dumont, Genevieve, Brillet, Georges, Deschenes, Georges, Mourad, Georges, Stamatakis, Georges, Cazajous, Geraldine, D'ythurbide, Geraldine, Wiart, Geraldine Robitaille, Cardon, Gerard, Champion, Gerard, Deschodt, Gerard, Mangenot, Gerard, Motte, Gerard, Schortgen, Gerard, Boulahia, Ghada, Maakaroun, Ghassan, Michel, Ghylene Bourdat, Zanetta, Gilbert, Hufnagel, Gilles, Messier, Gilles, Piccoli, Giorgina, Desvergnes, Gregoire Couvrat, Bobrie, Guillaume, Bonnard, Guillaume, Clement, Guillaume, Jean, Guillaume, Queffeulou, Guillaume, Seret, Guillaume, Vernin, Guillaume, Delavaud, Guy, Lambrey, Guy, Rostoker, Guy, Poussard, Gwenaelle, Kesler, Gwenaelle Roussey, Leon, H., Aboubekr, Habib, Boulechfar, Hacene, Sekhri, Hacene, Hebibi, Hadia, Benalia, Hadjira, Fessi, Hafed, Atchia, Hafsabhai, Bittar, Haiat, Maiza, Hakim, Mazouz, Hakim, El Ali, Hamid, Bougrida, Hammouche, Van Der Pijl, Hans, Lokmane, Hassan, Izzedine, Hassane, Adda, Hassen, De Preneuf, Helene, Leray, Helene, Philippot, Helene, Boulanger, Henri, Merault, Henri, Renaud, Henri, Bonarek, Herve, Maheut, Herve, Nzeyimana, Hilaire, Mehama, Hocine, Zaidi, Hocine, Weclawiak, Hugo, Flodrops, Hugues, Karaaslan, Huseyin, Haskour, Ibrahim, Belhadj, Ihssen, Almoubarak, Imad, Haddad, Imad, Castellano, Ines, Ferrandiz, Ines, Daniliuc, Ioana, Darie, Ioana, Enache, Ioana, Prunescu, Ionut, Djiconkpode, Irenee, Shahapuni, Irina, Bouchoule, Isabelle, Devriendt, Isabelle, Kazes, Isabelle, Kolb, Isabelle, Landru, Isabelle, Poli, Isabelle, Rey, Isabelle, Segalen, Isabelle, Selcer, Isabelle, Vernier, Isabelle, Vrillon, Isabelle, Guenifi, Ismahane, Gheerbrandt, J. Dominique, Potier, Jacky, Becart, Jacques, Cledes, Jacques, Ducros, Jacques, Duvic, Jacques, Fourcade, Jacques, Gaultier, Jacques, Jurine, Jacques, Lebleu, Jacques, Ollier, Jacques, Charles, Jacques Ibsen, Yazji, Jamal, Mansour, Janette, Arnautou, Jean, Brocard, Jean, Carolfi, Jean, Montoriol, Jean, Gouin, Jean Baptiste, Palcoux, Jean Bernard, Bendini, Jean Christophe, Aldigier, Jean Claude, Alphonse, Jean Claude, Delbet, Jean Daniel, Bonne, Jean Francois, Cantin, Jean Francois, De Fremont, Jean Francois, Dessassis, Jean Francois, Subra, Jean Francois, Valentin, Jean Francois, Verdier, Jean Francois, Dion, Jean Jacques, Haultier, Jean Jacques, Montseny, Jean Jacques, Bacri, Jean Louis, Bouchet, Jean Louis, Mahe, Jean Luc, Chalopin, Jean Marc, Gabriel, Jean Marc, Hurot, Jean Marc, Lanau, Jean Marc, Batho, Jean Marie, Coulibaly, Jean Marie, Hardin, Jean Michel, Marc, Jean Michel, Poux, Jean Michel, Rebibou, Jean Michel, Tivollier, Jean Michel, Ottavioli, Jean Noel, Faucon, Jean Paul, Imiela, Jean Paul, Jaulin, Jean Paul, Masselot, Jean Paul, Ortiz, Jean Paul, Bourdenx, Jean Philippe, Devaux, Jean Philippe, Hammelin, Jean Philippe, Rivory, Jean Pierre, Wauquier, Jean Pierre, Larue, Jean Rene, Mondain, Jean Rene, Borde, Jean Sebastien, Virot, Jean