Toward Fully Automated Personalized Orthopedic Treatments: Innovations and Interdisciplinary Gaps
Abstract
:1. Introduction
2. The Lifecycle of Standard Orthopedic Treatments
- I.
- Patient Evaluation and Data Collection
- II.
- Diagnosis and Treatment Planning
- III.
- Device Acquisition
- IV.
- Surgical Implantation
- V.
- Post-Operative Care and Rehabilitation
- VI.
- Long-Term Follow-Up
3. Innovative Technologies Driving Personalization of Orthopedic Treatments
3.1. Artificial Intelligence (AI)
3.2. Innovative Biomaterials
3.3. Genomic and Proteomic Analysis Techniques
3.4. Lab-on-a-Chip
3.5. Medical Imaging Technologies
3.6. Image-Based Biomechanical Finite Element Modeling
3.7. Advances in Biomimicry
3.8. Three-Dimensional Printing and Bioprinting
3.9. Wearable and Implantable Sensors
4. Interdisciplinary Gaps and Bridging Strategies
4.1. Non-Invasive Quantitative Characterization of Bone Quality and Strength
4.2. Patient-Specific Biocompatibility
4.3. Personalized Design of Orthopedic Devices with Optimal Balance between Biological and Mechanical Requirements
4.4. Seamless Integration of Multidisciplinary Technologies and Full Automation of Personalized Orthopedic Treatment
5. Conclusions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Conflicts of Interest
References
- GBD 2019 Fracture Collaborators. Global, regional, and national burden of bone fractures in 204 countries and territories, 1990–2019: A systematic analysis from the global burden of disease study 2019. Lancet Healthy Longev. 2021, 2, e580–e592. [Google Scholar] [CrossRef] [PubMed]
- Public Health Agency of Canada. Osteoporosis and Related Fractures in Canada: Report from the Canadian Chronic Disease Surveillance System; Technical Report; Public Health Agency of Canada = Agence de la santé publique du Canada: Ottawa, ON, Canada, 2020; Available online: https://www.canada.ca/en/public-health/services/publications/diseases-conditions/osteoporosis-related-fractures-2020.html (accessed on 8 June 2024).
- Oryan, A.; Alidadi, S.; Moshiri, A.; Maffulli, N. Bone regenerative medicine: Classic options, novel strategies, and future directions. J. Orthop. Surg. Res. 2014, 9, 18. [Google Scholar] [CrossRef] [PubMed]
- Marsh, A.C.; Chamorro, N.P.; Chatzistavrou, X. 15-Long-term performance and failure of orthopedic devices. In Woodhead Publishing Series in Biomaterials, Bone Repair Biomaterials, 2nd ed.; Pawelec, K.M., Planell, J.A., Eds.; Woodhead Publishing: Cambridge, UK, 2019; pp. 379–410. [Google Scholar]
- Oosthuizen, P.B.; Snyckers, C.H. Controversies around modern bearing surfaces in total joint replacement surgery. SA Orthop. J. 2013, 12, 44–50. [Google Scholar]
- Bohm, E.R.; Dunbar, M.J.; Frood, J.J.; Johnson, T.M.; Morris, K.A. Rehospitalizations, early revisions, infections, and hospital resource use in the first year after hip and knee arthroplasties. J. Arthroplast. 2012, 27, 232–237.e1. [Google Scholar] [CrossRef] [PubMed]
- Evans, J.T.; Evans, J.P.; Walker, R.W.; Blom, A.W.; Whitehouse, M.R.; Sayers, A. How long does a hip replacement last? a systematic review and meta-analysis of case series and national registry reports with more than 15 years of follow-up. Lancet 2019, 393, 647–654. [Google Scholar] [CrossRef] [PubMed]
- Nunley, R.M.; Nam, D.; Berend, K.R.; Lombardi, A.V.; Dennis, D.A.; Della Valle, C.J.; Barrack, R.L. New total knee arthroplasty designs: Do young patients notice? Clin. Orthop. Relat. Res. 2015, 473, 101–108. [Google Scholar] [CrossRef] [PubMed]
- Bayliss, L.E.; Culliford, D.; Monk, A.P.; Glyn-Jones, S.; Prieto-Alhambra, D.; Judge, A.; Cooper, C.; Carr, A.J.; Arden, N.K.; Beard, D.J.; et al. The effect of patient age at intervention on risk of implant revision after total replacement of the hip or knee: A population-based cohort study. Lancet 2017, 389, 1424–1430. [Google Scholar] [CrossRef] [PubMed]
- Singh, J.A.; Mehta, B.; Mirza, S.Z.; Figgie, M.P.; Sculco, P.; Parks, M.; Goodman, S.M. When has a knee or hip replacement failed? a patient perspective. J. Rheumatol. 2021, 48, 447–453. [Google Scholar] [CrossRef] [PubMed]
- Yong, K.S.; Kareem, B.A.; Ruslan, G.N.; Harwant, S. Risk factors for infection in total hip replacement surgery at hospital kuala lumpur. Med. J. Malays. 2001, 56 (Suppl. C), 57–60. [Google Scholar]
- Wier, J.; Liu, K.C.; Piple, A.S.; Christ, A.B.; Longjohn, D.B.; Oakes, D.A.; Heckmann, N.D. Factors associated with failure following proximal femoral replacement for salvage hip surgery for nononcologic indications. J. Arthroplast. 2023, 38, 2429–2435.e2. [Google Scholar] [CrossRef]
- Crawford, R.W.; Murrat, D.W. Total hip replacement: Indications for surgery and risk factors for failure. Ann. Rheum. Dis. 1997, 56, 455–457. [Google Scholar] [CrossRef] [PubMed]
- Harrysson, O.L.; Hosni, Y.A.; Nayfeh, J.F. Custom-designed orthopedic implants evaluated using finite element analysis of patient-specific computed tomography data: Femoral-component case study. BMC Musculoskelet. Disord. 2007, 8, 91. [Google Scholar] [CrossRef] [PubMed]
- Safali, S.; Berk, T.; Makelov, B.; Acar, M.A.; Gueorguiev, B.; Pape, H.C. The possibilities of personalized 3d printed implants—A case series study. Medicina 2023, 59, 249. [Google Scholar] [CrossRef]
- Wixted, C.M.; Peterson, J.R.; Kadakia, R.J.; Adams, S.B. Three-dimensional printing in orthopaedic surgery: Current applications and future developments. J. Am. Acad. Orthop. Surg. Glob. Res. Rev. 2021, 5, e20.00230-11. [Google Scholar] [CrossRef] [PubMed]
- Kulkarni, P.G.; Paudel, N.; Magar, S.; Santilli, M.F.; Kashyap, S.; Baranwal, A.K.; Zamboni, P.; Vasavada, P.; Katiyar, A.; Singh, A.V. Overcoming challenges and innovations in orthopedic prosthesis design: An interdisciplinary perspective. Biomed. Mater. Devices 2024, 2, 58–69. [Google Scholar] [CrossRef] [PubMed]
- Davenport, T.; Kalakota, R. The potential for artificial intelligence in healthcare. Future Heal. J. 2019, 6, 94–98. [Google Scholar] [CrossRef] [PubMed]
