Evaluating and tracking wound size is a fundamental metric for the wound assessment process. Good location and size estimates can enable proper diagnosis and effective treatment. Traditionally, laboratory wound healing studies include a collection of images at uniform time intervals exhibiting the wounded area and the healing process in the test animal, often a mouse. These images are then manually observed to determine key metrics —such as wound size progress— relevant to the study. However, this task is a time-consuming and laborious process. In addition, defining the wound edge could be subjective and can vary from one individual to another even among experts. Furthermore, as our understanding of the healing process grows, so does our need to efficiently and accurately track these key factors for high throughput (e.g., over large-scale and long-term experiments). Thus, in this study, we develop a deep learning-based image analysis pipeline that aims to intake non-uniform wound images and extract relevant information such as the location of interest, wound only image crops, and wound periphery size over-time metrics. In particular, our work focuses on images of wounded laboratory mice that are used widely for translationally relevant wound studies and leverages a commonly used ring-shaped splint present in most images to predict wound size. We apply the method to a dataset that was never meant to be quantified and, thus, presents many visual challenges. Additionally, the data set was not meant for training deep learning models and so is relatively small in size with only 256 images. We compare results to that of expert measurements and demonstrate preservation of information relevant to predicting wound closure despite variability from machine-to-expert and even expert-to-expert. The proposed system resulted in high fidelity results on unseen data with minimal human intervention. Furthermore, the pipeline estimates acceptable wound sizes when less than 50% of the images are missing reference objects.

From the simplest unicellular organisms to complex animals, feedback control based on sensing and actuation is a staple of self‐regulation in biological processes and is a key to life itself. Malfunctioning of this control loop can often lead to disease or death. Bioelectronic devices that interface electronics with biological systems can be used for sensing and actuation of biological processes and have potential for novel therapeutic applications. Due to the complexity of biological systems and the challenge of affecting their innate self‐regulation, closing the loop between sensing and actuation with bioelectronics is difficult to achieve. Herein, bioelectronic proton‐conducting devices are integrated with fluorescence sensing using machine learning to provide closed‐loop control of bioelectronic actuation in living cells. Proton‐conducting bioelectronic devices control pH in a microfluidic system that houses pluripotent mammalian stem cells. This pH control affects the membrane voltage (V_mem) of the cells that is measured using genetically encoded fluorescent V_mem reporters. In this fashion, proof‐of‐concept real‐time control of V_mem toward a desired set‐point is demonstrated. Given the importance of V_mem in cell function, differentiation, and proliferation, this proof‐of‐concept opens up many possibilities in bioelectronic closed‐loop control of cell systems.

Bioelectronic devices have shown their unprecedented potential in a wide range of biomedical applications due to their vast functionality. To fully unleash their potential, bioelectronic devices should be able to precisely respond to real-time changes in the environment to drive biological systems' response towards the desired goals. However, controlling the biological systems' response with bioelectronic devices is challenging due to the presence of uncertainties, stochasticity, unmodeled dynamics, and complex nonlinearities. Here, we demonstrate the promise of leveraging tools in synthetic biology with an online machine learning (ML)-based feedback controller to achieve a precise spatiotemporal response of biological systems using bioelectronics driven by an adaptive external “sense and respond” learning algorithm. The proposed ML-based feedback controller leverages the learning capability and low computational complexity of a class of artificial neural networks called the Radial Basis Function (RBF) network. Lyapunov analysis is provided to prove stability of the proposed controller. The satisfactory performance of the proposed method is experimentally validated by maintaining media pH using bioelectronic proton pumps.