Audrey Durand

Claudine Auger

Anouk Lamontagne

Sara Ahmed

2022-12-09

BMC Neurology (publié)

Performative Prediction in Time Series: A Case Study

Rupali Bhati

Jennifer Jones

David Langelier

Anthony Reiman

Jonathan Greenland

Kristin Campbell

2022-12-02

NeurIPS.cc/2022/Workshop/TS4H (poster)

openreview.net

Annotation Cost-Sensitive Deep Active Learning with Limited Data (Student Abstract)

Renaud Bernatchez

Flavie Lavoie-Cardinal

2022-06-28

Proceedings of the AAAI Conference on Artificial Intelligence (publié)

Biomedical Research and Informatics Living Laboratory for Innovative Advances of New Technologies in Community Mobility Rehabilitation: Protocol for Evaluation and Rehabilitation of Mobility Across Continuums of Care

Sara Ahmed

Philippe Archambault

Claudine Auger

Joyce Fung

Eva Kehayia

Anouk Lamontagne

Annette Majnemer

Sylvie Nadeau

Alain Ptito

Bonnie Swaine

Background Rapid advances in technologies over the past 10 years have enabled large-scale biomedical and psychosocial rehabilitation researc… (voir plus)h to improve the function and social integration of persons with physical impairments across the lifespan. The Biomedical Research and Informatics Living Laboratory for Innovative Advances of New Technologies (BRILLIANT) in community mobility rehabilitation aims to generate evidence-based research to improve rehabilitation for individuals with acquired brain injury (ABI). Objective This study aims to (1) identify the factors limiting or enhancing mobility in real-world community environments (public spaces, including the mall, home, and outdoors) and understand their complex interplay in individuals of all ages with ABI and (2) customize community environment mobility training by identifying, on a continuous basis, the specific rehabilitation strategies and interventions that patient subgroups benefit from most. Here, we present the research and technology plan for the BRILLIANT initiative. Methods A cohort of individuals, adults and children, with ABI (N=1500) will be recruited. Patients will be recruited from the acute care and rehabilitation partner centers within 4 health regions (living labs) and followed throughout the continuum of rehabilitation. Participants will also be recruited from the community. Biomedical, clinician-reported, patient-reported, and brain imaging data will be collected. Theme 1 will implement and evaluate the feasibility of collecting data across BRILLIANT living labs and conduct predictive analyses and artificial intelligence (AI) to identify mobility subgroups. Theme 2 will implement, evaluate, and identify community mobility interventions that optimize outcomes for mobility subgroups of patients with ABI. Results The biomedical infrastructure and equipment have been established across the living labs, and development of the clinician- and patient-reported outcome digital solutions is underway. Recruitment is expected to begin in May 2022. Conclusions The program will develop and deploy a comprehensive clinical and community-based mobility-monitoring system to evaluate the factors that result in poor mobility, and develop personalized mobility interventions that are optimized for specific patient subgroups. Technology solutions will be designed to support clinicians and patients to deliver cost-effective care and the right intervention to the right person at the right time to optimize long-term functional potential and meaningful participation in the community. International Registered Report Identifier (IRRID) PRR1-10.2196/12506

2022-06-01

JMIR Research Protocols (publié)

Contextual bandit optimization of super-resolution microscopy

Anthony Bilodeau

Renaud Bernatchez

Albert Michaud-Gagnon

Flavie Lavoie-Cardinal

2022-05-27

Applied Informatics (publié)

Microscopy analysis neural network to solve detection, enumeration and segmentation from image-level annotations

Anthony Bilodeau

Constantin V. L. Delmas

M. Parent

Paul De Koninck

Flavie Lavoie-Cardinal

2022-04-14

Nature Machine Intelligence (published)

Microscopy analysis neural network to solve detection, enumeration and segmentation from image-level annotations

Anthony Bilodeau

Constantin V.L. Delmas

Martin Parent

Paul De Koninck

Flavie Lavoie-Cardinal

2022-04-14

Nature Machine Intelligence (publié)

Predicting Visual Improvement After Macular Hole Surgery: A Combined Model Using Deep Learning and Clinical Features

Alexandre Lachance

Mathieu Godbout

Fares Antaki

Mélanie Hébert

Serge Bourgault

Mathieu Caissie

Éric Tourville

Ali Dirani

2022-04-06

Translational Vision Science & Technology (publié)

Predicting Visual Improvement After Macular Hole Surgery: A Combined Model Using Deep Learning and Clinical Features