- Mackay, B.S.; Marshall, K.; Grant-Jacob, J.A.; Kanczler, J.; Eason, R.W.; Oreffo, R.O.C.; Mills, B. The future of bone regeneration: Integrating AI into tissue engineering. Biomed. Phys. Eng. Express 2021, 7, 052002. [Google Scholar] [CrossRef] [PubMed]
- Takabatake, T.; Fujiwara, K.; Okamoto, S.; Kishimoto, R.; Kagawa, N.; Toyota, M. Discovery of orthogonal synthesis using artificial intelligence: Pd(OAc)2-catalyzed one-pot synthesis of benzofuran and bicyclo [3.3.1] nonane scaffolds. Tetrahedron Lett. 2020, 61, 152275. [Google Scholar] [CrossRef]
- McDonald, S.M.; Augustine, E.K.; Lanners, Q.; Rudin, C.; Brinson, L.C.; Becker, M.L. Applied machine learning as a driver for polymeric biomaterials design. Nat. Commun. 2023, 14, 4838. [Google Scholar] [CrossRef]
- Sultan, H.; Owais, M.; Choi, J.; Mahmood, T.; Haider, A.; Ullah, N.; Park, K.R. Artificial intelligence-based solution in personalized computer-aided arthroscopy of shoulder prostheses. J. Pers. Med. 2022, 12, 109. [Google Scholar] [CrossRef]
- Conev, A.; Litsa, E.E.; Perez, M.R.; Diba, M.; Mikos, A.G.; Kavraki, L.E. Machine learning-guided three-dimensional printing of tissue engineering scaffolds. Tissue Eng. Part A 2020, 26, 1359–1368. [Google Scholar] [CrossRef] [PubMed]
- Al-Kharusi, G.; Dunne, N.J.; Little, S.; Levingstone, T.J. The role of machine learning and design of experiments in the advancement of biomaterial and tissue engineering research. Bioengineering 2022, 9, 561. [Google Scholar] [CrossRef] [PubMed]
- Barrera, M.D.B.; Franco-Martinez, F.; Lantada, A.D. Artificial intelligence aided design of tissue engineering scaffolds employing virtual tomography and 3D convolutional neural networks. Materials 2021, 14, 5278. [Google Scholar] [CrossRef]
- Cilla, M.; Borgiani, E.; Martinez, J.; Duda, G.N.; Checa, S.; Tsuchiya, H. Machine learning techniques for the optimization of joint replacements: Application to a short-stem hip implant. PLoS ONE 2017, 12, e0183755. [Google Scholar] [CrossRef] [PubMed]
- Zhang, K.; Wang, S.; Zhou, C.; Cheng, L.; Gao, X.; Xie, X.; Sun, J.; Wang, H.; Weir, M.D.; Reynolds, M.A.; et al. Advanced smart biomaterials and constructs for hard tissue engineering and regeneration. Bone Res. 2018, 6, 31. [Google Scholar] [CrossRef] [PubMed]
- Cao, D.; Ding, J. Recent advances in regenerative biomaterials. Regen. Biomater. 2022, 9, rbac098. [Google Scholar] [CrossRef] [PubMed]
- Magazzini, L.; Grilli, S.; Fenni, S.E.; Donetti, A.; Cavallo, D.; Monticelli, O. The blending of poly(glycolic acid) with polycaprolactone and poly(l-lactide): Promising combinations. Polymers 2021, 13, 2780. [Google Scholar] [CrossRef] [PubMed]
- Castaneda-Rodriguez, S.; Gonzalez-Torres, M.; Ribas-Aparicio, R.M.; Del Prado-Audelo, M.L.; Leyva-Gomez, G.; Gurer, E.S.; Sharifi-Rad, J. Recent advances in modified poly (lactic acid) as tissue engineering materials. J. Biol. Eng. 2023, 17, 21. [Google Scholar] [CrossRef] [PubMed]
- Shi, H.; Zhou, P.; Li, J.; Liu, C.; Wang, L. Functional gradient metallic biomaterials: Techniques, current scenery, and future prospects in the biomedical field. Front. Bioeng. Biotechnol. 2021, 8, 616845. [Google Scholar] [CrossRef]
- Sola, A.; Bellucci, D.; Cannillo, V. Functionally graded materials for orthopedic applications—An update on design and manufacturing. Biotechnol. Adv. 2016, 34, 504–531. [Google Scholar] [CrossRef]
- Mehrali, M.; Shirazi, F.S.; Mehrali, M.; Metselaar, H.S.C.; Kadri, N.A.B.; Osman, N.A.A. Dental implants from functionally graded materials: Dental implants from FGM. J. Biomed. Mater. Res. Part A 2013, 101, 3046–3057. [Google Scholar] [CrossRef] [PubMed]
- Gao, Y.; Seles, M.A.; Rajan, M. Role of bioglass derivatives in tissue regeneration and repair: A review. Rev. Adv. Mater. Sci. 2023, 62, S53–S82. [Google Scholar] [CrossRef]
- Rahaman, M.N.; Day, D.E.; Bal, B.S.; Fu, Q.; Jung, S.B.; Bonewald, L.F.; Tomsia, A.P. Bioactive glass in tissue engineering. Acta Biomater. 2011, 7, 2355–2373. [Google Scholar] [CrossRef] [PubMed]
- Catoira, M.C.; Fusaro, L.; Di Francesco, D.; Ramella, M.; Boccafoschi, F. Overview of natural hydrogels for regenerative medicine applications. J. Mater. Sci. Mater. Med. 2019, 30, 115. [Google Scholar] [CrossRef] [PubMed]
- Madduma-Bandarage, U.S.K.; Madihally, S.V. Synthetic hydrogels: Synthesis, novel trends, and applications. J. Appl. Polym. Sci. 2021, 138, 50376. [Google Scholar] [CrossRef]
- Mantha, S.; Pillai, S.; Khayambashi, P.; Upadhyay, A.; Zhang, Y.; Tao, O.; Pham, H.M.; Tran, S.D. Smart hydrogels in tissue engineering and regenerative medicine. Materials 2019, 12, 3323. [Google Scholar] [CrossRef] [PubMed]
- Liu, X.; Wu, S.; Yeung, K.W.K.; Chan, Y.L.; Hu, T.; Xu, Z.N.; Liu, X.; Chung, J.C.Y.; Cheung, K.M.C.; Chu, P.K. Relationship between osseointegration and superelastic biomechanics in porous NiTi scaffolds. Biomaterials 2011, 32, 330–338. [Google Scholar] [CrossRef]
- Yuan, B.; Zhu, M.; Chung, C.Y. Biomedical porous shape memory alloys for hard-tissue replacement materials. Materials 2018, 11, 1716. [Google Scholar] [CrossRef]
- Mitchell, M.J.; Billingsley, M.M.; Haley, R.M.; Wechsler, M.E.; Peppas, N.A.; Langer, R. Engineering precision nanoparticles for drug delivery. Nat. Rev. Drug Discov. 2021, 20, 101–124. [Google Scholar] [CrossRef]
- Hasan, A.; Morshed, M.; Memic, A.; Hassan, S.; Webster, T.J.; Marei, H.E. Nanoparticles in tissue engineering: Applications, challenges and prospects. Int. J. Nanomed. 2018, 13, 5637–5655. [Google Scholar] [CrossRef]
- Fadilah, N.I.M.; Isa, I.L.M.; Zaman, W.S.W.K.; Tabata, Y.; Fauzi, M.B. The effect of nanoparticle-incorporated natural-based biomaterials towards cells on activated pathways: A systematic review. Polymers 2022, 14, 476. [Google Scholar] [CrossRef] [PubMed]
- Xue, T.; Attarilar, S.; Liu, S.; Liu, J.; Song, X.; Li, L.; Zhao, B.; Tang, Y. Surface modification techniques of titanium and its alloys to functionally optimize their biomedical properties: Thematic review. Front. Bioeng. Biotechnol. 2020, 8, 603072. [Google Scholar] [CrossRef] [PubMed]
