Cold atmospheric plasmas (CAPs) are becoming a breakthrough technology in a variety of materials processing and characterization applications, including in plasma medicine. CAP jets (CAPJs) are a versatile tool in plasma medicine because they can be a low-cost, portable, point-of-care solution for a variety of biomedical applications. However, selecting operational parameters of CAPJs (or CAPs in general) remains an open challenge due to a variety of factors, including variability in patients (i.e., target interface), variability in CAPJ operation, sensitivity to disturbances and environmental conditions, and difficult-to-model dynamics of CAPs resulting in uncertain predictions about CAP-interface interactions. Predictive control has become the state-of-the-art in addressing aspects of the safety, reproducibility, and efficacy of CAP treatments. This dissertation addresses two open aspects of CAP control, specifically designing feasible embedded control systems for point-of-care CAPJs and designing individualized CAP treatment regimens. Together, these two aspects represent the overarching objective of this dissertation: enabling point-of-care devices for precision plasma medicine.
CAPJs for biomedical applications are often touted for their portability and point-of-care use. Additionally, medicine as a field is moving towards more targeted approaches to patient healthcare due to the influx of data from personal devices (e.g., smart wearables) that track health trends and physical activity and due to the importance of considering diverse patient profiles for equitable and efficacious medical treatments. This trend (part of a tendency towards "edge computing") combined with the nonlinear, multi-variable CAP dynamics calls for embedded control policies that are capable of implementation on resource-limited hardware. The first part of this dissertation provides a novel fusion of hardware and software design (aka "hardware-software co-design") of control policies to find optimal and feasible embedded control policies on resource-limited hardware. In particular, key elements of the end-to-end design pipeline include the digital control policy, the physical computing hardware, and the closed-loop performance measures of interest such as chemical/biological effects of CAPs on target interfaces. We demonstrate that a data-driven optimization framework based on Bayesian optimization (BO), which can simultaneously incorporate the control policy design and hardware considerations when implementing the control policy, can effectively design feasible embedded control policies that target multiple objectives. An estimation of the Pareto frontier (i.e., trade-off curve) can be generated via hardware-in-the-loop simulations and used to inform the design of real-time control policies.
Several applications in plasma medicine require repeated treatments to realize therapeutically effective treatment outcomes to avoid overdosing and/or to treat long-term conditions. Prior works have illustrated predictive control strategies are capable of safely delivering CAP treatments to patients, but these strategies generally rely on underlying assumptions of individual subject characteristics (i.e., empirical models based on population data). This consideration necessitates adaptive treatments that are updated via observations of treatment outcomes, which can be addressed through data-driven optimization. In simulations and experiments, we demonstrated that deep learning-based control policies, which are amenable to resource-limited hardware, can be updated directly using multi-objective BO. We demonstrated how deep learning-based control policies can be updated to find the optimal trade-offs in treatment objectives when characteristics of individual subjects may differ from the population. In a complementary direction, we developed a novel strategy to safely explore the individualized objective space without compromising on performance improvements. We demonstrated that our safe explorative BO strategy finds a balance between overly-cautious exploration that may get stuck at local optima and overly-eager exploration that may violate safety-critical constraints.
The primary focus of this dissertation was on the therapeutic benefits of CAPs. The final contribution of this dissertation investigated a novel aspect of CAPs for biomedical use: (biological) material characterization. We demonstrated that CAPs are uniquely capable of producing minimally destructive effects during interactions with biological tissues that can be used to identify and classify different tissue types. A key aspect of this finding is that real-time chemical and electrical measurements of plasma-tissue interactions can be analyzed in physics-informed ways and fed into machine learning strategies to predict the type of a biological tissue. Results from this study can have significant implications in non-invasive early skin cancer detection systems and/or in real-time surgical assistance.
To conclude, this dissertation presented results that illustrate an end-to-end journey from the design of physical computing hardware to the design of digital control policies to the design and characterization of (bio)chemical outcomes of plasma treatments in medicine. This dissertation established that data-driven optimization is a versatile tool to regulate and personalize the outcomes of CAP treatments. For medicine, BO mimics the doctor-patient interaction, and thus provides a natural augmentation to the medical toolkit. Future work may involve addressing additional challenges regarding connected devices and data-driven strategies (i.e., (cyber)security, privacy, distributed deployment), fusion of physics-structured learning with data, and evaluation of such methods in preclinical and clinical studies. The findings in this dissertation were grounded in plasma medicine, but can be broadly applicable to other non-equilibrium plasma applications, e.g., semiconductor processing.