Simon, Bosc, Jean Yves, Achiche, Jedjiga, Parasote, Jennifer, Diolez, Jeremie, Harambat, Jerome, Potier, Jerome, Sampol, Jerome, Mustel, Jihad, Lefevre, Jean Jacques, Maurizi, Jocelyne, Gamberoni, Joel, Claudeon, Joelle, Terzic, Joelle, Rogol, Joffrey, Sayegh, Johnny, Cardozo, Jorge, Brasseur, Jose, Guiserix, Jose, Barsumau, Joseph, Albaret, Julie, Beaume, Julie, Attias, Julie Sohier, Dehay, Julien, Hogan, Julien, Journet, Julien, Ott, Julien, Baleynaud, Juliette, Bacchetta, Justine, Faucher, Justine, Yousfi, Kamel, Dardim, Karim, Clabault, Karine, Moreau, Karine, Thomas, Kedna, Sirajedine, Khaled, Chedid, Khalil, El Kaeoui, Khalil, El Karoui, Khalil, Bouachi, Khedidja, Hue, Kheira, El Nasser, Khuzama, Akposso, Kodso, Kunz, Kristian, Bijak, Krzysztof, Kihal, Lilia, Rasoloarijaona, L., Harbouche, Laid, Bencheikh, Larbi, Lamriben, Larbie, Hanafi, Latifa, Parvez, Laura Braun, Champion, Laure, Croze, Laure, Eprinchard, Laure, Patrier, Laure, Nicolet, Laurence, Vrigneaud, Laurence, Duflot, Laurent, Mackaya, Leandre, Chenine, Leila, Odry, Leon, Tamiji, Lili Taghipour, Bouzar, Lilia Antri, Nga Messi, Liliane Ngango, Le Mouellic, Lionel, Mandart, Lise, Weis, Lise, Pouteau, Lise Marie, Georgieva, Lora, Vitanova, Lorita, Chalabi, Lotfi, Delvallez, Luc, Frimat, Luc, Fromentin, Luc, Marty, Luc, Monjot, Luc, Spataru, Luciana, Bessenay, Lucie, Boissinot, Lucie, Wajsbrot, Lucie, Rakoff, Lucien, Lebourg, Ludivine, Perez, Lydie, Lafage, Lyliane, Azzouz, Lynda, Dumoulin, Madeleine, Ouziala, Messaoud, Joseph, Maan, Brahimi, Mabrouk, Fat, Maeva Wong, Fort, Magalie, Nakhla, Magued, Abtahi, Mahdi, Albadawy, Mahen, Alouach, Mahmoud, Mezghani, Mahmoud, Daroux, Maite, Boukelmoune, Maklouf, Dhib, Malek, Touam, Malik, Dubau, Malina, Balde, Mamadou, Khoa, Man Nguyen, Ismer, Manfred, Mehdi, Manolie, Laforet, Manon, Bouiller, Marc, Eugene, Marc, Fila, Marc, Hazzan, Marc, Kribs, Marc, Ladriere, Marc, Lebot, Marc, Padilla, Marc, Souid, Marc, Marraoui, Marcel, Burbach, Maren, Manescu, Maria, Noguera Gonzalez, Maria Eugenia, Revenco, Mariana, Terrasse, Marianne, Essi, Marie, Macher, Marie Alice, Nogier, Marie Beatrice, Cazin, Marie Cecile, Schweitzer Camoin, Marie Christine, Thouret, Marie Christine, Hannaert, Marie Claude, Servel, Marie France, Chabannier, Marie Helene, Coudert Krier, Marie Jeanne, Catoliquot, Marie Noelle, Guillodo, Marie Paule, Gavard, Marie Sophie, Vairon Codaccioni, Marie Xaviere, Rabec, Marina, Freist, Marine, Gauthier, Marion, Lemaire, Marion, Mehrenberger, Marion, Venot, Marion, Pongas, Marios, Diant, Marlene Beaubrun, Levannier, Martial, Bertaux, Martine, Jablonski, Mathieu, Sacquepee, Mathieu, Dargelos, Mathilde, Lemoine, Mathilde, Tamain, Mathilde, Monge, Matthieu, Reberolle, Matthieu, Cousin, Maud, Francois, Maud, Baron, Maurice, Hoffmann, Maxime, Ingwiller, Maxime, Touzot, Maxime, Mohajer, Mederick, Maaz, Mehadji, Hanoy, Melanie, Marroc, Melanie, Cuny, Melodie, Van