阿尔伯塔大学招全奖硕士博士(生物力学和人工智能方向）

mqp2259UID: 3266120

Funded Ph.D. or M.Sc. Opportunity – Integration of Artificial Intelligence with Surface Topography for Managing Scoliosis

Start Date: 2022 (earlier or later start dates may be considered)
How to Apply: Interested candidates may contact Dr. Lindsey Westover or Dr. Qipei Mei by email (lwestove@ualberta.ca or qipei@ualberta.ca) with a recent CV, transcripts, and cover letter.

The Project:
Our project goal is to improve the clinical management of children and youth suffering from scoliosis by reducing cumulative X-ray exposure during their growing years, resulting in improved long-term health outcomes. Scoliosis is an orthopaedic condition resulting in curvature and rotation of the spine. It typically first appears during adolescence, and affects females more than males. Asymmetry of the torso is one of the symptoms. Scoliosis is diagnosed and monitored using x-rays. Unfortunately, x-rays expose young patients to the effects of radiation including a documented increase in cancer risk. Surface topography (ST) is a non-invasive three dimensional (3D) assessment of the torso shape. Using a laser scanner, 3D images of the torso are acquired and the asymmetry of the torso is measured. The severity of scoliosis is then quantified using indices reflecting the symmetry of the torso. Previous studies using surface topography with 2D measurements instead of the available 3D data were not able to accurately predict the severity of the spinal curvature. In our recent work, we introduced a novel 3D asymmetry technique that does not rely on markers placed on the torso or on simple 2D measurements. Our ST measures were able to quantify the severity and progression of scoliosis. In the current proposal, we will develop artificial intelligence techniques to better use the surface topography parameters to estimate the actual shape of the underlying spinal curvature. The developed methods will be designed to ensure that no moderate/severe curves are missed and that all progressing curves are detected to make sure that patients are not missing important treatment opportunities, while dramatically reducing the x-ray radiation exposure to patients. This project involves an exciting multidisciplinary collaboration with research teams across engineering and medicine.
Qualifications of the applicant:

• Applicants must hold a Master’s of Science (or Engineering) degree in a relevant discipline (e.g., civil engineering, mechanical engineering, computer science, electrical/computer engineering, etc.). Candidates with a Bachelor’s of Science (or Engineering) degree may also be considered.
• Applicants should be willing and able to register in the University of Alberta’s Ph.D. program in Mechanical Engineering (strong M.Sc. level candidates may also be considered).
• Applicants should be highly motivated, well organized and passionate about research with good communication skills in English. Please refer to the U of A requirements for English Language Proficiency.
• Applicants should have strong desire to conduct transformational research in close collaboration with clinical professionals.
• Applicants must have a minimum GPA of 3.3
• The position is open to Canadian Citizens, permanent residents of Canada, and International Students
  

·