Artificial neural networks for pattern recognition in patient pain drawings

Date of Award




Degree Name

Doctor of Philosophy (Ph.D.)

First Committee Member

Mark D. Brown - Committee Chair

Second Committee Member

David B. Hertz - Committee Member


The purpose of this research is to study the pattern recognition capabilities of artificial neural networks on quantitative spatial-anatomic data obtained from the pain drawings of low back pain patients.Experiments are performed with a self-correcting artificial intelligence program to determine if they can be used to categorize patient pain drawings into clinically relevant classes. The study is designed to demonstrate the capabilities of fundamental artificial neural network theory for (A) recognizing patterns in empirical patient data, and (B) using knowledge communicated by experts on a pragmatic, ill-defined medical application. Also, the analysis increases the current body of knowledge regarding the quantitative spatial-anatomic characteristics of pain drawing measurements.Our results indicate that artificial neural networks can achieve classification accuracy comparable to human experts and traditional discriminant analysis without the overhead associated with the latter.Artificial neural systems yield useful, often neglected, diagnostic information through their topology, and may assist in producing more reliable and more comprehensive outcomes from other diagnostic systems. The most accurate ANN performed with 48% overall accuracy compared to 51% for human experts and 47.4% for traditional discriminant analysis.


Engineering, Biomedical; Engineering, Electronics and Electrical; Computer Science

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