Artificial Intelligence in Biomaterials: A Comprehensive Review
Source abstract
Abstract
The importance of biomaterials lies in their fundamental roles in medical applications such as tissue engineering, drug delivery, implantable devices, and radiological phantoms, with their interactions with biological systems being critically important. In recent years, advancements in deep learning (DL), artificial intelligence (AI), machine learning (ML), supervised learning (SL), unsupervised learning (UL), and reinforcement learning (RL) have significantly transformed the field of biomaterials. These technologies have introduced new possibilities for the design, optimization, and predictive modeling of biomaterials. This review explores the applications of DL and AI in biomaterial development, emphasizing their roles in optimizing material properties, advancing innovative design processes, and accurately predicting material behaviors. We examine the integration of DL in enhancing the performance and functional attributes of biomaterials, explore AI-driven methodologies for the creation of novel biomaterials, and assess the capabilities of ML in predicting biomaterial responses to various environmental stimuli. Our aim is to elucidate the pivotal contributions of DL, AI, and ML to biomaterials science and their potential to drive the innovation and development of superior biomaterials. It is suggested that future research should further deepen these technologies’ contributions to biomaterials science and explore new application areas.
Source: crossref · Quality: baseline
Research context
OpenAlex topics: Machine Learning in Materials Science · Advanced Neural Network Applications · Industrial Vision Systems and Defect Detection
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
Affiliations
Department of Computer Engineering, Ankara University, Ankara 06830, Turkey
Institute of Nuclear Sciences, Ankara University, Ankara 06830, Turkey
Department of Artificial Intelligence and Data Engineering, Ankara University, Ankara 06830, Turkey
Fonet Information Technologies, Ankara 06520, Turkey
Faculty of Medicine and Health Technology, Tampere University, 33720 Tampere, Finland
VTT Technical Research Centre of Finland, 33101 Tampere, Finland
References (100+)
Show source-provided references
1. Material structure-property linkages using three-dimensional convolutional neural networks10.1016/j.actamat.2017.11.053 ↗
2. Robot-scientists will lead tomorrow’s biomaterials discovery10.1016/j.cobme.2018.03.005 ↗
3. Machine Learning-Driven Biomaterials Evolution10.1002/adma.202102703 ↗
4. Supervised Learning and Mass Spectrometry Predicts the in Vivo Fate of Nanomaterials10.1021/acsnano.9b02774 ↗
5. Time to kick-start text mining for biomaterials10.1038/s41578-020-0215-z ↗
6. Data-Driven Prediction of Protein Adsorption on Self-Assembled Monolayers toward Material Screening and Design10.1021/acsbiomaterials.0c01008 ↗
7. Assessing Biomaterial-Induced Stem Cell Lineage Fate by Machine Learning-Based Artificial Intelligence10.1002/adma.202210637 ↗
8. Al-Kharusi, G., Dunne, N.J., Little, S., and Levingstone, T.J. (2022). The Role of Machine Learning and Design of Experiments in the Advancement of Biomaterial and Tissue Engineering Research. Bioengineering, 9.10.3390/bioengineering9100561 ↗
9. A Review for Machine Learning Applications in Characterizing Biomaterials and Biological Materials Properties10.34297/AJBSR.2021.13.001893 ↗
10. Machine learning and big data provide crucial insight for future biomaterials discovery and research10.1016/j.actbio.2021.05.053 ↗
11. Machine learning-based image processing in materials science and engineering: A review10.1016/j.matpr.2022.01.200 ↗
12. Computational intelligence-based design of biomaterials
13. Redefining biomaterial biocompatibility: Challenges for artificial intelligence and text mining10.1016/j.tibtech.2023.09.015 ↗
14. Mitchell, M. (2019). Artificial Intelligence: A Guide for Thinking Humans. Farrar, Straus and Giroux.
15. Bishop, C.M. (2006). Pattern Recognition and Machine Learning, Springer.
16. Organizational decision-making structures in the age of artificial intelligence
17. Machine learning and deep learning10.1007/s12525-021-00475-2 ↗
18. Goodfellow, I., Bengio, Y., and Courville, A. (2016). Deep Learning, MIT Press.
19. Deep learning10.1038/nature14539 ↗
20. Enhancing human decision making using deep learning for complex problems
21. Hastie, T., Tibshirani, R., and Friedman, J. (2009). The Elements of Statistical Learning: Data Mining, Inference, and Prediction, Springer.10.1007/978-0-387-84858-7 ↗
22. Montgomery, D.C., Peck, E.A., and Vining, G.G. (2012). Introduction to Linear Regression Analysis, John Wiley & Sons.
