Table 1. Summary of ML algorithms in bioprinting.

Application area	Tasks	ML methods	Ref.
Image analysisbased in situ monitoring	Identify cone mode in scaffold fabrication process in EHD jetting	CNN	[14]
	Identify deposit fibers’ continuity, uniformity, and regularity in EBB	Four-layer CNN, ResNeXt-50 network, linear SVM classifier	[25]
	Extract the flow pattern and droplet evolution in DBB	Deep recurrent neural network (DRNN)	[29]
Printing parameter optimization	Predict the electrospun diameter of PCL/Gt nanofibers	Multiple regression, multilayer perceptron ANN	[24]
	Identify suitable printing conditions for PPF scaffold in EBB	Random forest classifiers (RFc), random forest regression (RFr)	[26]
	Optimize ink composition and printing parameters in EBB	SVM classifier	[27]
	Predict the droplet diameter in EHD inkjet printing	Statistical regression analysis, GA-NN, BPNN	[28]
	Optimize printing parameters for GelMA and HAMA bioinks	Bayesian optimization (BO)	[30]
	Optimize the droplet size and printing frequency in EHD inkjet	Desirability function analysis	[31]
Biomaterial/bioink optimization	Achieve high shape fidelity in EBB	Inductive logic programming, multiple regression	[32]
	Achieve ideal linewidth and shape fidelity in EBB	Hierarchical machine learning	[33]
	Predict filament diameter in EBB	RFr, linear regression, intrastudy linear regression	[34]
Cell performance analysis	Predict cell viability	SVM regression, linear regression, RFr, SVM classifier, RFc, logistic regression classifier	[34]
	Detect the impact of scaffold morphology on cell shape phenotypes	SVM classifier	[35]
	Analyze cell-scaffold interaction	AD-GAN	[36]
	Predict cell-material interactions in fibrous scaffold	RFr model	[37]
	Associate cell morphologies with diverse microenvironment	SVM classifier	[38]