机器学习和医疗设备——组织工程的发展趋势

Hannah A. Pearce; Antonios G. Mikos

doi:10.1016/j.eng.2021.05.014

PDF(537 KB)

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

观点述评

机器学习和医疗设备——组织工程的发展趋势

作者信息 +

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

Author information +

History +

引用本文

EndNote

Ris (Procite)

Bibtex

导出引用

Hannah A. Pearce, Antonios G. Mikos. 机器学习和医疗设备——组织工程的发展趋势. Engineering. 2021, 7(12): 1704-1706 https://doi.org/10.1016/j.eng.2021.05.014

参考文献

原文顺序 | 文献年度倒序 | 文中引用次数倒序

[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.