23. Murphy, K.P. (2012). Machine Learning: A Probabilistic Perspective, MIT Press.
24. Data clustering: 50 years beyond K-means10.1016/j.patrec.2009.09.011 ↗
25. MacQueen, J. (1967). Some methods for classification and analysis of multivariate observations. Proceedings of the Fifth Berkeley Symposium on Mathematical Statistics and Probability, University of California Press. No. 14.
26. Hierarchical clustering schemes10.1007/BF02289588 ↗
27. Jolliffe, I.T. (2002). Principal Component Analysis, Springer.
28. Visualizing data using t-SNE
29. Agrawal, R., Imieliński, T., and Swami, A. (1993, January 26–28). Mining association rules between sets of items in large databases. Proceedings of the 1993 ACM SIGMOD International Conference on Management of Data, Washington, DC, USA.10.1145/170035.170072 ↗
30. Theodoridis, S., and Koutroumbas, K. (2008). Pattern Recognition, Academic Press.
31. Survey of clustering algorithms10.1109/TNN.2005.845141 ↗
32. Sutton, R.S., and Barto, A.G. (2018). Reinforcement Learning: An Introduction, MIT Press.
33. Amodei, D., Olah, C., Steinhardt, J., Christiano, P., Schulman, J., and Mané, D. (2016). Concrete Problems in AI Safety. arXiv.
34. Ruder, S. (2016). An overview of gradient descent optimization algorithms. arXiv.
35. Yosinski, J., Clune, J., Bengio, Y., and Lipson, H. (2014). How transferable are features in deep neural networks?. Advances in Neural Information Processing Systems, MIT Press.
36. High fidelity FEM based on deep learning for arbitrary composite material structure10.1016/j.compstruct.2024.118176 ↗
37. Machine learning-based accelerated property prediction of two-phase materials using microstructural descriptors and finite element analysis10.1016/j.commatsci.2021.110328 ↗
38. Multi-fidelity machine learning based uncertainty quantification of progressive damage in composite laminates through optimal data fusion10.1016/j.engappai.2023.106647 ↗
39. Harnessing deep learning for physics-informed prediction of composite strength with microstructural uncertainties10.1016/j.commatsci.2021.110663 ↗
40. Machine learning prediction of elastic properties and glass-forming ability of bulk metallic glasses10.1557/mrc.2019.44 ↗
41. Machine learning as a tool to design glasses with controlled dissolution for healthcare applications10.1016/j.actbio.2020.02.037 ↗
42. Structure-Mechanical Stability Relations of Metal-Organic Frameworks via Machine Learning10.1016/j.matt.2019.03.002 ↗
43. Medical devices biomaterials—A review
44. A review of the applications of data mining and machine learning for the prediction of biomedical properties of nanoparticles10.1016/j.cmpb.2016.04.025 ↗
45. Random forest classifier for high entropy alloys phase diagnosis10.1007/s13370-024-01198-1 ↗
46. Resolving Transition Metal Chemical Space: Feature Selection for Machine Learning and Structure–Property Relationships10.1021/acs.jpca.7b08750 ↗
47. CA method with machine learning for simulating the grain and pore growth of aluminum alloys10.1016/j.commatsci.2017.09.059 ↗
48. Metallic Metal–Organic Frameworks Predicted by the Combination of Machine Learning Methods and Ab Initio Calculations10.1021/acs.jpclett.8b01707 ↗
49. Towards understanding mechanisms governing cytotoxicity of metal oxides nanoparticles: Hints from nano-QSAR studies
50. Strategies and Software for Machine Learning Accelerated Discovery in Transition Metal Chemistry10.1021/acs.iecr.8b04015 ↗
51. Toward Effective Utilization of Methane: Machine Learning Prediction of Adsorption Energies on Metal Alloys
52. Machine learning assisted design of high entropy alloys with desired property10.1016/j.actamat.2019.03.010 ↗
53. Shi, S., and Xıong, J. (2020). Data for: A machine-learning approach to predicting and understanding the properties of amorphous metallic alloys. Mendeley Data.
