Theranostic Pancreatic Ultrasound in Pediatric Clinical Medicine
Open Access DepositedA Focus on the Diabetic Pancreas via Modeling and Machine Learning Imaging Classification
Theranostics, or the ability to both treat and diagnose pathologies, offers a unimodal medical approach to unique pathologies. Our lab has previously elucidated a clear potential for therapeutic ultrasound to ameliorate type 2 diabetes mellitus. In silico, in vitro, and in vivo models support an association between acoustic stimulation and insulin release in pancreatic beta cells, the primary cell type responsible in blood glucose regulation. To translate therapeutic ultrasound models into the clinical space, medical imaging of the pancreas (location of beta cells) is necessary to provide safe and effective stimulation to the pancreatic target site. Necessary imaging of the pancreas can be deployed via the same modality of ultrasound. In fact, in pediatric contexts where radiation exposure and prolonged imaging time are key barriers to accurate, real-time imaging for surgical intervention, theranostic ultrasound is the optimal choice for future image-guided therapies. This dissertation offers the first ultrasound imaging repository of the pediatric diabetic pancreas (44 images across 11 patients). This repository was explored for both imaging features and key pancreatic anatomical information. Diabetic and nondiabetic images of the pediatric pancreas were analyzed for quantitative differences and relationships between pancreatic ultrasound and diabetic status. Patient-specific therapeutic ultrasound simulations were performed to offer a clinically-relevant translation for future clinical work. Finally, dynamic molecular modeling of key mechanosensitive proteins believed to direct the mechanism of insulin release in the pancreas was performed. This dissertation offers multiple, clinical tools for the translation of theranostic ultrasound into the pediatric diabetic clinical context. An active, IRB-approved clinical trial is still active from the work performed during this dissertation. Future efforts will focus on expanding the imaging repository to include more diabetic pancreatic ultrasound samples. As the repository expands, efforts to expand machine learning efforts in diabetes detection will continue.
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Meredith_gwu_0075A_17614.pdf | 2025-12-12 | Open Access |
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