Microfluidics is a rapidly growing discipline that involves studying and manipulating fluids at reduced length scale and volume, typically on the scale of micro- or nanoliters. Under the reduced length scale and larger surface-to-volume ratio, advantages of low reagent consumption, faster reaction kinetics, and more compact systems are evident in microfluidics. However, miniaturization of microfluidic chips and systems introduces challenges of stricter tolerances in designing and controlling them for interdisciplinary applications. Recent advances in artificial intelligence (AI) have brought innovation to microfluidics from design, simulation, automation, and optimization to bioanalysis and data analytics. In microfluidics, the Navier–Stokes equations, which are partial differential equations describing viscous fluid motion that in complete form are known to not have a general analytical solution, can be simplified and have fair performance through numerical approximation due to low inertia and laminar flow. Approximation using neural networks trained by rules of physical knowledge introduces a new possibility to predict the physicochemical nature. The combination of microfluidics and automation can produce large amounts of data, where features and patterns that are difficult to discern by a human can be extracted by machine learning. Therefore, integration with AI introduces the potential to revolutionize the microfluidic workflow by enabling the precision control and automation of data analysis. Deployment of smart microfluidics may be tremendously beneficial in various applications in the future, including high-throughput drug discovery, rapid point-of-care-testing (POCT), and personalized medicine. In this review, we summarize key microfluidic advances integrated with AI and discuss the outlook and possibilities of combining AI and microfluidics.

Artificial intelligence techniques are used on chest X-ray images for accurate detection of diseases and this paper aims to develop a process which is capable of diagnosing COVID-19 using deep learning methods on X-ray images. For this purpose, we used Mask R-CNN method to train and test on the dataset to classify between patients infected and non-infected with COVID-19. The dataset used here contains a large number of frontal views of X-ray images which are an essential resource for the algorithms used in the development of tools for the detection of COVID-19. Using 668 chest X-ray images, the proposed model achieved an accuracy as high as 96.98%, specificity of 97.36% with the precision of 96.60%. The entire process is presented in detail. When a comparison table on the AI-based techniques is prepared, it is noticed that the Mask R-CNN technique on chest X-ray images provides better efficiency in the detection of COVID-19. The Mask R-CNN method is found to be accurate and robust in the detection of COVID-19 from chest X-ray images.

A non-invasive method of measuring bioimpedance is presented to study the effect of moisturizing cream on human skin. A novel two-electrode system is developed to get electrical access at the area of interest on human skin. Controlled experiments are conducted for a duration of 47 days. Bioimpedance is measured by LCR meter in the frequency range of 1 Hz to 5 kHz. The measured impedance is then fitted with the Cole model and the relevant Cole parameters are extracted. Analysis of variance is performed on the Cole parameters to gain statistical relevance. It is found that one of the Cole parameters, namely the relaxation-time changes significantly due to the treatment of the skin with a moisturizing cream at more than 95% confidence level. The relaxation-time increases sharply during the treatment period and drops sharply in the post-treatment period. The effectiveness of the two types of cream is studied. The gender distinguishability during the application of a moisturizing cream is also studied. A very generic bioimpedance measurement method to assess the physiological condition of local skin is demonstrated here and the method has potential application in human skin related diseases and treatments.

Accurate detection of non-alcoholic fatty liver disease (NAFLD) through biopsies is challenging. Manual detection of the disease is not only prone to human error but is also time-consuming. Using artificial intelligence and deep learning, we have successfully demonstrated the issues of the manual detection of liver diseases with a high degree of precision. This article uses various neural network-based techniques to assess non-alcoholic fatty liver disease. In this investigation, more than five thousand biopsy images were employed alongside the latest versions of the algorithms. To detect prominent characteristics in the liver from a collection of Biopsy pictures, we employed the YOLOv3, Faster R-CNN, YOLOv4, YOLOv5, YOLOv6, YOLOv7, YOLOv8, and SSD models. A highlighting point of this paper is comparing the state-of-the-art Instance Segmentation models, including Mask R-CNN, U-Net, YOLOv5 Instance Segmentation, YOLOv7 Instance Segmentation, and YOLOv8 Instance Segmentation. The extent of severity of NAFLD and non-alcoholic steatohepatitis was examined for liver cell ballooning, steatosis, lobular, and periportal inflammation, and fibrosis. Metrics used to evaluate the algorithms' effectiveness include accuracy, precision, specificity, and recall. Improved metrics are achieved by optimizing the hyperparameters of the associated models. Additionally, the liver is scored in order to analyse the information gleaned from biopsy images. Statistical analyses are performed to establish the statistical relevance in evaluating the score for different zones.

Janus particles, characterized by their anisotropic structure, exhibit rotational Brownian motion that causes periodic intensity fluctuations (blinking) when suspended in viscous fluids. This phenomenon provides a powerful optical signature for viscosity quantification. In this study, we developed a novel deep learning-based framework to analyze these blinking dynamics for rapid micro-volume viscosity estimation. We first performed extensive simulations of 1-μm Janus particles in fluids with viscosities ranging from 0.1 to 5.0 cP, systematically testing challenging conditions including high translational diffusion, particle aggregation, slow/fast blinking, high noise, and clinical scenarios. Experimental validation was conducted using real glycerol-water mixtures with viscosities ranging between 1.0 and 5.0 cP. A Python-based pipeline combining YOLOv8 instance segmentation and Trackpy tracking accurately detected and tracked individual particles across 140-frame video sequences recorded at 20 frames per second. The Continuous Wavelet Transform (CWT) was then applied to the intensity time-series of each tracked particle to generate time-frequency scalograms (0.05–20 Hz), from which flickering frequencies driven by rotational diffusion were extracted. Finally, a Convolutional Neural Network model, trained on a dataset of 480 CWT-generated intensity heatmaps (20 per viscosity level across 24 viscosity classes), achieved 95.8% accuracy with a mean error of only 0.33% and a 5-fold cross-validation accuracy of 92.7%. Single-blind tests on unseen experimental data yielded viscosity predictions with errors less than 2% with a computational time of less than 2 minutes, with aggregated and non-blinking particles effectively filtered in advance to ensure data quality. This work demonstrates the first seamless integration of instance segmentation, particle tracking, wavelet analysis, and deep learning for automated viscosity prediction, offering a rapid, non-invasive, and robust method with strong potential for applications in microfluidics, biosensing, and clinical diagnostics.