Date of Award


Degree Type


Degree Name

Master of Mechanical Engineering


Mechanical Engineering

First Advisor

Ahmet Erdemir

Second Advisor

Brian Davis

Third Advisor

Shawn Ryan


Tissue indentation response is an important metric for understanding how different musculoskeletal regions respond to loading and is a function of the tissue’s form. Modem imaging techniques provide information about the internal structures of human tissue. Ultrasound remains one of the most common imaging techniques performed, given its portability and low costs. Prior work and data collection on 100 patients involved the collection of ultrasound images at eight different locations across the musculoskeletal extremities. Given the tissue structure information that the medical imaging provided, it was hypothesized that the mechanical properties of the tissue could be predicted from this data. This work aimed to incorporate various forms of patient data into different machine learning models for the prediction of tissue indentation response. These surrogate models would be capable of prediction of tissue compliance once input features are provided, potentially making them relevant in the clinical domain. Eight different surrogate models were developed, with four statistics models built and four deep learning models built to assess which method and which input factors were most suitable for accurately predicting indentation mechanics. The first four models were informed by tissue thicknesses and indentation region. The statistics surrogate models consist of two pure statistical models, while the other two models were based on a physics-based interpretation of two springs in series. The statistical models showed reasonable capability of predicting tissue surface stiffness, with the mean absolute percent difference ranging from 25.4% to 29.7% across the four models. The deep learning approach was divided between two separate forms of deep learning. The first model was fed only demographic features, while a second model of demographics and manually extracted tissue thicknesses. These models also showed reasonable capability of predicting tissue indentation stiffness, with a mean absolute percent difference of 25.5% and 26.3%, respectively. A final modeling approach involved using convolutional neural networks, which utilized the raw ultrasound images. One model was only given the ultrasound image and gave a mean absolute percent difference of 31.5%. A final model consisted of the raw image, image metadata, and demographics and returned a mean absolute percent difference of 25.9%.