54. Recent advances in computational modeling of MOFs: From molecular simulations to machine learning10.1016/j.ccr.2023.215112 ↗
55. Structure/property studies of polymeric gene delivery using a library of poly(β-amino esters)10.1016/j.ymthe.2004.11.015 ↗
56. QSAR Models for the Analysis of Bioresponse Data from Combinatorial Libraries of Biomaterials10.1002/qsar.200420062 ↗
57. 3-Polymer surface functionalities that control human embryoid body cell adhesion revealed by high throughput surface characterization of combinatorial material microarrays10.1016/j.biomaterials.2010.08.028 ↗
58. High throughput methods applied in biomaterial development and discovery10.1016/j.biomaterials.2009.09.037 ↗
59. Modelling human embryoid body cell adhesion to a combinatorial library of polymer surfaces10.1039/c2jm34782b ↗
60. A decade of progress in tissue engineering10.1038/nprot.2016.123 ↗
61. Scoping the polymer genome: A roadmap for rational polymer dielectrics design and beyond10.1016/j.mattod.2017.11.021 ↗
62. Using machine learning and a data-driven approach to identify the small fatigue crack driving force in polycrystalline materials10.1038/s41524-018-0094-7 ↗
63. Design of self-assembly dipeptide hydrogels and machine learning via their chemical features10.1073/pnas.1903376116 ↗
64. Machine learning metrology of cell confinement in melt electrowritten three-dimensional biomaterial substrates10.1038/s41378-019-0055-4 ↗
65. Structure–Property Correlation Study for Organic Photovoltaic Polymer Materials Using Data Science Approach10.1021/acs.jpcc.0c00517 ↗
66. Immune-Instructive Polymers Control Macrophage Phenotype and Modulate the Foreign Body Response In Vivo10.1016/j.matt.2020.03.018 ↗
67. End-to-End Deep Learning Model to Predict and Design Secondary Structure Content of Structural Proteins10.1021/acsbiomaterials.1c01343 ↗
68. Yu, C.-H., Khare, E., Narayan, O.P., Parker, R., Kaplan, D.L., and Buehler, M.J. (2022). ColGen: An end-to-end deep learning model to predict thermal stability of de novo collagen sequences. J. Mech. Behav. Biomed. Mater., 125.10.1016/j.jmbbm.2021.104921 ↗
69. Applied machine learning as a driver for polymeric biomaterials design10.1038/s41467-023-40459-8 ↗
70. Machine learning approaches for elastic localization linkages in high-contrast composite materials10.1186/s40192-015-0042-z ↗
71. Deep learning approaches for mining structure-property linkages in high contrast composites from simulation datasets10.1016/j.commatsci.2018.05.014 ↗
72. Bioinspired hierarchical composite design using machine learning: Simulation, additive manufacturing, and experiment10.1039/C8MH00653A ↗
73. Artificial intelligence design algorithm for nanocomposites optimized for shear crack resistance10.1088/2399-1984/ab36f0 ↗
74. Prediction of the Composition and Hardness of High-Entropy Alloys by Machine Learning10.1007/s11837-019-03704-4 ↗
75. Using Deep Learning to Predict Fracture Patterns in Crystalline Solids10.1016/j.matt.2020.04.019 ↗
76. Machine learning recommends affordable new Ti alloy with bone-like modulus10.1016/j.mattod.2019.08.008 ↗
77. Phase prediction in high entropy alloys with a rational selection of materials descriptors and machine learning models10.1016/j.actamat.2019.11.067 ↗
78. Ansah, I.B., Leming, M., Lee, S.H., Yang, J.-Y., Mun, C., Noh, K., An, T., Kim, S.L.D.-H., Kim, M., and Im, H. (2023). Label-free detection and discrimination of respiratory pathogens based on electrochemical synthesis of biomaterials-mediated plasmonic composites and machine learning analysis. Biosens. Bioelectron., 227.10.1016/j.bios.2023.115178 ↗