Der Straaten, Menno, Serveaux, Mf., Basteri, Michel, Chong, Michel Fen, Hecht, Michel, Massad, Michel, Normand, Michel, Olmer, Michel, Tolani, Michel, Tsimaratos, Michel, Hemery, Michele, Kessler, Michele, Esposito, Miguel, Shenouda, Milad, Kareche, Mimi, Khalili, Mina, Diaconita, Mirella, Rifard, Mohamad Khair, Aladib, Mohamed, Belmouaz, Mohamed, Brahim, Mohamed, Diouani, Mohamed, Cherif, Mohamed Fodil, Jamali, Mohamed, Maghlaoua, Mohamed, Meddeb, Mohamed, Ramdane, Mohamed, Rifaat, Mohamed, Islam, Mohamed Sharifull, Abbade, Mohamed Adnan, Amrandi, Mokhtar, Chawki, Mokhtar, Ciobotaru, Monica, Indrieis, Monica, Chanas, Monique, Hoarau, Monique, Tomeh, Monzer, Bellou, Moufida, Bouzernidj, Mouloud, Ammor, Mounia, Guergour, Mounir, Benzakour, Mountassir, Hachicha, Mourad, Coulibaly, Moussa, Smati, Mustafa, Al Morabiti, Mustapha, Amirou, Mustapha, Isnard, Myriam, Pastural, Myriam, Pujo, Myriam, Boumendjel, Nourredine, Majbri, Nabil, Goumri, Nabila, Mingat, Nadege, Bassilios, Nader, Kerkeni, Nadia, Sedrati, Nadia, Soltani, Nadia, Maroun, Nadine, Neyrat, Nadine, Luang, Nahn, El Esper, Najeh, Ammar, Naji, Ghali, Nasredine, Hamdini, Nasser, Noel, Natacha, Potelune, Natacha, Maisonneuve, Nathalie, Pertuiset, Nathalie, Raynal, Nathalie, Vittoz, Nathalie, Terki, Nazim, Castin, Nelly, Nankeu, Nestor, Bouvier, Nicolas, Keller, Nicolas, Legros, Nicolas, Peters, Nicolas, Quirin, Nicolas, Lefrancois, Nicole, Monnier, Nicole, Rance, Nicole, Bruckmann, Niels, Mertens, Noel, Lorcy, Nolwenn, Gilbert, Olivia, Coldefy, Olivier, Drouineau, Olivier, Dunand, Olivier, Fritz, Olivier, Imhoff, Olivier, Kourilsky, Olivier, Lavelle, Olivier, Moranne, Olivier, Papin, Olivier, Roques, Olivier, Le Maner, Ophelie, Benbrahim, Oussamah Fikri, Erina Torres, Pablo Antonio, Urena Torres, Pablo Antonio, Malvezzi, Paolo, Bindi, Pascal, Cluzel, Pascal, Fontanier, Pascal, Wheatley, Pascal, Depraetre, Pascale, Dubosq, Pascale, Halin, Pascale, Sebahoun, Pascale, Siohan, Pascale, Testevuide, Pascale, Deteix, Patrice, Nolen, Patrice, Hue, Patricia, Lemarchand, Patricia, Donnadieu, Patrick, Fievet, Patrick, Fohrer, Patrick, Francais, Patrick, Giraud, Patrick, Hallonet, Patrick, Henri, Patrick, Michaut, Patrick, Niaudet, Patrick, Pauly, Patrick, Thomas, Patrick, Deleaval, Patrik, Finielz, Paul, Stroumza, Paul, Yverneau, Paule Hardy, Caillard, Pauline, Palacin, Pedro, Aubertin, Perrine, Attias, Philippe, Brunet, Philippe, Chauveau, Philippe, Coindre, Philippe, Coste, Philippe, Dubot, Philippe, Fournier, Philippe, Hiernaux, Philippe, Jousset, Philippe, Yue Wah, Philippe Lan, Lang, Philippe, Le Cacheux, Philippe, Dupont, Philippe Martin, Michel, Philippe, Mirgaine, Philippe, Moriniere, Philippe, Nicoud, Philippe, Rieu, Philippe, Rousseau, Philippe, Sporer, Philippe, Thorel, Philippe, Vanhille, Philippe, Vigeral, Philippe, Zaoui, Philippe, Bataille, Pierre, Brignon, Pierre, Filipozzi, Pierre, Housset, Pierre, Peyronnet, Pierre, Ramperez, Pierre, Vautrin, Pierre, Michel, Pierre