- Kligman, S.; Ren, Z.; Chung, C.H.; Perillo, M.A.; Chang, Y.C.; Koo, H.; Zheng, Z.; Li, C. The impact of dental implant surface modifications on osseointegration and biofilm formation. J. Clin. Med. 2021, 10, 1641. [Google Scholar] [CrossRef] [PubMed]
- Accioni, F.; Vazquez, J.; Merinero, M.; Begines, B.; Alcudia, A. Latest trends in surface modification for dental implantology: Innovative developments and analytical applications. Pharmaceutics 2022, 14, 455. [Google Scholar] [CrossRef] [PubMed]
- de Paula, A.G.P.; de Lima, J.D.; Bastos, T.S.B.; Czaikovski, A.P.; dos Santos Luz, R.B.; Yuasa, B.S.; Smanioto, C.C.S.; Robert, A.W.; Braga, T.T. Decellularized extracellular matrix: The role of this complex biomaterial in regeneration. ACS Omega 2023, 8, 22256–22267. [Google Scholar] [CrossRef] [PubMed]
- Zhang, X.; Chen, X.; Hong, H.; Hu, R.; Liu, J.; Liu, C. Decellularized extracellular matrix scaffolds: Recent trends and emerging strategies in tissue engineering. Bioact. Mater. 2021, 10, 15–31. [Google Scholar] [CrossRef]
- Xiao, H.; Chen, X.; Liu, X.; Wen, G.; Yu, Y. Recent advances in decellularized biomaterials for wound healing. Mater. Today Bio 2023, 19, 100589. [Google Scholar] [CrossRef]
- Bril, M.; Fredrich, S.; Kurniawan, N.A. Stimuli-responsive materials: A smart way to study dynamic cell responses. Smart Mater. Med. 2022, 3, 257–273. [Google Scholar] [CrossRef]
- Wells, C.M.; Harris, M.; Choi, L.; Murali, V.P.; Guerra, F.D.; Jennings, J.A. Stimuli-responsive drug release from smart polymers. J. Funct. Biomater. 2019, 10, 34. [Google Scholar] [CrossRef]
- He, Y.; Lu, F. Development of synthetic and natural materials for tissue engineering applications using adipose stem cells. Stem Cells Int. 2016, 2016, 5786257. [Google Scholar] [CrossRef]
- Reddy, M.S.B.; Ponnamma, D.; Choudhary, R.; Sadasivuni, K.K. A comparative review of natural and synthetic biopolymer composite scaffolds. Polymers 2021, 13, 1105. [Google Scholar] [CrossRef] [PubMed]
- Ye, B.; Wu, B.; Su, Y.; Sun, T.; Guo, X. Recent advances in the application of natural and synthetic polymer-based scaffolds in musculoskeletal regeneration. Polymers 2022, 14, 4566. [Google Scholar] [CrossRef] [PubMed]
- McGuire, A.L.; Gabriel, S.; Tishkoff, S.A.; Wonkam, A.; Chakravarti, A.; Furlong, E.E.M.; Treutlein, B.; Meissner, A.; Chang, H.Y.; López-Bigas, N.; et al. The road ahead in genetics and genomics. Nat. Rev. Genet. 2020, 21, 581–596. [Google Scholar] [CrossRef]
- Joyce, K.; Fabra, G.T.; Bozkurt, Y.; Pandit, A. Bioactive potential of natural biomaterials: Identification, retention and assessment of biological properties. Sig. Transduct. Target. Ther. 2021, 6, 122. [Google Scholar] [CrossRef] [PubMed]
- Aamodt, J.M.; Grainger, D.W. Extracellular matrix-based biomaterial scaffolds and the host response. Biomaterials 2016, 86, 68–82. [Google Scholar] [CrossRef] [PubMed]
- Othman, Z.; Pastor, B.C.; van Rijt, S.; Habibovic, P. Understanding interactions between biomaterials and biological systems using proteomics. Biomaterials 2018, 167, 191–204. [Google Scholar] [CrossRef] [PubMed]
- Jiang, S.; Wang, M.; He, J. A review of biomimetic scaffolds for bone regeneration: Toward a cell-free strategy. Bioeng. Transl. Med. 2020, 6, e10206. [Google Scholar] [CrossRef] [PubMed]
- Li, H.; Yang, Y.; Hong, W.; Huang, M.; Wu, M.; Zhao, X. Applications of genome editing technology in the targeted therapy of human diseases: Mechanisms, advances and prospects. Sig. Transduct. Target. Ther. 2020, 5, 1. [Google Scholar] [CrossRef] [PubMed]
- Dupree, E.J.; Jayathirtha, M.; Yorkey, H.; Mihasan, M.; Petre, B.A.; Darie, C.C. A critical review of bottom-up proteomics: The good, the bad, and the future of this field. Proteomes 2020, 8, 14. [Google Scholar] [CrossRef]
- Hassan, M.; Awan, F.M.; Naz, A.; Galiana, E.J.D.; Alvarez, O.; Cernea, A.; Fernandez-Brillet, L.; Fernandez-Martinez, J.L.; Kloczkowski, A. Innovations in genomics and big data analytics for personalized medicine and health care: A review. Int. J. Mol. Sci. 2022, 23, 4645. [Google Scholar] [CrossRef]
- Martel-Pelletier, J.; Barr, A.; Cicuttini, F.; Conaghan, P.G.; Cooper, C.; Goldring, M.B.; Goldring, S.R.; Jones, G.; Teichtahl, A.J.; Pelletier, J.P. Osteoarthritis. Nat. Rev. Dis. Primers 2016, 2, 16072. [Google Scholar] [CrossRef] [PubMed]
- Attur, M.; Krasnokutsky-Samuels, S.; Samuels, J.; Abramson, S.B. Prognostic biomarkers in osteoarthritis. Curr. Opin. Rheumatol. 2013, 25, 136–144. [Google Scholar] [CrossRef] [PubMed]
- VanEpps, J.S.; Younger, J.G. Implantable device-related infection. Shock 2016, 46, 597–608. [Google Scholar] [CrossRef] [PubMed]
- Garcia-Cordero, J.L.; Ricco, A.J. Lab-on-a-chip (general philosophy). In Encyclopedia of Microfluidics and Nanofluidics; Li, D., Ed.; Springer: Boston, MA, USA, 2008. [Google Scholar]
- Carvalho, V.; Teixeira, S.D.F.C.F.; Ribeiro, J. Micro/Nanofluidic and Lab-on-a-Chip Devices for Biomedical Applications; MDPI—Multidisciplinary Digital Publishing Institute: Basel, Switzerland, 2022. [Google Scholar]
- Sharma, B.; Sharma, A. Microfluidics: Recent advances toward lab-on-chip applications in bioanalysis. Adv. Eng. Mater. 2022, 24, 2100738. [Google Scholar] [CrossRef]
- Wu, Q.; Liu, J.; Wang, X.; Feng, L.; Wu, J.; Zhu, X.; Wen, W.; Gong, X. Organ-on-a-chip: Recent breakthroughs and future prospects. Biomed. Eng. Online 2020, 19, 9. [Google Scholar] [CrossRef] [PubMed]
- Lee, N.Y. Recent progress in lab-on-a-chip technology and its potential application to clinical diagnoses. Int. Neurourol. J. 2013, 17, 2–10. [Google Scholar] [CrossRef] [PubMed]
- Dervisevic, E.; Tuck, K.L.; Voelcker, N.H.; Cadarso, V.J. Recent progress in lab-on-a-chip systems for the monitoring of metabolites for mammalian and microbial cell research. Sensors 2019, 19, 5027. [Google Scholar] [CrossRef] [PubMed]
- Zou, Z.; Luo, X.; Chen, Z.; Zhang, Y.S.; Wen, C. Emerging microfluidics-enabled platforms for osteoarthritis management: From benchtop to bedside. Theranostics 2022, 12, 891–909. [Google Scholar] [CrossRef] [PubMed]
- Zhang, D.; Bi, H.; Liu, B.; Qiao, L. Detection of pathogenic microorganisms by microfluidics based analytical methods. Anal. Chem. 2018, 90, 5512–5520. [Google Scholar] [CrossRef]