79. Current application status of multi-scale simulation and machine learning in research on high-entropy alloys10.1016/j.jmrt.2023.07.233 ↗
80. Fast extraction of three-dimensional nanofiber orientation from WAXD patterns using machine learning10.1107/S205225252300204X ↗
81. Identification and ranking biomaterials for bone scaffolds using machine learning and PROMETHEE10.1007/s42600-022-00257-5 ↗
82. Machine learning unifies flexibility and efficiency of spinodal structure generation for stochastic biomaterial design10.1038/s41598-023-31677-7 ↗
83. A general-purpose machine learning framework for predicting properties of inorganic materials10.1038/npjcompumats.2016.28 ↗
84. Dataset’s chemical diversity limits the generalizability of machine learning predictions10.1186/s13321-019-0391-2 ↗
85. Predicting the Young’s Modulus of Silicate Glasses using High-Throughput Molecular Dynamics Simulations and Machine Learning10.1038/s41598-019-45344-3 ↗
86. Predicting Young’s modulus of oxide glasses with sparse datasets using machine learning10.1016/j.jnoncrysol.2019.119643 ↗
87. Buehler, M.J. (2023). Diatom-inspired architected materials using language-based deep learning: Perception, transformation and manufacturing. Perspectives on the Mechanics of Fracture & Biological Materials, LuLu Publishing.
88. Towards detection of cancer biomarkers in human exhaled air by transfer-learning-powered analysis of odor-evoked calcium activity in rat olfactory bulb10.1016/j.heliyon.2023.e20173 ↗
89. The Effect of the Second Peak formed in Biomaterials used in a Slab Head Phantom on the Proton Bragg Peak
90. Ekinci, F., Acici, K., Asuroglu, T., and Soylu, B.E. MC TRIM Algorithm in Mandibula Phantom in Helium Therapy. In Healthcare. MDPI, 2023; Volume 11, p. 2523.10.3390/healthcare11182523 ↗
91. Analysing the effect of a cranium thickness on a Bragg peak range in the proton therapy: A TRIM and GEANT4 based study
92. Ekinci, F., Asuroglu, T., and Acici, K. (2023). Monte Carlo Simulation of TRIM Algorithm in Ceramic Biomaterial in Proton Therapy. Materials, 16.10.3390/ma16134833 ↗
93. A Monte Carlo Study for Soft Tissue Equivalency of Potential Polymeric Biomaterials Used in Carbon Ion Radiation Therapy10.1080/00295450.2023.2188144 ↗
94. Ekinci, F. (2022). Investigation of tissue equivalence of phantom biomaterials in 4He heavy ion therapy. Radiation Effects and Defects in Solids, Taylor and Francis.10.1080/10420150.2022.2153251 ↗
95. Effects of preheating and sonic delivery techniques on the internal adaptation of bulk-fill resin composites10.2341/19-241-L ↗
96. Development of a flexible composite based on vulcanized silicon casting with bismuth oxide and characterization of its radiation shielding effectiveness in diagnostic X-ray energy range and medium gamma-ray energies10.1016/j.net.2024.02.016 ↗
97. Investigation of the suitability of new developed epoxy based-phantom for child’s tissue equivalency in paediatric radiology10.1016/j.net.2021.07.002 ↗
98. 3D printed poly(ε-caprolactone) scaffolds modified with hydroxyapatite and poly(propylene fumarate) and their effects on the healing of rabbit femur defects10.1039/C7BM00514H ↗
99. Thyroid and contralateral breast surface dose variation in mammography: A phantom study on the role of breast tissue composition10.1140/epjp/s13360-024-05115-0 ↗
100. Big Data Creates New Opportunities for Materials Research: A Review on Methods and Applications of Machine Learning for Materials Design10.1016/j.eng.2019.02.011 ↗