Alexandre, Westeel, Pierre Francois, Carron, Pierre Louis, Durand, Pierre Yves, Parent, Pierrot, Seniuta, Piotr, Kuentz, François, Fraoui, Rabah, Tetaz, Rachel, Amaria, Rachid, Bourouma, Rachid, Djeffal, Rachid, Nebbad, Rachida, Allal, Radia, Dimulescu, Radu, Boustani, Rafaat, Mesbah, Rafik, Makdassi, Raifat, Diab, Raji, Puslenghea, Raluca, Roura, Raoul, Khayat, Rateb, Azar, Raymond, Frayssinet, Raymond, Monkam, Regine, Boulahrouz, Rehouni, Boudet, Remi, Demontis, Renato, Gansey, Renaud, Cuvelier, Rene, Schmitt, Renee, Noordally, Reschad, Binaut, Reynald, Latif, Rezkallah, Dufresne, Richard, Montagnac, Richard, Reade, Richard, Genin, Robert, Novo, Robert, Fickl, Rocsana, Dufresne, Roger, Magnol, Roger, Issautier, Roland, Mortelette, Romain, Delaval, Ronan, Lohro, Ronan, M'barga, Roseline, Beau, S., Dupuis, Clémentine, Vidil, Marie Jacques, Hacini, Sabria, Dahmoune, Said, Lekhal, Saliha, Sakso, Salima Ahriz, Saksi, Salima, Citarda, Salvatore, Boubenider, Samir, Kassis, Samuel, Verhille, Sandra, Genestier, Sandrine, Muller, Sandrine, Krid, Saoussen, Richter, Sarah, Delbes, Sebastien, Mailliez, Sebastien, Veillon, Sebastien, Nony, Sébastien, Benarbia, Seddick, Beaudreuil, Severine, Benyaghla, Sidi Ali, Duquennoy, Simon, Baluta, Simona, Boncila, Simona, Mzoughi, Sonia, Ribal, Sonia, Acamer, Sophie, Chauvet, Sophie, Girerd, Sophie, Ozenne, Sophie, Parahy, Sophie, Duval, Sophie Rubens, Taque, Sophie, Menouer, Soraya, Chargui, Soumaya, Bataille, Stanislas, Barbier, Stephane, Billion, Stephane, Roueff, Stephane, Torner, Stephane, Martin, Stephane Jean, Coupel, Stephanie, Cloarec, Sylvie, Lavaud, Sylvie, Leou, Sylvie, Chatelet, T., Onesta, Tania, Benhabib, Tassadit, Bensalem, Tayeb, Dimulescu, Theodora, Sawadogo, Theophile, Hitze, Thibault Dolley, Baranger, Thierry, Boudemaghe, Thierry, Hannedouche, Thierry, Krummel, Thierry, Lobbedez, Thierry, Milcent, Thierry, Dervaux, Thomas, Guincestre, Thomas, Kofman, Thomas, Raphael, Thomas, Sadreux, Thomas, Ulinski, Tim, Roger, Tiphaine Guyon, Serrato, Tomas, Kofman, Tomek, Wong, Tony, Boubia, Toufik, Gbindoun, Ubald Assogba, Khuzaie, Usama, Caudwell, Valerie, Chatelet, Valerie, Crougneau, Valerie, De Precigout, Valerie, Drouillat, Valerie, Galantine, Valerie, Hugot, Valerie Granveau, Leroy, Valerie, Boubia, Veronique, Falque, Veronique, Fournier, Veronique, Queron, Veronique, Viviani, Veronique, Gueuttin, Victor, Panescu, Victor, Calonge, Victorio Menoyo, Nguyen, Viet, Allot, Vincent, Delattre, Vincent, Leduc, Vincent, Pradier, Vincent, Aglae, Violaine Emal, Badulescu, Viorica, Molina, Virginia, Besson, Virginie, Chaigne, Virginie, Jaber, Waddah, Boudi, Wael, El Haggan, Wael, Guillon, Wen Qin, Aneni, Wided Tabbi, Hanf, William, Kohn, Wladimir, Bellenfant, Xavier, Gaudry, Xavier