- Foglieni, B.; Brisci, A.; Biagio, F.S.; Di Pietro, P.; Petralia, S.; Conoci, S.; Ferrari, M.; Cremonesi, L. Integrated PCR amplification and detection processes on a lab-on-chip platform: A new advanced solution for molecular diagnostics. Clin. Chem. Lab. Med. 2010, 48, 329–336. [Google Scholar] [CrossRef]
- Paek, K.; Kim, S.; Tak, S.; Kim, M.K.; Park, J.; Chung, S.; Park, T.H.; Kim, J.A. A high-throughput biomimetic bone-on-a-chip platform with artificial intelligence-assisted image analysis for osteoporosis drug testing. Bioeng. Transl. Med. 2022, 8, e10313. [Google Scholar] [CrossRef] [PubMed]
- Qiu, S.; Cai, Y.; Yao, H.; Lin, C.; Xie, Y.; Tang, S.; Zhang, A. Small molecule metabolites: Discovery of biomarkers and therapeutic targets. Sig. Transduct. Target. Ther. 2023, 8, 132. [Google Scholar]
- Mansoorifar, A.; Gordon, R.; Bergan, R.; Bertassoni, L.E. Bone-on-a-chip: Microfluidic technologies and microphysiologic models of bone tissue. Adv. Funct. Mater. 2021, 31, 2006796. [Google Scholar] [CrossRef] [PubMed]
- Ma, C.; Peng, Y.; Li, H.; Chen, W. Organ-on-a-chip: A new paradigm for drug development. Trends Pharmacol. Sci. 2021, 42, 119–133. [Google Scholar] [CrossRef] [PubMed]
- Morales, I.A.; Boghdady, C.-M.; Campbell, B.E.; Moraes, C. Integrating mechanical sensor readouts into organ-on-a-chip platforms. Front. Bioeng. Biotechnol. 2022, 10, 1060895. [Google Scholar] [CrossRef] [PubMed]
- Nasseri, B.; Soleimani, N.; Rabiee, N.; Kalbasi, A.; Karimi, M.; Hamblin, M.R. Point-of-care microfluidic devices for pathogen detection. Biosens. Bioelectron. 2018, 117, 112–128. [Google Scholar] [CrossRef] [PubMed]
- Koyakutty, M.; Meethaleveedu, S.K.; Ashokan, A.; Somasundaram, V.H.; Nair, S. Patent Issued for MRI and CT Contrast-Enabled Composite Implants for Image-Guided Tissue Regeneration and Therapy. U.S. Patent 10,806,805, 20 October 2020. [Google Scholar]
- Nguyen, T.T.; Thelen, J.C.; Bhatt, A.A. Bone up on spinal osseous lesions: A case review series. Insights Imaging 2020, 11, 80. [Google Scholar] [CrossRef] [PubMed]
- Hussain, S.; Mubeen, I.; Ullah, N.; Shah, S.S.D.; Khan, B.A.; Zahoor, M.; Ullah, R.; Khan, F.A.; Sultan, M.A. Modern diagnostic imaging technique applications and risk factors in the medical field: A review. BioMed Res. Int. 2022, 2022, 5164970. [Google Scholar] [CrossRef]
- Frisardi, G.; Barone, S.; Razionale, A.V.; Paoli, A.; Frisardi, F.; Tullio, A.; Lumbau, A.; Chessa, G. Biomechanics of the press-fit phenomenon in dental implantology: An image-based finite element analysis. Head Face Med. 2012, 8, 18. [Google Scholar] [CrossRef]
- Boccaccio, A.; Ballini, A.; Pappalettere, C.; Tullo, D.; Cantore, S.; Desiate, A. Finite element method (FEM), mechanobiology and biomimetic scaffolds in bone tissue engineering. Int. J. Biol. Sci. 2011, 7, 112–132. [Google Scholar] [CrossRef]
- Haleem, A.; Javaid, M. Role of CT and MRI in the design and development of orthopaedic model using additive manufacturing. J. Clin. Orthop. Trauma. 2018, 9, 213–217. [Google Scholar] [CrossRef] [PubMed]
- Monllau, J.C.; Poggioli, F.; Erquicia, J.; RamÃ, E.; Pelfort, X.; Gelber, P.; Torres-Claramunt, R. Magnetic resonance imaging and functional outcomes after a polyurethane meniscal scaffold implantation: Minimum 5-year follow-up. Arthroscopy 2018, 34, 1621–1627. [Google Scholar] [CrossRef] [PubMed]
- Sofka, C.M.; Potter, H.G.; Adler, R.S.; Pavlov, H. Musculoskeletal imaging update: Current applications of advanced imaging techniques to evaluate the early and long-term complications of patients with orthopedic implants. HSS J. 2006, 2, 73–77. [Google Scholar] [CrossRef] [PubMed]
- Engelbrecht, L.; Ollewagen, T.; de Swardt, D. Advances in fluorescence microscopy can reveal important new aspects of tissue regeneration. Biochimie 2022, 196, 194–202. [Google Scholar] [CrossRef] [PubMed]
- Hyun, H.; Cho, C.S. Updates in molecular imaging techniques. Tissue Eng. Regen. Med. 2019, 16, 431–432. [Google Scholar] [CrossRef] [PubMed]
- Willadsen, M.; Chaise, M.; Yarovoy, I.; Zhang, A.Q.; Parashurama, N. Engineering molecular imaging strategies for regenerative medicine. Bioeng. Transl. Med. 2018, 3, 232–255. [Google Scholar] [CrossRef] [PubMed]
- Patel, R.B.; Solorio, L.; Wu, H.; Krupka, T.; Exner, A.A. Effect of injection site on in situ implant formation and drug release in vivo. J. Control. Release 2010, 147, 350–358. [Google Scholar] [CrossRef] [PubMed]
- Zhou, H.; Hernandez, C.; Goss, M.; Gawlik, A.; Exner, A.A. Biomedical imaging in implantable drug delivery systems. Curr. Drug Targets 2015, 16, 672–682. [Google Scholar] [CrossRef] [PubMed]
- Zhu, W.; Chu, C.; Kuddannaya, S.; Yuan, Y.; Walczak, P.; Singh, A.; Song, X.; Bulte, J.W.M. In vivo imaging of composite hydrogel scaffold degradation using CEST MRI and two-color NIR imaging. Adv. Funct. Mater. 2019, 29, 1903753. [Google Scholar] [CrossRef]
- Talacua, H.; Söntjens, S.H.M.; Thakkar, S.H.; Brizard, A.M.A.; van Herwerden, L.A.; Vink, A.; van Almen, G.C.; Dankers, P.Y.W.; Bouten, C.V.C.; Budde, R.P.J.; et al. Imaging the in vivo degradation of tissue engineering implants by use of supramolecular radiopaque biomaterials. Macromol. Biosci. 2020, 20, 2000024. [Google Scholar] [CrossRef]
- Kim, K.; Jeong, C.G.; Hollister, S.J. Non-invasive monitoring of tissue scaffold degradation using ultrasound elasticity imaging. Acta Biomater. 2008, 4, 783–790. [Google Scholar] [CrossRef]
- Koff, M.F.; Esposito, C.; Shah, P.; Miranda, M.; Baral, E.; Fields, K.; Bauer, T.; Padgett, D.E.; Wright, T.; Potter, H.G. MRI of THA correlates with implant wear and tissue reactions: A cross-sectional study. Clin. Orthop. Relat. Res. 2019, 477, 159–174. [Google Scholar] [CrossRef]
- Mushtaq, N.; To, K.; Gooding, C.; Khan, W. Radiological imaging evaluation of the failing total hip replacement. Front. Surg. 2019, 6, 35. [Google Scholar] [CrossRef]
- Durastanti, G.; Belvedere, C.; Ruggeri, M.; Donati, D.M.; Spazzoli, B.; Leardini, A. A pelvic reconstruction procedure for custom-made prosthesis design of bone tumor surgical treatments. Appl. Sci. 2022, 12, 1654. [Google Scholar] [CrossRef]
