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Deep Learning-Powered Powder Bed Fusion in Precision Medicine

https://doi.org/10.2139/ssrn.4755049
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Understanding the effects of formulation and process variables on the printlets quality manufactured by selective laser sintering 3D printing
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M3DISEEN: A novel machine learning approach for predicting the 3D printability of medicines
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3D-printing of lopinavir printlets by selective laser sintering and quantification of crystalline fraction by XRPD-chemometric models
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Machine learning predicts the effect of food on orally administered medicines
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Machine learning using multi-modal data predicts the production of selective laser sintered 3D printed drug products
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Machine learning predicts 3D printing performance of over 900 drug delivery systems
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Selective Laser Sintering of a Photosensitive Drug: Impact of Processing and Formulation Parameters on Degradation, Solid State, and Quality of 3D-Printed Dosage Forms
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Pattern Recognition and Machine Learning
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Artificial Neural Network Algorithms for 3D Printing
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Selective Laser Sintering 3D Printing of Orally Disintegrating Printlets Containing Ondansetron
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Understanding the Effect of Energy Density and Formulation Factors on the Printability and Characteristics of SLS Irbesartan Tablets—Application of the Decision Tree Model
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3D Printing of Pediatric Medication: The End of Bad Tasting Oral Liquids?—A Scoping Review
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The Advent of a New Era in Digital Healthcare: A Role for 3D Printing Technologies in Drug Manufacturing?
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Four-Dimensional Printed Construct from Temperature-Responsive Self-Folding Feedstock for Pharmaceutical Applications with Machine Learning Modeling
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Scikit-learn: Machine Learning in Python
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Simple and Scalable Predictive Uncertainty Estimation using Deep Ensembles
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A Unified Approach to Interpreting Model Predictions
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UMAP: Uniform Manifold Approximation and Projection for Dimension Reduction
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Recent advances in machine learning algorithms for sintering processes
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