Fig. 5.

CV-based drug screening system. a Prediction of the therapeutic effect of drugs on tumor spheres by a convolutional neural network⁷⁴. The chip can simultaneously test six drug conditions and capture bright-field images and live/dead cell staining images of tumor cells after drug treatment. The bright-field images are input into a convolutional neural network to predict the viability of cells, and the living/dead cell staining images are used as the true values to evaluate the accuracy of the prediction results. b Combination of a hydrogel droplet platform and computer vision to screen the antisolvent crystallization conditions of active pharmaceutical ingredients⁷⁵. The method collects images of hydrogel droplets containing different drug crystals in serpentine channels and detects different drug crystal shapes in the hydrogel droplets by using an object detection algorithm. c Real-time drug screening by ultralarge-scale high-resolution imaging and computer vision. Video clips of Ca2+ ion signals in cells are recorded at 30 Hz and then analyzed offline. Within each region of interest, all image frames are accumulated to synthesize a grayscale map, and individual cells are then identified by the binarization algorithm ImageJ. The fluorescence intensity of these cells in each frame are extracted and resolved, revealing the rhythmic response of cardiomyocytes following drug injection⁷⁸