Alexandre Lachance

Mathieu Godbout

Fares Antaki

Mélanie Hébert

Serge Bourgault

Mathieu Caissie

Éric Tourville

A. Dirani

Purpose The purpose of this study was to assess the feasibility of deep learning (DL) methods to enhance the prediction of visual acuity (VA… (voir plus)) improvement after macular hole (MH) surgery from a combined model using DL on high-definition optical coherence tomography (HD-OCT) B-scans and clinical features. Methods We trained a DL convolutional neural network (CNN) using pre-operative HD-OCT B-scans of the macula and combined with a logistic regression model of pre-operative clinical features to predict VA increase ≥15 Early Treatment Diabetic Retinopathy Study (ETDRS) letters at 6 months post-vitrectomy in closed MHs. A total of 121 MHs with 242 HD-OCT B-scans and 484 clinical data points were used to train, validate, and test the model. Prediction of VA increase was evaluated using the area under the receiver operating characteristic curve (AUROC) and F1 scores. We also extracted the weight of each input feature in the hybrid model. Results All performances are reported on the held-out test set, matching results obtained with cross-validation. Using a regression on clinical features, the AUROC was 80.6, with an F1 score of 79.7. For the CNN, relying solely on the HD-OCT B-scans, the AUROC was 72.8 ± 14.6, with an F1 score of 61.5 ± 23.7. For our hybrid regression model using clinical features and CNN prediction, the AUROC was 81.9 ± 5.2, with an F1 score of 80.4 ± 7.7. In the hybrid model, the baseline VA was the most important feature (weight = 59.1 ± 6.9%), while the weight of HD-OCT prediction was 9.6 ± 4.2%. Conclusions Both the clinical data and HD-OCT models can predict postoperative VA improvement in patients undergoing vitrectomy for a MH with good discriminative performances. Combining them into a hybrid model did not significantly improve performance. Translational Relevance OCT-based DL models can predict postoperative VA improvement following vitrectomy for MH but fusing those models with clinical data might not provide improved predictive performance.

2022-04-01

Translational Vision Science & Technology (published)

Biomedical Research and Informatics Living Laboratory for Innovative Advances of New Technologies in Community Mobility Rehabilitation: Protocol for Evaluation and Rehabilitation of Mobility Across Continuums of Care (Preprint)

Sara Ahmed

Philippe Archambault

Claudine Auger

Joyce Fung

Eva Kehayia

Anouk Lamontagne

Annette Majnemer

Sylvie Nadeau

Alain Ptito

Bonnie Swaine

2022-01-13

(publié)

Biomedical Research and Informatics Living Laboratory for Innovative Advances of New Technologies in Community Mobility Rehabilitation: Protocol for Evaluation and Rehabilitation of Mobility Across Continuums of Care

Sara Ahmed

P. Archambault

Claudine Auger

Joyce Phua Pau Fung

Eva Kehayia

Anouk Lamontagne

Annette Majnemer

Sylvie Nadeau

Alain Ptito

B. Swaine

Background Rapid advances in technologies over the past 10 years have enabled large-scale biomedical and psychosocial rehabilitation researc… (voir plus)h to improve the function and social integration of persons with physical impairments across the lifespan. The Biomedical Research and Informatics Living Laboratory for Innovative Advances of New Technologies (BRILLIANT) in community mobility rehabilitation aims to generate evidence-based research to improve rehabilitation for individuals with acquired brain injury (ABI). Objective This study aims to (1) identify the factors limiting or enhancing mobility in real-world community environments (public spaces, including the mall, home, and outdoors) and understand their complex interplay in individuals of all ages with ABI and (2) customize community environment mobility training by identifying, on a continuous basis, the specific rehabilitation strategies and interventions that patient subgroups benefit from most. Here, we present the research and technology plan for the BRILLIANT initiative. Methods A cohort of individuals, adults and children, with ABI (N=1500) will be recruited. Patients will be recruited from the acute care and rehabilitation partner centers within 4 health regions (living labs) and followed throughout the continuum of rehabilitation. Participants will also be recruited from the community. Biomedical, clinician-reported, patient-reported, and brain imaging data will be collected. Theme 1 will implement and evaluate the feasibility of collecting data across BRILLIANT living labs and conduct predictive analyses and artificial intelligence (AI) to identify mobility subgroups. Theme 2 will implement, evaluate, and identify community mobility interventions that optimize outcomes for mobility subgroups of patients with ABI. Results The biomedical infrastructure and equipment have been established across the living labs, and development of the clinician- and patient-reported outcome digital solutions is underway. Recruitment is expected to begin in May 2022. Conclusions The program will develop and deploy a comprehensive clinical and community-based mobility-monitoring system to evaluate the factors that result in poor mobility, and develop personalized mobility interventions that are optimized for specific patient subgroups. Technology solutions will be designed to support clinicians and patients to deliver cost-effective care and the right intervention to the right person at the right time to optimize long-term functional potential and meaningful participation in the community. International Registered Report Identifier (IRRID) PRR1-10.2196/12506

2022-01-13

JMIR Research Protocols (published)

Biomedical Research & Informatics Living Laboratory for Innovative Advances of New Technologies in Community Mobility Rehabilitation: Protocol for a longitudinal evaluation of mobility outcomes (Preprint)

Sara Ahmed

Philippe Archambault

Claudine Auger

Joyce Fung

Eva Kehayia

Anouk Lamontagne

Annette Majnemer

Sylvie Nadeau

Alain Ptito

Bonnie Swaine

UNSTRUCTURED The Biomedical Research and Informatics Living Laboratory for Innovative Advances of New Technologies in Community Mobility Re… (voir plus)habilitation (BRILLIANT) program to provide evidence-based research to improve rehabilitation for individuals with Acquired Brain Injury (ABI: traumatic brain injury [TBI], cerebral palsy-fetal/perinatal brain injury, and stroke). The vision of the BRILLIANT program is to optimize mobility of persons with ABI across the lifespan. The program will develop and deploy a comprehensive clinical and community based mobility monitoring system to evaluate the factors that result in poor mobility, and develop personalized mobility interventions that are optimized for specific patient sub-groups. These innovations will be used by front-line clinicians to deliver cost-effective care; the right intervention to the right person at the right time, accounting for long-term functional potential and meaningful participation in the community.

2022-01-13

(published)