Biomedical Engineering and Bioengineering Commons^™

System-Characterized Artificial Intelligence Approaches For Cardiac Cellular Systems And Molecular Signature Analysis, Ziqian Wu

Dartmouth College Ph.D Dissertations

The dissertation presents a significant advancement in the field of cardiac cellular systems and molecular signature systems by employing machine learning and generative artificial intelligence techniques. These methodologies are systematically characterized and applied to address critical challenges in these domains. A novel computational model is developed, which combines machine learning tools and multi-physics models. The main objective of this model is to accurately predict complex cellular dynamics, taking into account the intricate interactions within the cardiac cellular system. Furthermore, a comprehensive framework based on generative adversarial networks (GANs) is proposed. This framework is designed to generate synthetic data that faithfully …

Using Natural Language Processing And Sentiment Analysis To Augment Traditional User-Centered Design: Development And Usability Study, Curtis L. Petersen, Ryan Halter, David Kotz, Lorie Loeb, Summer B. Cook, Dawna M. Pidgeon, Brock Christensen, John A. Batsis

Dartmouth Scholarship

Background: Sarcopenia, defined as the age-associated loss of muscle mass and strength, can be effectively mitigated through resistance-based physical activity. With compliance at approximately 40% for home-based exercise prescriptions, implementing a remote sensing system would help patients and clinicians to better understand treatment progress and increase compliance. The inclusion of end users in the development of mobile apps for remote-sensing systems can ensure that they are both user friendly and facilitate compliance. With advancements in natural language processing (NLP), there is potential for these methods to be used with data collected through the user-centered design process.

Objective: This study aims …

Utilizing Neural Networks And Wearables To Quantify Hip Joint Angles And Moments During Walking And Stair Ascent, Megan V. Mccabe

ENGS 88 Honors Thesis (AB Students)

Wearable sensors were leveraged to develop two methods for computing hip joint angles and moments during walking and stair ascent that are more portable than the gold standard. The Insole-Standard (I-S) approach replaced force plates with force-measuring insoles and achieved results that match the curvature of results from similar studies. Peaks in I-S kinetic results are high due to error induced by applying the ground reaction force to the talus. The Wearable-ANN (W-A) approach combines wearables with artificial neural networks to compute the same results. Compared against the I-S, the W-A approach performs well (average rRMSE = 18%, R^{2 …}

Abso2luteu-Net: Tissue Oxygenation Calculation Using Photoacoustic Imaging And Convolutional Neural Networks, Kevin Hoffer-Hawlik, Geoffrey P. Luke

ENGS 88 Honors Thesis (AB Students)

Photoacoustic (PA) imaging uses incident light to generate ultrasound signals within tissues. Using PA imaging to accurately measure hemoglobin concentration and calculate oxygenation (sO₂) requires prior tissue knowledge and costly computational methods. However, this thesis shows that machine learning algorithms can accurately and quickly estimate sO₂. absO₂luteU-Net, a convolutional neural network, was trained on Monte Carlo simulated multispectral PA data and predicted sO₂ with higher accuracy compared to simple linear unmixing, suggesting machine learning can solve the fluence estimation problem. This project was funded by the Kaminsky Family Fund and the Neukom Institute.

A Gamos Plug-In For Geant4 Based Monte Carlo Simulation Of Radiation-Induced Light Transport In Biological Media, Adam K. Glaser, Stephen C. Kanick, Rongxiao Zhang, Pedro Arce, Brian W. Pogue

Dartmouth Scholarship

We describe a tissue optics plug-in that interfaces with the GEANT4/GAMOS Monte Carlo (MC) architecture, providing a means of simulating radiation-induced light transport in biological media for the first time. Specifically, we focus on the simulation of light transport due to the Čerenkov effect (light emission from charged particle's traveling faster than the local speed of light in a given medium), a phenomenon which requires accurate modeling of both the high energy particle and subsequent optical photon transport, a dynamic coupled process that is not well-described by any current MC framework. The results of validation simulations show excellent agreement with …