Machine Learning and Medical Devices: The Next Step for Tissue Engineering

Hannah A. Pearce; Antonios G. Mikos

doi:10.1016/j.eng.2021.05.014

PDF(537 KB)

Engineering ›› 2021, Vol. 7 ›› Issue (12) : 1704-1706. DOI: 10.1016/j.eng.2021.05.014

Machine Learning and Medical Devices: The Next Step for Tissue Engineering

Author information +

History +

Cite this article

EndNote

Ris (Procite)

Bibtex

Download citation ▾

Hannah A. Pearce, Antonios G. Mikos. Machine Learning and Medical Devices: The Next Step for Tissue Engineering. Engineering, 2021, 7(12): 1704‒1706 https://doi.org/10.1016/j.eng.2021.05.014

References

Publishing order | Descend order by publishing year | Descend order by cited within

[[1]]

Ghaemi RV, Siang LC, Yadav VG. Improving the rate of translation of tissue engineering products. Adv Healthc Mater 2019;8(19):1900538.

[[2]]

Fargen KM, Frei D, Fiorella D, McDougall CG, Myers PM, Hirsch JA, et al. The FDA approval process for medical devices: an inherently flawed system or a valuable pathway for innovation? J Neurointerv Surg 2013;5(4):269–75.

[[3]]

Williams DF. Challenges with the development of biomaterials for sustainable tissue engineering. Front Bioeng Biotechnol 2019;7:127.

[[4]]

Hakimi O, Krallinger M, Ginebra MP. Time to kick-start text mining for biomaterials. Nat Rev Mater 2020;5(8):553–6.

[[5]]

Sharpe C, Wiest T, Wang P, Seepersad CC. A comparative evaluation of supervised machine learning classification techniques for engineering design applications. J Mech Des 2019;141(12):121404.

[[6]]

Mazurenko S, Prokop Z, Damborsky J. Machine learning in enzyme engineering. ACS Catal 2020;10(2):1210–23.

[[7]]

Ray S. A quick review of machine learning algorithms. IEEE 2019:35–9.

[[8]]

Coley CW, Green WH, Jensen KF. Machine learning in computer-aided synthesis planning. Acc Chem Res 2018;51(5):1281–9.

[[9]]

Conev A, Litsa EE, Perez MR, Diba M, Mikos AG, Kavraki LE. Machine learningguided three-dimensional printing of tissue engineering scaffolds. Tissue Eng Part A 2020;26(23–24):1359–68.

[[10]]

Allen B. The role of the FDA in ensuring the safety and efficacy of artificial intelligence software and devices. J Am Coll Radiol 2019;16(2):208–10.

[[11]]

Tison GH, Sanchez JM, Ballinger B, Singh A, Olgin JE, Pletcher MJ, et al. Passive detection of atrial fibrillation using a commercially available smartwatch. JAMA Cardiol 2018;3(5):409–16.

[[12]]

Serhani MA, El Kassabi HT, Ismail H, Nujum Navaz A. ECG monitoring systems: review, architecture, processes, and key challenges. Sensors 2020; 20(6):1796.

[[13]]

Siau K, Wang W. Building trust in artificial intelligence, machine learning, and robotics. Cutter Bus Technol J 2018;31(2):47–53.

[[14]]

Mariescu-Istodor R, Jormanainen I. Machine Learning Exercises for High School Students. In: Proceedings of 19th Koli Calling International Conference on Computing Education Research. 2019 Nov 21–24; Koli, Finland. New York: the Association for Computing Machinery.