Image‐based real‐timefeedback control of magnetic digital microfluidics by artificialintelligence‐empoweredrapid object detector for automated in vitro diagnostics

Abstract

In vitro diagnostics (IVD) plays a critical role in healthcare and public health management. Magnetic digital microfluidics (MDM) perform IVD assays by manipulating droplets on an open substrate with magnetic particles. Automated IVD based on MDM could reduce the risk of accidental exposure to contagious pathogens among healthcare workers. However, it remains challenging to create a fully automated IVD platform based on the MDM technology because of a lack of effective feedback control system to ensure the successful execution of various droplet operations required for IVD. In this work, an artificial intelligence (AI)-empowered MDM platform with image-based real-time feedback control is presented. The AI is trained to recognize droplets and magnetic particles, measure their size, and determine their location and relationship in real time; it shows the ability to rectify failed droplet operations based on the feedback information, a function that is unattainable by conventional MDM platforms, thereby ensuring that the entire IVD process is not interrupted due to the failure of liquid handling. We demonstrate fundamental droplet operations, which include droplet transport, particle extraction, droplet merging and droplet mixing, on the MDM platform and show how the AI rectify failed droplet operations by acting upon the feedback information. Protein quantification and antibiotic resistance detection are performed on this AI-empowered MDM platform, and the results obtained agree well with the benchmarks. We envision that this AI-based feedback approach will be widely adopted not only by MDM but also by other types of digital microfluidic platforms to offer precise and error-free droplet operations for a wide range of automated IVD applications.

Virtual differential phase‐contrast and dark‐field imaging of x‐ray absorption images via deep learning

“…Artificial intelligence (AI) has great potential in medicine, 35 including assisting to evaluate the efficacy and side effects of potential drugs, 36 predicting end‐product quality and composition from early time point in‐process measurements during therapeutic cell manufacturing, 37 and developing an automated in vitro diagnostics platform for an effective feedback control 38 . Even though the animal specimens used in grating interferometers are disease‐free, the CT results (Figures 4, 5, and Figures S2 and S4) demonstrate the feasibility of using AI to learn absorption projections and use virtual projections to reconstruct.…”

“…Artificial intelligence (AI) has great potential in medicine, 35 including assisting to evaluate the efficacy and side effects of potential drugs, 36 predicting end-product quality and composition from early time point in-process measurements during therapeutic cell manufacturing, 37 and developing an automated in vitro diagnostics platform for an effective feedback control. 38 Even though the animal specimens used in grating interferometers are disease-free, the CT results (Figures 4, 5 In the presence of pathological processes in breast or lung tissues, this approach may provide radiologists with significantly enhanced contrast compared to routine absorption imaging, as is the case with radiology and CT. In addition, other forms of radiation images could be studied using the cross-modality image learning method, such as neutrons or atomic particles.…”

Advanced technologies for biomedical applications by emerging researchers in Asia‐Pacific

“…For example, Tang et al utilized an AI-empowered rapid object detector for real-time feedback control of magnetic digital microfluidics, which achieved automated in vitro diagnostics. 27 Tostado et al developed an AI-assisted single-cell phenomictranscriptomic platform, which showed great potential in elucidating mechanisms and therapeutic targets against immune response. 28 Misra et al introduced novel deep learning-based techniques for segmenting lesions and distinguishing between benign and malignant cases by analyzing B-mode and strain elastography images.…”

“…A significant trend is the combination of medical diagnosis techniques with the power of artificial intelligence (AI), machine learning and deep learning. For example, Tang et al utilized an AI‐empowered rapid object detector for real‐time feedback control of magnetic digital microfluidics, which achieved automated in vitro diagnostics 27 . Tostado et al developed an AI‐assisted single‐cell phenomic‐transcriptomic platform, which showed great potential in elucidating mechanisms and therapeutic targets against immune response 28 .…”

An Artificial Intelligence-Assisted Digital Microfluidic System for Multistate Droplet Control

“…26,27 These sensing and control methods not only enhance the droplet manipulation stability but also optimize the motion performance of the droplet. 26 Imaging technologies, which include background subtraction methods, edge detection, object detection, and image segmentation, [28][29][30][31] are applied to droplet motion recognition. They offer a non-contact, real-time solution for signal acquisition, resulting in rapid and accurate detection and tracking of droplets, even transparent ones.…”