- Jahangir, S.; Mohammadi, A.; Mononen, M.E.; Hirvasniemi, J.; Suomalainen, J.S.; Saarakkala, S.; Korhonen, R.K.; Tanska, P. Rapid x-ray-based 3-d finite element modeling of medial knee joint cartilage biomechanics during walking. Ann. Biomed. Eng. 2022, 50, 666–679. [Google Scholar] [CrossRef]
- Klodowski, K.; Kaminski, J.; Nowicka, K.; Tarasiuk, J.; Wronski, S.; Swietek, M.; Biazewicz, M.; Figiel, H.; Turek, K.; Szponder, T. Micro-imaging of implanted scaffolds using combined MRI and micro-CT. Comput. Med. Imaging Graph. 2014, 38, 458–468. [Google Scholar] [CrossRef] [PubMed]
- Bahraminasab, M. Challenges on optimization of 3d-printed bone scaffolds. BioMed. Eng. OnLine 2020, 19, 69. [Google Scholar] [CrossRef] [PubMed]
- Wu, J.; Zhang, Y.; Lyu, Y.; Cheng, L. On the various numerical techniques for the optimization of bone scaffold. Materials 2023, 16, 974. [Google Scholar] [CrossRef] [PubMed]
- Rho, J.Y.; Hobatho, M.C.; Ashman, R.B. Relations of mechanical properties to density and ct numbers in human bone. Med. Eng. Phys. 1995, 17, 347–355. [Google Scholar] [CrossRef]
- Snyder, S.M.; Schneider, E. Estimation of mechanical properties of cortical bone by computed tomography. J. Orthop. Res. 1991, 9, 422–431. [Google Scholar] [CrossRef]
- Gerasimov, O.V.; Kharin, N.V.; Fedyanin, A.O.; Bolshakov, P.V.; Baltin, M.E.; Statsenko, E.O.; Fadeev, F.O.; Islamov, R.R.; Baltina, T.V.; Sachenkov, O.A. Bone stress-strain state evaluation using CT based FEM. Front. Mech. Eng. 2021, 7, 688474. [Google Scholar] [CrossRef]
- Askari, E.; Cengiz, I.F.; Alves, J.L.; Henriques, B.; Flores, P.; Fredel, M.C.; Reis, R.L.; Oliveira, J.M.; Silva, F.S.; Mesquita-Guimaraes, J. Micro-CT based finite element modelling and experimental characterization of the compressive mechanical properties of 3-d zirconia scaffolds for bone tissue engineering. J. Mech. Behav. Biomed. Mater. 2020, 102, 103516. [Google Scholar] [CrossRef] [PubMed]
- Luo, Y. Image-Based Multilevel Biomechanical Modeling for Fall-Induced Hip Fracture; Springer Nature: Dordrecht, The Netherlands, 2017. [Google Scholar]
- Faisal, T.R.; Luo, Y. Study of stress variations in single-stance and sideways fall using image-based finite element analysis. Bio-Med. Mater. Eng. 2016, 27, 1–17. [Google Scholar] [CrossRef]
- Faisal, T.R.; Luo, Y. Study of fracture risk difference in left and right femur by QCT-based FEA. Biomed. Eng. Online 2017, 16, 116. [Google Scholar] [CrossRef] [PubMed]
- Kheirollahi, H.; Luo, Y. Assessment of hip fracture risk using cross-section strain energy determined from QCT-based finite element model. BioMed Res. Int. 2015, 2015, 413839. [Google Scholar] [CrossRef]
- Kheirollahi, H.; Luo, Y. Understanding Hip Fracture by QCT-Based Finite Element Modeling. J. Med. Biol. Eng. 2017, 37, 686–694. [Google Scholar] [CrossRef]
- Reddy, M.S.; Sundram, R.; Abdemagyd, H.A.E. Application of finite element model in implant dentistry: A systematic review. J. Pharm. Bioallied Sci. 2019, 11, S85–S91. [Google Scholar] [CrossRef] [PubMed]
- Moghadasi, K.; Isa, M.S.M.; Ariffin, M.A.; Mohd jamil, M.Z.; Raja, S.; Wu, B.; Yamani, M.; Muhamad, M.R.B.; Yusof, F.; Jamaludin, M.F.; et al. A review on biomedical implant materials and the effect of friction stir based techniques on their mechanical and tribological properties. J. Mater. Res. Technol. 2022, 17, 1054–1121. [Google Scholar] [CrossRef]
- Kladovasilakis, N.; Tsongas, K.; Tzetzis, D. Finite element analysis of orthopedic hip implant with functionally graded bioinspired lattice structures. Biomimetics 2020, 5, 44. [Google Scholar] [CrossRef]
- Oshkour, A.; Osman, N.A.; Yau, Y.; Tarlochan, F.; Abas, W.W. Design of new generation femoral prostheses using functionally graded materials: A finite element analysis. Proc. Inst. Mech. Eng. Part H J. Eng. Med. 2013, 227, 3–17. [Google Scholar] [CrossRef]
- Lin, D.; Li, Q.; Li, W.; Zhou, S.; Swain, M.V. Design optimization of functionally graded dental implant for bone remodeling. Compos. Part B Eng. 2009, 40, 668–675. [Google Scholar] [CrossRef]
- Pandithevan, P.; Kumar, G.S. Finite element analysis of a personalized femoral scaffold with designed microarchitecture. Proc. Inst. Mech. Eng. Part H J. Eng. Med. 2010, 224, 877–889. [Google Scholar] [CrossRef] [PubMed]
- Miranda, P.; Pajares, A.; Guiberteau, F. Finite element modeling as a tool for predicting the fracture behavior of robocast scaffolds. Acta Biomater. 2008, 4, 1715–1724. [Google Scholar] [CrossRef]
- Cahill, S.; Lohfeld, S.; McHugh, P.E. Finite element predictions compared to experimental results for the effective modulus of bone tissue engineering scaffolds fabricated by selective laser sintering. J. Mater. Sci. Mater. Med. 2009, 20, 1255–1262. [Google Scholar] [CrossRef] [PubMed]
- Page, M.I.; Linde, P.E.; Puttlitz, C.M. High throughput computational evaluation of how scaffold architecture, material selection, and loading modality influence the cellular micromechanical environment in tissue engineering strategies. JOR Spine 2021, 4, e1152. [Google Scholar] [CrossRef]
- Alaneme, K.K.; Kareem, S.A.; Ozah, B.N.; Alshahrani, H.A.; Ajibuwa, O.A. Application of finite element analysis for optimizing selection and design of Ti-based biometallic alloys for fractures and tissues rehabilitation: A review. J. Mater. Res. Technol. 2022, 19, 121–139. [Google Scholar] [CrossRef]
- Feher, B.; Lettner, S.; Heinze, G.; Karg, F.; Ulm, C.; Gruber, R.; Kuchler, U. An advanced prediction model for postoperative complications and early implant failure. Clin. Oral Implant. Res. 2020, 31, 928–935. [Google Scholar] [CrossRef]
- Lei, T.; Zhang, T.; Ju, W.; Chen, X.; Heng, B.C.; Shen, W.; Yin, Z. Biomimetic strategies for tendon/ligament-to-bone interface regeneration. Bioact. Mater. 2021, 6, 2491–2510. [Google Scholar] [CrossRef]
- Sankar, S.; Sharma, C.S.; Rath, S.N.; Ramakrishna, S. Electrospun nanofibres to mimic natural hierarchical structure of tissues: Application in musculoskeletal regeneration. J. Tissue Eng. Regen. Med. 2018, 12, e604–e619. [Google Scholar] [CrossRef]
