CURRENT RESEARCH PROJECTS
Smart Dental Implant System
Osseointegrated dental implants have become a routine component of a daily dental practice. A significant body of evidence indicates that the accumulation of bacterial biofilms (dental plaque) at the soft tissue-implant interface and the subsequent local inflammatory response seems to be key in the pathogenesis of the peri-implant mucositis. Thus, we developed an ambulatory photo-biomodulation therapy using a seamless, human oral motion powered Smart Dental Implant (SDI) that can prevent the formation of biofilms on implants (and restorative components) and reduce cell inflammation as well as regenerate tissue to minimize the occurrence of implant failures.
* US provisional patent application was filed. No. 63/380,494.
 H. E. Kim, S. Islam, M. C. Park, A. Kim, and G. Hwang “A Comprehensive Analysis of Near- Contact In Situ Photobiomodulation Therapy in the Host-Bacteria Interaction Model,” Advanced Biosystem, vol. 4, no. 3, pp1900227, 2020
 A. Kim, J. Zhou, S. Song, and B. Ziaie, “An Implantable Ultrasonically-Powered Micro-Light- Source (μLight) for Photodynamic Therapy,” Nature Scientific Reports, vol. 9, no. 1395, 2019
Smart Stent for Autonomous Post-Endovascular Aneurysm Repair Surveillance
Endovascular aneurysm repair (EVAR) is an established and reliable surgical treatment of abdominal aortic aneurysm (AAA), where a covered stent is implanted to prevent blood flow in the aneurysm sac. Although current EVAR is an effective technique, it requires long-term monitoring for the post-operative complications (e.g., endoleak, stent occlusion), which usually requires expensive radiologic imaging techniques such as ultrasonography, computed tomography (CT), or magnetic resonance imaging (MRI). In this project, we present an accessible and continuous post-EVAR surveillance scheme by developing a smart stent that features ultrasonic powering, blood flow sensing, and integrated wireless electronics.
 J. Zhou, A. Kim, and B. Ziaie, “An Ultrasoncially Controlled Switching System for Power Management in Implantable Devices,” Biomedical Micro Devices, vol. 20, no 2, pp1-8, 2018
An Ultrasonically Powered Implantable Micro Electrolytic Ablation for Tumor Necrosis
Electrolytic ablation is a technique that can remove non- resectable tumors from internal organs (such as liver, kidney, pancreas, etc.) with highly localized control to minimize harm to adjoining healthy tissue. Here, we aim to utilize the principle of electrolytic ablation in an implantable platform and power it by an external ultrasonic wave. The implantable micro electrolytic ablation (IMEA) will address challenges of the current existing tethered method such as constraints in electrode size, multiple targets and repeated treatments in case of cancer recurrence. We characterized the prototype of IMEA in an agarose gel containing phenolphthalein to simulate internal body tissue. Color change in phenolphthalein shows that the device responds to external ultrasonic stimulation and shows electrolytic behavior in an area around the electrodes that spreads outward with time.
 A. Kim, S. K. Lee, T. Parupudi, R. Rahimi, S. H. Song, M. C. Park, S. Islam, J. Zhou, A. K. Majumdar, J. S. Park, J. M. Yoo, and B. Ziaie, “An Ultrasonically-Powered Implantable Microprobe for Electrolytic Ablation,” Nature Scientific Reports, vol. 10, no 1, pp1-9, 2020
A Machine Learning Enabled Wireless Intracranial Brain Deformation Sensing System
A leading cause of traumatic brain injury (TBI) is intracranial brain deformation due to mechanical impact. This deformation is viscoelastic and differs from a traditional rigid transformation. In this paper, we describe a machine learning enabled wireless sensing system that predicts the trajectory of intracranial brain deformation. The sensing system consists of an implantable soft magnet and an external magnetic sensor array. Machine learning algorithm predicts the brain deformation by interpreting the magnetic sensor outputs created by the change in position of the implanted soft magnet. Three different machine learning models were trained on calibration data: (1) random forests, (2) k-nearest neighbors, and (3) a multi-layer perceptron-based neural network. These models were validated using both in vitro (a needle inserted into PVC gel) and in vivo (blast exposure to live and dead rat brains) experiments. These results suggest that the proposed machine learning enabled sensor system can be an effective tool for measuring in situ brain deformation.
 A. Kim, S. Song, N. S. Race, T. Zhang, R. Shi, and B. Ziaie,“A Wireless Intracranial Brain Deformation Sensing System for Blast-Induced Traumatic Brain Injury,” Nature Scientific Report, 5, 2015