Trainee Rounds: Arjun Balachandar & Mohammad Ali Khan

Hear from two UofT trainees researching new ways to use artificial intelligence in medicine. 

Dr. Mohammad Khan from the University of Toronto Department of Family and Community Medicine and Dr. Arjun Balachandar from the University of Toronto Division of Neurology (Medicine) will be presenting their research. 

Dr. Mohammad Khan, MD
Department of Family and Community Medicine, University of Toronto

TITLE: Machine Learning Quantification of Fluid Volume in Eyes with Retinal Vein Occlusion Undergoing Treatment with Aflibercept: The REVOLT study

ABSTRACT: To investigate the combined relationship between ischemia, retinal fluid, and layer thickness measurements with visual acuity outcomes for RVO patients and derive insights into disease pathology using machine learning. SS-OCT data were used to assess retinal layer thicknesses and quantify both intraretinal fluid (IRF) and subretinal fluid (SRF) using a deep learning-based, macular fluid segmentation algorithm for 49 treatment-naive eyes that were diagnosed with visual impairment due to central or branch RVO. Patients received three loading doses of 2 mg intravitreal aflibercept injections (IAI) and then were put on a treat-and-extend regimen. Image analysis was performed at baseline, three-month, and six-month follow-ups. Baseline OCT morphological features and fluid measurements were correlated using the Pearson correlation coefficient (PCC) to changes in BCVA to determine which features most impacted change in BCVA at 6 months. Areas of non-perfusion in OCTA images at baseline were also correlated with changes in BCVA at six months. A combined model of IRF volume, OPL and RNFL layer thicknesses, alongside ischemic indices, provides the best correlation to BCVA changes. Combined fluid and layer segmentation of OCT images provides clinically useful biomarkers for RVO patients.

Dr. Arjun Balachandar, MD
Division of Neurology, Department of Medicine, University of Toronto

TITLE: Automated Sleep Detection in Movement Disorders Using Deep Brain Stimulation and Machine Learning

ABSTRACT:  Sleep disturbances in movement disorders can be debilitating. Deep Brain Stimulation (DBS) can treat motor symptoms, but treating sleep disorders requires detecting sleep. We recorded neural activity in various movement disorders in patients with DBS and created new machine-learning methods to detect sleep accurately.

WEDNESDAY, JUNE 7th, 2023
12:00-1:00pm ET
ZOOM