SiLab

Welcome to the Stroke Innovation (& Machine learning) Lab [SiLab]

Transforming Neurovascular & Stroke Care Through Machine Learning Intelligence: #AI4QI; Our lab focuses on Quality Improvement in stroke and neurologic care. Our research domain is clinical neurology, with a particular emphasis on neurovascular care. In my quality improvement (QI) lab, we concentrate on stroke care and utilize machine learning techniques applied to datasets and time-series data. Recently, our focus has shifted to exploring sound as a biomarker. I am the Principal Investigator, Dr. Houman Khosravani, from the Division of Neurology at the University of Toronto.

Areas of academic focus:

• #AI4QI or ML4QI - We pioneer machine learning applications to elevate quality improvement in acute stroke care. Our flagship initiative, Project MASA (Machine Learning Assisted Swallowing Assessment), analyzes voice biomarkers to revolutionize swallowing assessments and enhance patient outcomes.

• Stroke Resuscitation Innovation - As originators of the “stroke resuscitation” concept and Protected Code Stroke protocol, we lead the integration of Crisis Resource Management (CRM) principles into acute stroke care. Our simulation program advances team performance and patient safety during critical moments of stroke intervention, complementing our broader work with the Neurovascular Innovations CollaborativE (NICE).

• Integrative Stroke Palliative Care - We champion comprehensive stroke care by developing evidence-based protocols that seamlessly integrate palliative medicine into acute stroke treatment. Our research defines best practices for incorporating palliative approaches while maintaining aggressive intervention when appropriate, ensuring optimal patient-centered outcomes across the care spectrum.

Members of the lab are also involved in educational initiatives, including a podcast on stroke education: Stroke FM Podcast, the official podcast of the Canadian Stroke Consortium.

Stroke Innovation-machine learning Lab (SiLab) machine learning for quality improvement

• My lab is part of the NQIL group, at the University of Toronto; a hub for QI for neurology.
• We also are exploring the intersection of machine learning and quality improvement, utilizing voice-based technologies to refine our methods. We have expertise in deep-learning and processing of audio signals.

• Our pursuit of excellence extends to the cutting-edge field of machine learning. With support from T-CAIREM and SHSC, we are leveraging bedside physiologic recordings to improve the quality of acute stroke care.

• Machine learning as applied to quality improvement in stroke
  • 2024-2025 (academic years)
    • Lab Members:
      • Rishit Dagli, Computer Science, U of T, T-CAIREM (2024 student award), now at NVIDIA doing a 1-year studentship, worked on ML4QI in Stroke in project MASA (machine learing assisted swallowing assessment), and pioneered and developed SEE2SOUND
      • Dr. A. Balachandar, Neurology, U of T, Movement disorders Fellow and pursuing PhD in movement disorders and DBS, worked on ML4QI in Stroke in project MASA (machine learing assisted swallowing assessment)
      • R. Saab, U of T Med, ML4QI in Stroke, works on ML4QI in Stroke in project MASA (machine learing assisted swallowing assessment)Sunnybrook Research Institute
      • E. Nashnoush, MSc, U of T Data Science, ML4QI in Stroke, Sunnybrook Research Institute, T-CAIREM, HealthQuality, part of Project MASA
      • H. Mahdi, Western University, Medicine, ML4QI in Stroke, part of project MASA
      • Dr. B. Tilley, Neurology, PGME Neurology, University of Toronto - working on the use LLMs in EHRs, and project 1stroke.org
      • Dr. E. Adegunna, Neurology, PGME Neurology, University of Toronto - working on palliative care in stroke, and project Pal-MASA
      • Diya Ahmed, UGME, U of T Medicine, working on QI in Neurology and future project on LLMs in Neurology
      • Rebecca Wu, UGME, U of T Medicine, working on LLMs in Neurology
      • Sofia Berrada, UG, U of T
      • Jake Lance, UG, U of T
  • 2023-2024
    • Lab Members:
      • Dr. A. Balachandar, Neurology, U of T, PGY4/5 Project, ML4QI in Stroke
      • R. Saab, U of T Med, ML4QI in Stroke, Sunnybrook Research Institute
      • E. Nashnoush, MSc, U of T Data Science, ML4QI in Stroke, Sunnybrook Research Institute, T-CAIREM (2023 student award)
      • H. Mahdi, Western University, Med, ML4QI in Stroke, Sunnybrook Research Institute
      • Dr. B. Sivanandan, Neurology, U of T, PGY4/5 Project, Palliative Care in Stroke
      • Dr. M. Mahendiran, U of T, Family Medicine (graduated), Palliative Care research, Hospital Medicine Fellow (Orange Team)
      • Georges Khalaf, CREMS, Summer 2024, UGME, U of T Medicine
      • Dr. E. Adegunna, Neurology, U of T, PGY2
      • Dr. B. Tilley, Neurology, U of T, PGY1
      • Diya Ahmed, UGME, U of T Medicine
  • 2022-2023
    • Lab Members:
      • Dr. A. Balachandar, Neurology, U of T
      • R. Saab, U of T Med, T-CAIREM (2022 student award)
      • M. Panchal, U of T Med, CREMS

Simulation in Code Stroke - Neurovascular Resuscitation - Neurovascular Innovations CollaborativE (NICE)

• Our lab championed the framework of crisis resource management in stroke simulation to optimize critical intervention metrics such as “door-to-needle” times. We are proud to be pioneers in the field and published the first reframing of Crisis Resource Management (CRM) for stroke care. We also developed the “protected code stroke” during the COVID-19 pandemic, which was integrated into national and international guidelines and downloaded over 30,000 times from the American Heart Association and Stroke journal’s website. Our research aims to enhance care pathways and human performance factors for acute stroke patients through the simulation of neurovascular resuscitation. We have introduced the concept of “neurovascular resuscitation,” applying principles from medical and trauma resuscitation to stroke treatment—thereby reinstating the ‘code’ in code stroke.
• In 2023, Dr. Houman Khosravani and Dr. Christine Hawkes co-founded the Neurovascular Innovations CollaborativE (NICE) @neuroresuslab, an initiative projected to contribute substantially to augmenting neurovascular care education. Our Lab’s focus at SiLab is the medical/neurocritical care aspects of hyperacute care and techniques.

Routine Integration of Palliative Care in Stroke

• Despite significant advancements in stroke care, a considerable number of patients still grapple with substantial morbidity and mortality. Recognizing this, we advocate for the routine integration of palliative care into stroke treatment. Compassionate and effective care forms the bedrock of the philosophy we advocate for in terms of expanding the confluence of palliative medicine and stroke care.

Prospective Students Each year my lab takes 1 student from T-CAIREM, and another student either via SRI (or CREMS).

If you are a student with an interest in QI and experienced in research, or if you are an engineering, CS, or MD student interested in clinical applications of machine learning in neurology please reach out. Get in touch or send an email (houman[at]neurovascular[dot]ca), if you are interested in collaborating on any of the above topics: machine learning, QI, human factors, and simulation in the realms of stroke/neurovascular or neurocritical care initiatives.

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