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Andrew Smith

Family Medicine
MD (Memorial), CCFP(EM), MEng

Assistant Professor of Family Medicine
Primary Healthcare Research Unit


t: 709-777-7795
f: 709-777-7916
lab:

andrew.smith@med.mun.ca


Bio
Dr Smith completed his undergraduate medical education in 2005 at Memorial University. He went on to complete his Family Medicine residency in 2007 and Special Competence in Emergency Medicine in 2008. He is currently enjoying a mixed urban-rural practice spending time in St. John's at the Family Practice Unit and in Ferryland at the Shamrock Medical Clinic. Prior to family medicine, Andrew was a full-time ER physician at both St. Claire's Mercy Hospital and the Health Sciences Centre.

Point of Care Ultrasound
Dr. Smith is heavily involved in developing and teaching of Point of Care Ultrasound at Memorial and across the province. Specific to Point of Care Ultrasound, Andrew worked with a team of individuals to develop
  • Ultrasound Guided Central Line Program for the Intensive Care Unit
  • Multidisciplinary PoCUS training for multiple specialties includingPointofCare-(1).png
    • Internal Medicine
    • General Surgery
    • Emergency Medicine
    • Obstetrics & Gynecology
    • Anesthesia
  • Rural Family Medicine PoCUS training including distributed competency development
  • Continuing Medical Education PoCUS training for practicing physicians across the province
Volume Status Monitoring using PoCUS
Congestive heart failure (CHF) is a common disease associated with high morbidity, mortality and readmission after hospitalization. Ninety-day readmission rates approach 25% resulting in a significant impact on quality of life and financial strain on the healthcare system. Dr. Smith is leading a research project investigating the potential of ultrasound to monitor and predict clinical deterioration in patients with CHF. His team of engineers and researchers have developed tools and algorithms capable of semi-automatically segmenting venous vascular structures such as the Internal Jugular Vein and Inferior Vena Cava to monitor changes in volume status. Segmentation of the venous vasculature enables researchers to monitor temporal and spatial changes of the vessels and extract information regarding short and long term trends including respiratory variation, relative changes as well as carotid and cardiac pulsations. 

CSA-vs-Time.png
Fig.1 Segmentation of an ultrasound of the IJ vein                                                        
Fig.2 Respiratory and cardiac pulsations with varying angle of inclination
 
The team has developed an online segmentation tool capable of semi-automatically segmenting a variety of vascular structures to extract the contour information. A variety of segmentation algorithms are available for use and are easily incorporated into the segmentation engine. The team are interested in providing access to their segmentation tool to support a variety of volume status research projects.

Point of Care Genetic Testing
Personalized medicine, an approach to patient care that utilizes bioprofiling information to guide treatment, promises to revolutionize the medical field. The ability to administer the right drug to the right patient at the right time has the potential to save lives, while concomitantly yielding billion dollar reductions in health care costs. At present, major impediments to implementing gene-guided treatment strategies in the clinical setting stem from limited accessibility, expense, and time delays associated with genetic testing.

A multi-disciplinary team of researchers at Memorial University is currently working to develop a Point-of-Care (PoC) genetic testing using ElectroWetting-On-Dielectric technology.
 
PoC-Genetics.png
                                             Fig.3 Benchtop setup for characterizing EWOD Point of Care Genetics testing
 


Select Publications
  1. Karami E, Shehata M, McGuire P, Smith A. A Semi-Automated Technique for Internal Jugular Vein Segmentation in Ultrasound Images Using Active Contours. International Conference on Biomedical and Health Informatics.  Las Vegas, NV. 2016.
  2. Smith J, Shehata M, Smith A. Texture Features for Classification of Vascular Ultrasound. International Conference on Pattern Recognition. Cancun, Mexico. 2016
  3. Bellows S, Shehata M, Smith J, McGuire P, Smith A. (2015) Validation of a Computerized Technique for Automatically Tracking and Measuring the Inferior Vena Cava in Ultrasound Imagery. Biosystems Engineering. http://dx.doi.org/10.1016/j.biosystemseng.2015.02.005
 
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