- Al-Rooqi, M.M.; Hassan, M.M.; Moussa, Z.; Obaid, R.J.; Suman, N.H.; Wagner, M.H.; Natto, S.S.A.; Ahmed, S.A. Advancement of chitin and chitosan as promising biomaterials. J. Saudi Chem. Soc. 2022, 26, 101561. [Google Scholar] [CrossRef]
- Espinales, C.; Romero-Pena, M.; Calderon, G.; Vergara, K.; Caceres, P.J.; Castillo, P. Collagen, protein hydrolysates and chitin from by-products of fish and shellfish: An overview. Heliyon 2023, 9, e14937. [Google Scholar] [CrossRef]
- Zheng, D.; Liwinski, T.; Elinav, E. Interaction between microbiota and immunity in health and disease. Cell Res. 2020, 30, 492–506. [Google Scholar] [CrossRef] [PubMed]
- Belkaid, Y.; Hand, T.W. Role of the microbiota in immunity and inflammation. Cell 2014, 157, 121–141. [Google Scholar] [CrossRef] [PubMed]
- Arango-Santander, S. Bioinspired topographic surface modification of biomaterials. Mater. 2022, 15, 2383. [Google Scholar] [CrossRef] [PubMed]
- Cremaldi, J.C.; Bhushan, B. Bioinspired self-healing materials: Lessons from nature. Beilstein J. Nanotechnol. 2018, 9, 907–935. [Google Scholar] [CrossRef] [PubMed]
- Peng, Y.; Gu, S.; Wu, Q.; Xie, Z.; Wu, J. High-performance self-healing polymers. Acc. Mater. Res. 2023, 4, 323–333. [Google Scholar] [CrossRef]
- Reyssat, E.; Mahadevan, L. Hygromorphs: From pine cones to biomimetic bilayers. J. R. Soc. Interface 2009, 6, 951–957. [Google Scholar] [CrossRef] [PubMed]
- Vasilevich, A.; Carlier, A.; Winkler, D.A.; Singh, S.; de Boer, J. Evolutionary design of optimal surface topographies for biomaterials. Sci. Rep. 2020, 10, 22160. [Google Scholar] [CrossRef] [PubMed]
- Wang, Y.; Jiang, X.; Li, X.; Ding, K.; Liu, X.; Huang, B.; Ding, J.; Qu, K.; Sun, W.; Xue, Z.; et al. Bionic ordered structured hydrogels: Structure types, design strategies, optimization mechanism of mechanical properties and applications. Mater. Horiz. 2023, 10, 4033–4058. [Google Scholar] [CrossRef]
- Ensikat, H.J.; Ditsche-Kuru, P.; Neinhuis, C.; Barthlott, W. Superhydrophobicity in perfection: The outstanding properties of the lotus leaf. Beilstein J. Nanotechnol. 2011, 2, 152–161. [Google Scholar] [CrossRef]
- Rajaramon, S.; David, H.; Sajeevan, A.; Shanmugam, K.; Sriramulu, H.; Dandela, R.; Solomon, A.P. Multi-functional approach in the design of smart surfaces to mitigate bacterial infections: A review. Front. Cell. Infect. Microbiol. 2023, 13, 1139026. [Google Scholar] [CrossRef] [PubMed]
- Chia, H.N.; Wu, B.M. Recent advances in 3d printing of biomaterials. J. Biol. Eng. 2015, 9, 2015. [Google Scholar] [CrossRef] [PubMed]
- Li, Z.; Wang, Q.; Liu, G. A review of 3d printed bone implants. Micromachines 2022, 13, 528. [Google Scholar] [CrossRef] [PubMed]
- Mirkhalaf, M.; Men, Y.; Wang, R.; No, Y.; Zreiqat, H. Personalized 3D printed bone scaffolds: A review. Acta Biomater. 2023, 156, 110–124. [Google Scholar] [CrossRef] [PubMed]
- Bose, S.; Vahabzadeh, S.; Bandyopadhyay, A. Bone tissue engineering using 3D printing. Mater. Today 2013, 16, 496–504. [Google Scholar] [CrossRef]
- Kilian, D.; Sembdner, P.; Bretschneider, H.; Ahlfeld, T.; Mika, L.; Lützner, J.; Holtzhausen, S.; Lode, A.; Stelzer, R.; Gelinsky, M. 3D printing of patient-specific implants for osteochondral defects: Workflow for an MRI-guided zonal design. Bio-Des. Manuf. 2021, 4, 818–832. [Google Scholar] [CrossRef]
- Wu, Y.; Liu, J.; Kang, L.; Tian, J.; Zhang, X.; Hu, J.; Huang, Y.; Liu, F.; Wang, H.; Wu, Z. An overview of 3d printed metal implants in orthopedic applications: Present and future perspectives. Heliyon 2023, 9, e17718. [Google Scholar] [CrossRef] [PubMed]
- Javaid, M.; Haleem, A.; Singh, R.P.; Suman, R. 3D printing applications for healthcare research and development. Glob. Health J. 2022, 6, 217–226. [Google Scholar] [CrossRef]
- An, J.; Mei, J.E.; Suntornnond, R.; Chua, C.K. Design and 3D printing of scaffolds and tissues. Engineering 2015, 1, 261–268. [Google Scholar] [CrossRef]
- Apaza, K.; Risco, R.; Ponce, S.; Bravo, D.; Aza, P.; Giraldo, B.; Rubio, L. Development and characterization of 3D printed PLA/hydroxyapatite composite scaffolds for bone tissue engineering. Heliyon 2020, 6, e03591. [Google Scholar]
- Buj-Corral, I.; Bagheri, A.; Petit-Rojo, O. 3D printing of porous scaffolds with controlled porosity and pore size values. Materials 2018, 11, 1532. [Google Scholar] [CrossRef]
- Deng, F.; Liu, L.; Li, Z.; Liu, J. 3D printed Ti6Al4V bone scaffolds with different pore structure effects on bone ingrowth. J. Biol. Eng. 2021, 15, 4. [Google Scholar] [CrossRef]
- Yang, J.; Li, Y.; Shi, X.; Shen, M.; Shi, K.; Shen, L.; Yang, C. Design and analysis of three-dimensional printing of a porous titanium scaffold. BMC Musculoskelet. Disord. 2021, 22, 654. [Google Scholar] [CrossRef] [PubMed]
- Gregor, A.; Filová, E.; Novák, M.; Kronek, J.; Chlup, H.; Buzgo, M.; Blahnová, V.; Lukášová, V.; Bartoš, M.; Nečas, A.; et al. Designing of PLA scaffolds for bone tissue replacement fabricated by ordinary commercial 3D printer. J. Biol. Eng. 2017, 11, 31. [Google Scholar] [CrossRef]
- Oladapo, B.; Zahedi, A.; Ismail, S.; Fernando, W.; Ikumapayi, O. 3D-printed biomimetic bone implant polymeric composite scaffolds. Int. J. Adv. Manuf. Technol. 2023, 126, 4259–4267. [Google Scholar] [CrossRef]
- Budharaju, H.; Suresh, S.; Sekar, M.P.; De Vega, B.; Sethuraman, S.; Sundaramurthi, D.; Kalaskar, D.M. Ceramic materials for 3D printing of biomimetic bone scaffolds—Current state-of-the-art & future perspectives. Mater. Des. 2023, 231, 112064. [Google Scholar]
- Rutz, A.L.; Hyland, K.E.; Jakus, A.E.; Burghardt, W.R.; Shah, R.N. A multimaterial bioink method for 3d printing tunable, cell-compatible hydrogels. Adv. Mater. 2015, 27, 1607–1614. [Google Scholar] [CrossRef] [PubMed]
- Baldock, S.J.; Kevin, P.; Harper, G.R.; Griffin, R.; Genedy, H.H.; Fong, M.J.; Zhao, Z.; Zhang, Z.; Shen, Y.; Lin, H.; et al. Creating 3D Objects with Integrated Electronics via Multiphoton Fabrication In Vitro and In Vivo. Adv. Mater. Technol. 2023, 8, 2201274. [Google Scholar] [CrossRef]
