Stroke Innovation-machine learning Lab, quality improvement in neurology, University of Toronto

Welcome to Stroke Innovation-Machine learning Lab (SiLab) for Quality Improvement in stroke and neurologic care. Our research domain is clinical neurology with a focus on neurovascular care. Our quality improvement (QI) lab focuses on stroke care and leverages machine learning as applied to datasets and time-series; most recently our focus is sound as a biomarker. Lab PI: Dr. Houman Khosravani, Division of Neurology at the University of Toronto.

Areas of academic focus:

  • ML4QI - machine learning for bolstering current standards of care. We are leveraging machine-learning to enhance QI within acute stroke care in the inpatient setting. Our current work is focused on using voice as a biomarker - we call this Sonic Diagnosis.
  • stroke resuscitation - our lab termed the work “stroke resuscitation”, developed the protected code stroke protocol, defined the use of crisis resource management in stroke, and thus our focus is education and quality improvement during the code stroke process. We use CRM principles and have an active code stroke simulation program. This also includes our efforts within NICE (Neurovascular Innovations CollaborativE) as listed below.
  • palliative care in acute stroke - we are passionate about providing maximal effective care and that includes integration of palliative care within stroke treatment; our lab works on the development of protocols for routine integration of palliative care as we define the scope of palliative medicine in acute stroke care.

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

  • Our 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)
        • Dr. A. Balachandar, Neurology, U of T, PGY5/Fellow(pursuing PhD in movement disorders and DBS) 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, HealthQuality
        • H. Mahdi, Western University, Med, ML4QI in Stroke
        • Dr. E. Adegunna, Neurology, U of T, PGY2/3
        • Dr. B. Tilley, Neurology, U of T, PGY1/2
        • Diya Ahmed, U of T Medicine
    • 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)
        • Dr. E. Adegunna, Neurology, U of T, PGY2
        • Dr. B. Tilley, Neurology, U of T, PGY1
        • Diya Ahmed, 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 we published the first reframe of Crisis Resource Management (CRM) for stroke care care. We also developed the “protected code stroke” during the COVID19 pandemic, which was integrated into national and international guidelines, and downloaded over 30,000 times from the American Heart Association, Stroke journal’s website. Our research aims to enhance care pathways and human performance factors for acute stroke patients through 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), an initiative projected to contribute substantially to augmenting neurovascular care education.
  • Our lab’s portion of focus is the medical/neurocritical care efforts within NICE, while Dr. Hawkes helms the neurovascular and catheter-based 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.

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.

Please note: All content on this site, including blogs, links, topics, and posts, is intended solely for educational purposes. This site does not constitute medical advice or consultation and does not establish a duty of care or any medical care/information/advice/or other obligations. Opinions expressed here are personal and do not reflect or replace expert advice from any institution, workplace, or organization. By using the site you agree to this.


May 12, 2024 Our manuscript “Retrospective Analysis of the Integration of Palliative Care Into the Care of Stroke Patients Admitted to a Regional Stroke Center”, has been published Read the full paper.
Feb 7, 2024 Our manuscript “Tuning In: Analysis of Audio Classifier Performance in Clinical Settings with Limited Data”, has been submitted and published in pre-print Read the full arXiv paper.
Dec 18, 2023 Thanks to all the students/trainees and interested folks that applied to the lab for the T-CAIREM summer 2024 studentship. A talented pool of applicants. If you are interested in volunteer positions get in touch with the lab.
Nov 24, 2023 Our manuscript is Published at Frontiers in Neuroscience, Read the full paper.
Nov 23, 2023 Read the blog post about our Machine-learning Assisted Swallowing Assessment, assistive technology for stroke swallow screening.