Toward Intelligent Optimization of Brace Treatment for Adolescent Idiopathic Scoliosis Open Access
- Other title
Decision Support System
- Type of item
- Degree grantor
University of Alberta
- Author or creator
Chalmers, David E.
- Supervisor and department
Dr. Edmond Lou (Electrical & Computer Engineering)
Dr. Vicky Zhao (Electrical & Computer Engineering)
- Examining committee member and department
Dr. Carl-Eric Aubin (Mechanical Engineering)
Dr. Marek Reformat (Electrical & Computer Engineering)
Dr. Kajsa Duke (Mechanical Engineering)
Department of Electrical and Computer Engineering
- Date accepted
- Graduation date
Doctor of Philosophy
- Degree level
Electronic decision support systems have the potential to improve healthcare practices in many domains. This thesis investigates the use of data-driven decision support to help optimize brace treatment for children who have Adolescent Idiopathic Scoliosis (AIS).
AIS is a spinal deformity affecting 2-3% of adolescents. If left untreated, AIS may progress (worsen), negatively affecting the adolescent’s emotional, social, and physical wellbeing and eventually necessitating surgical intervention. Brace treatment is the most common non-surgical treatment for AIS; in brace treatment a back brace applies corrective pressure to the torso, with the goal of preventing progression. Patients’ faithfulness in wearing the brace as long and as tightly as prescribed affects treatment outcome. But the outcome also depends on patient characteristics, the nature of the deformity, and many other factors in addition to compliance. The relationships between these factors and treatment outcome are complex and not perfectly understood; as a result, brace treatment outcome is difficult to predict. As technology improves our ability to predict treatment outcome, the ability to optimize treatment protocols for individual AIS patients should improve as well.
This research envisions a complete system for collecting patient data and using it to generate treatment recommendations for new patients. In this system, electronic sensors collect information about patients’ brace-wear habits, machine-learning techniques use sensor and other data to train prediction models, and these models’ predictions of new patients’ outcomes are used to customize treatment protocols to those patients. This work developed the components of this system and implemented them in a scalable hardware/software platform. Data from 31 patients was collected and processed by the system. Simulations were used to provide an initial assessment of the system’s treatment recommendations.
- Permission is hereby granted to the University of Alberta Libraries to reproduce single copies of this thesis and to lend or sell such copies for private, scholarly or scientific research purposes only. The author reserves all other publication and other rights in association with the copyright in the thesis and, except as herein before provided, neither the thesis nor any substantial portion thereof may be printed or otherwise reproduced in any material form whatsoever without the author's prior written permission.
- Citation for previous publication
E Chalmers, W Pedrycz, and E Lou, “Predicting the Outcome of Brace Treatment for Scoliosis using Conditional Fuzzy Clustering,” Joint Congress of the International Fuzzy Systems Association and North American Fuzzy Information Processing Society, June 24-28 2013E Chalmers, W Pedrycz, and E Lou, “Human experts’ and a fuzzy model’s predictions of outcomes of scoliosis treatment: A comparative analysis”, IEEE Transactions on Biomedical Engineering, 62(3): 1001-1007, 2015E Chalmers, D Hill, H Zhao, and E Lou, “Prescriptive analytics applied to brace treatment for AIS: a pilot demonstration”, Scoliosis, 10(Suppl 2): S13, 2015E Chalmers, E Lou E, D Hill, V Zhao, “An Advanced Compliance Monitor for Patients Undergoing Brace Treatment for Idiopathic Scoliosis”, Medical Engineering & Physics, 37(2):203-209, 2015Chalmers E, Mizianty M, Parent E, Yuan Y, Lou E, “Toward maximum-predictive-value classification”, Pattern Recognition, 47(12): 3949-3958, 2014
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