Moreau, Delmas, Yahsou, Knefati, Yannick, Saingra, Yannick, Tirolien, Yannick, Mann, Youssef, Brunak, Yvan, Dimitrov, Yves, Doussy, Yves, Tanter, Yves, Benabid, Zaid, Soltani, Zaara, Boukerroucha, Zacharia, Takla, Zafer, Ramanantsialonina, Zana, Dickson, Zara, Tubail, Zead, Pour, Zoe Koochaki, Boukhalfa, Zohra, Jacquot, Zohra, Couchoud, Cécile, Bayer, Florian, Ayav, Carole, Béchade, Clémence, Chantrel, François, Galland, Roula, Hourmant, Maryvonne, and Mercadal, Lucile

Published
2020
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46. Needs-assessment for an artificial intelligence-based Chatbot for pharmacists in HIV care: results from a knowledge–attitudes–practices survey

Author

Laymouna, Moustafa, Ma, Yuanchao, Lessard, David, Engler, Kim, Therrien, Rachel, Schuster, Tibor, Vicente, Serge, Achiche, Sofiane, Haj, Maria Nait El, Lemire, Benoît, Kawaiah, Abdalwahab, Lebouché, Bertrand, Laymouna, Moustafa, Ma, Yuanchao, Lessard, David, Engler, Kim, Therrien, Rachel, Schuster, Tibor, Vicente, Serge, Achiche, Sofiane, Haj, Maria Nait El, Lemire, Benoît, Kawaiah, Abdalwahab, and Lebouché, Bertrand

Abstract

Background: Pharmacists need up-to-date knowledge and decision-making support in HIV care. We aim to develop MARVIN-Pharma, an adapted artificial intelligence-based chatbot initially for people with HIV, to assist pharmacists in considering evidence-based needs. Methods: From December 2022 to December 2023, an online needs-assessment survey evaluated Québec pharmacists’ knowledge, attitudes, involvement, and barriers relative to HIV care, alongside perceptions relevant to the usability of MARVIN-Pharma. Recruitment involved convenience and snowball sampling, targeting National HIV and Hepatitis Mentoring Program affiliates. Results: Forty-one pharmacists (28 community, 13 hospital-based) across 15 Québec municipalities participated. Participants perceived their HIV knowledge as moderate (M = 3.74/6). They held largely favorable attitudes towards providing HIV care (M = 4.02/6). They reported a “little” involvement in the delivery of HIV care services (M = 2.08/5), most often ART adherence counseling, refilling, and monitoring. The most common barriers reported to HIV care delivery were a lack of time, staff resources, clinical tools, and HIV information/training, with pharmacists at least somewhat agreeing that they experienced each (M ≥ 4.00/6). On average, MARVIN-Pharma’s acceptability and compatibility were in the ‘undecided’ range (M = 4.34, M = 4.13/7, respectively), while pharmacists agreed to their self-efficacy to use online health services (M = 5.6/7). Conclusion: MARVIN-Pharma might help address pharmacists’ knowledge gaps and barriers to HIV treatment and care, but pharmacist engagement in the chatbot’s development seems vital for its future uptake and usability.

Published
2024

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