- Ota, H.; Emaminejad, S.; Gao, Y.; Zhao, A.; Wu, E.; Challa, S.; Chen, K.; Fahad, H.M.; Jha, A.K.; Kiriya, D.; et al. Application of 3d printing for smart objects with embedded electronic sensors and systems. Adv. Mater. Technol. 2016, 1, 1600013. [Google Scholar] [CrossRef]
- Zhang, Y.; Wang, C. Recent advances in 3D printing hydrogel for topical drug delivery. MedComm—Biomater. Appl. 2022, 1, e11. [Google Scholar] [CrossRef]
- Zhu, X.; Li, H.; Huang, L.; Zhang, M.; Fan, W.; Cui, L. 3D printing promotes the development of drugs. Biomed. Pharmacother. 2020, 131, 110644. [Google Scholar] [CrossRef]
- Kim, D.; Wu, Y.; Oh, Y.-K. On-demand delivery of protein drug from 3D-printed implants. J. Control. Release 2022, 349, 133–142. [Google Scholar] [CrossRef] [PubMed]
- Lepowsky, E.; Tasoglu, S. 3D printing for drug manufacturing: A perspective on the future of pharmaceuticals. Int. J. Bioprint 2017, 4, 119. [Google Scholar]
- Domsta, V.; Seidlitz, A. 3D-printing of drug-eluting implants: An overview of the current developments described in the literature. Molecules 2021, 26, 4066. [Google Scholar] [CrossRef] [PubMed]
- Lewis, P. The promise and peril of 3D bioprinting. Nat. Biomed. Eng. 2020, 4, 79–87. [Google Scholar]
- Kupfer, M.; Lin, W.; Ravnic, D. Three-dimensional bioprinting in medicine: Applications, challenges and potential opportunities for future development. J. Biomed. Mater. Res. Part B Appl. Biomater. 2020, 108, 2495–2512. [Google Scholar]
- Bishop, E.S.; Mostafa, S.; Pakvasa, M.; Luu, H.H.; Lee, M.J.; Wolf, J.M.; Ameer, G.A.; He, T.-C.; Reid, R.R. 3-D bioprinting technologies in tissue engineering and regenerative medicine: Current and future trends. Genes Dis. 2017, 4, 185–195. [Google Scholar] [CrossRef] [PubMed]
- Ahmed, E.; Ali, F.; Hasan, A. Bioprinting and its applications in tissue engineering and regenerative medicine. Int. J. Biol. Biomed. Eng. 2020, 14, 37–49. [Google Scholar]
- Skeldon, G.; Lucendo-Villarin, B.; Shu, W. Three-dimensional bioprinting of stem-cell derived tissues for human regenerative medicine. Phil. Trans. R. Soc. B 2018, 373, 20170224. [Google Scholar] [CrossRef]
- Ong, C.; Yesantharao, P.; Huang, C.; Mattson, G.; Boktor, J.; Fukunishi, T.; Zhang, H.; Hibino, N. 3D bioprinting using stem cells. Pediatr. Res. 2018, 83, 223–231. [Google Scholar] [CrossRef]
- Faramarzi, N.; Yazdi, I.K.; Nabavinia, M.; Gemma, A.; Fanelli, A.; Caizzone, A.; Ptaszek, L.M.; Sinha, I.; Khademhosseini, A.; Ruskin, J.N.; et al. Patient-specific bioinks for 3D bioprinting of tissue engineering scaffolds. Adv. Health Mater. 2018, 7, e1701347. [Google Scholar] [CrossRef] [PubMed]
- Mazzocchi, A.; Soker, S.; Skardal, A. 3D bioprinting for high-throughput screening: Drug screening, disease modeling, and precision medicine applications. Appl. Phys. Rev. 2019, 6, 011302. [Google Scholar] [CrossRef]
- Cacciamali, A.; Villa, R.; Dotti, S. 3D cell cultures: Evolution of an ancient tool for new applications. Front. Physiol. 2022, 13, 836480. [Google Scholar] [CrossRef]
- Liu, G.; Lv, Z.; Batool, S.; Li, M.-Z.; Zhao, P.; Guo, L.; Wang, Y.; Zhou, Y.; Han, S.-T. Biocompatible material-based flexible biosensors: From materials design to wearable/implantable devices and integrated sensing systems. Small 2023, 19, e2207879. [Google Scholar] [CrossRef] [PubMed]
- D’Lima, D.D.; Fregly, B.J.; Colwell, C.W., Jr. Colwell. Implantable sensor technology: Measuring bone and joint biomechanics of daily life in vivo. Arthritis Res. Ther. 2013, 15, 203. [Google Scholar] [CrossRef]
- Barthes, J.; Ozcelik, H.; Hindie, M.; Ndreu-Halili, A.; Hasan, A.; Vrana, N.E. Cell microenvironment engineering and monitoring for tissue engineering and regenerative medicine: The recent advances. Biomed. Res. Int. 2014, 2014, 921905. [Google Scholar] [CrossRef] [PubMed]
- Whulanza, Y.; Ucciferri, N.; Domenici, C.; Vozzi, G.; Ahluwalia, A. Sensing scaffolds to monitor cellular activity using impedance measurements. Biosens. Bioelectron. 2011, 26, 3303–3308. [Google Scholar] [CrossRef] [PubMed]
- Ghorbanizamani, F.; Moulahoum, H.; Celik, E.G.; Timur, S. Material design in implantable biosensors toward future personalized diagnostics and treatments. Appl. Sci. 2023, 13, 4630. [Google Scholar] [CrossRef]
- Haleem, A.; Javaid, M.; Singh, R.P.; Suman, R.; Rab, S. Biosensors applications in medical field: A brief review. Sens. Int. 2021, 2, 100100. [Google Scholar] [CrossRef]
- Sonmezoglu, S.; Fineman, J.R.; Maltepe, E.; Maharbiz, M.M. Monitoring deep-tissue oxygenation with a millimeter-scale ultrasonic implant. Nat. Biotechnol. 2021, 39, 855–864. [Google Scholar] [CrossRef]
- Nelson, B.D.; Karipott, S.S.; Wang, Y.; Ong, K.G. Wireless technologies for implantable devices. Sensors 2020, 20, 4604. [Google Scholar] [CrossRef] [PubMed]
- Kim, J.J.; Stafford, G.R.; Beauchamp, C.; Kim, S.A. Development of a dental implantable temperature sensor for real-time diagnosis of infectious disease. Sensors 2020, 20, 3953. [Google Scholar] [CrossRef] [PubMed]
- Bian, S.; Zhu, B.; Rong, G.; Sawan, M. Towards wearable and implantable continuous drug monitoring: A review. J. Pharm. Anal. 2021, 11, 1–14. [Google Scholar] [CrossRef] [PubMed]
- Liu, Y.; Li, J.; Xiao, S.; Liu, Y.; Bai, M.; Gong, L.; Zhao, J.; Chen, D. Revolutionizing precision medicine: Exploring wearable sensors for therapeutic drug monitoring and personalized therapy. Biosensors 2023, 13, 726. [Google Scholar] [CrossRef] [PubMed]
- Wang, X.; Yu, H.; Kold, S.; Rahbek, O.; Bai, S. Wearable sensors for activity monitoring and motion control: A review. Biomim. Intell. Robot. 2023, 3, 100089. [Google Scholar] [CrossRef]
- Tokucoglu, F. Monitoring physical activity with wearable technologies. Noro Psikiyatr. Ars. 2018, 55, S63–S65. [Google Scholar] [CrossRef] [PubMed]
- Amin, T.; Mobbs, R.J.; Mostafa, N.; Sy, L.W.; Choy, W.J. Wearable devices for patient monitoring in the early postoperative period: A literature review. mHealth 2021, 7, 50. [Google Scholar] [CrossRef] [PubMed]
- Lu, L.; Zhang, J.; Xie, Y.; Gao, F.; Xu, S.; Wu, X.; Ye, Z. Wearable health devices in health care: Narrative systematic review. JMIR mHealth Uhealth 2020, 8, e18907. [Google Scholar] [CrossRef] [PubMed]
- Lopez, X.; Afrin, K.; Nepal, B. Examining the design, manufacturing and analytics of smart wearables. Med. Deivces Sens. 2020, 3, e10087. [Google Scholar] [CrossRef]
- Wang, C.; He, T.; Zhou, H.; Zhang, Z.; Lee, C. Artificial intelligence enhanced sensors—Enabling technologies to next-generation healthcare and biomedical platform. Bioelectron. Med. 2023, 9, 17. [Google Scholar] [CrossRef]
- Darwish, A.; Hassanien, A.E. Wearable and implantable wireless sensor network solutions for healthcare monitoring. Sensors 2011, 11, 5561–5595. [Google Scholar] [CrossRef] [PubMed]
- Ahuja, A.S. The impact of artificial intelligence in medicine on the future role of the physician. PeerJ 2019, 7, e7702. [Google Scholar] [CrossRef] [PubMed]
- Gao, X.; Fraulob, M.; Haiat, G. Biomechanical behaviours of the bone-implant interface: A review. J. R. Soc. Interface 2019, 16, 20190259. [Google Scholar] [CrossRef] [PubMed]
- Johanson, N.A.; Litrenta, J.; Zampini, J.M.; Kleinbart, F.; Goldman, H.M. Surgical treatment options in patients with impaired bone quality. Clin. Orthop. Relat. Res. 2011, 469, 2237–2247. [Google Scholar] [CrossRef] [PubMed]
- Elsayed, M.D. Biomechanical factors that influence the bone-implant-interface. Res. Rep. Oral Maxillofac. Surg. 2019, 3, 023. [Google Scholar]
- Irandoust, S.; Muftu, S. The interplay between bone healing and remodeling around dental implants. Sci. Rep. 2020, 10, 4335. [Google Scholar] [CrossRef] [PubMed]
- Elias, C.N. Factors Affecting the Success of Dental Implants; IntechOpen: London, UK, 2011. [Google Scholar]
- Bailey, S.; Vashishth, D. Mechanical characterization of bone: State of the art in experimental approaches-what types of experiments do people do and how does one interpret the results? Curr. Osteoporos. Rep. 2018, 16, 423–433. [Google Scholar] [CrossRef] [PubMed]
- Morgan, E.F.; Unnikrisnan, G.U.; Hussein, A.I. Bone mechanical properties in healthy and diseased states. Annu. Rev. Biomed. Eng. 2018, 20, 119–143. [Google Scholar] [CrossRef] [PubMed]
- Currey, J. Measurement of the mechanical properties of bone: A recent history. Clin. Orthop. Relat. Res. 2009, 467, 1948–1954. [Google Scholar] [CrossRef]
- Mirzaali, M.J.; Schwiedrzik, J.J.; Thaiwichai, S.; Best, J.P.; Michler, J.; Zysset, P.K.; Wolfram, U. Mechanical properties of cortical bone and their relationships with age, gender, composition and microindentation properties in the elderly. Bone 2016, 93, 196–211. [Google Scholar] [CrossRef]
- National Research Council (US) and Institute of Medicine (US) Committee on the Mathematics and Physics of Emerging Dynamic Biomedical Imaging. Mathematics and Physics of Emerging Biomedical Imaging; National Academies Press (US): Washington, DC, USA, 1996. [Google Scholar]
- Rho, J.Y. Ultrasonic characterisation in determining elastic modulus of trabecular bone material. Med. Biol. Eng. Comput. 1998, 36, 57–59. [Google Scholar] [CrossRef] [PubMed]
- Baroncelli, G. Quantitative ultrasound methods to assess bone mineral status in children: Technical characteristics, performance, and clinical application. Pediatr. Res. 2008, 63, 220–228. [Google Scholar] [CrossRef] [PubMed]
- Teo, J.C.M.; Teo, E.Y.L.; Shim, V.P.W.; Teoh, S.H. Determination of bone trabeculae modulus-an ultrasonic scanning and microCT (mu CT) imaging combination approach. Exp. Mech. 2006, 46, 453–461. [Google Scholar] [CrossRef]
- Kirkpatrick, C.J.; Bittinger, F.; Wagner, M.; Kohler, H.; van Kooten, T.G.; Klein, C.L.; Otto, M. Current trends in biocompatibility testing. Proc. Inst. Mech. Eng. H. 1998, 212, 75–84. [Google Scholar] [CrossRef]
- Albrektsson, T. Osseointegrated Oral Implants Mechanisms of Implant Anchorage, Threats and Long-Term Survival Rates; MDPI—Multidisciplinary Digital Publishing Institute: Basel, Switzerland, 2020. [Google Scholar]
- Nuss, K.M.; von Rechenberg, B. Biocompatibility issues with modern implants in bone—A review for clinical orthopedics. Open Orthop. J. 2008, 2, 66–78. [Google Scholar] [CrossRef] [PubMed]
- Franz, S.; Rammelt, S.; Scharnweber, D.; Simon, J.C. Immune responses to implants—A review of the implications for the design of immunomodulatory biomaterials. Biomaterials 2011, 32, 6692–6709. [Google Scholar] [CrossRef]
- Huzum, B.; Puha, B.; Necoara, R.M.; Gheorghevici, S.; Puha, G.; Filip, A.; Sirbu, P.D.; Alexa, O. Biocompatibility assessment of biomaterials used in orthopedic devices: An overview (review). Exp. Ther. Med. 2021, 22, 1315. [Google Scholar] [CrossRef]
- Al-Shalawi, F.D.; Ariff, A.H.M.; Jung, D.W.; Ariffin, M.K.A.M.; Kim, C.L.S.; Brabazon, D.; Al-Osaimi, M.O. Biomaterials as implants in the orthopedic field for regenerative medicine: Metal versus synthetic polymers. Polymers 2023, 15, 2601. [Google Scholar] [CrossRef] [PubMed]
- Lacroix, D.; Planell, J.A.; Prendergast, P.J. Computer-aided design and finite-element modelling of biomaterial scaffolds for bone tissue engineering. Philos. Trans. R. Soc. A 2009, 367, 1993–2009. [Google Scholar] [CrossRef]
- Isaksson, H. Recent advances in mechanobiological modeling of bone regeneration. Mech. Res. Commun. 2012, 42, 22–31. [Google Scholar] [CrossRef]
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Luo, Y. Toward Fully Automated Personalized Orthopedic Treatments: Innovations and Interdisciplinary Gaps. Bioengineering 2024, 11, 817. https://doi.org/10.3390/bioengineering11080817
Luo Y. Toward Fully Automated Personalized Orthopedic Treatments: Innovations and Interdisciplinary Gaps. Bioengineering. 2024; 11(8):817. https://doi.org/10.3390/bioengineering11080817
Chicago/Turabian StyleLuo, Yunhua. 2024. "Toward Fully Automated Personalized Orthopedic Treatments: Innovations and Interdisciplinary Gaps" Bioengineering 11, no. 8: 817. https://doi.org/10.3390/bioengineering11080817
APA StyleLuo, Y. (2024). Toward Fully Automated Personalized Orthopedic Treatments: Innovations and Interdisciplinary Gaps. Bioengineering, 11(8), 817. https://doi.org/10.3390/bioengineering11080817