Osseointegration Pharmacology: A Systematic Mapping Using Artificial Intelligence. (1st January 2021)
- Record Type:
- Journal Article
- Title:
- Osseointegration Pharmacology: A Systematic Mapping Using Artificial Intelligence. (1st January 2021)
- Main Title:
- Osseointegration Pharmacology: A Systematic Mapping Using Artificial Intelligence
- Authors:
- Mahri, Mohammed
Shen, Nicole
Berrizbeitia, Francisco
Rodan, Rania
Daer, Ammar
Faigan, Matthew
Taqi, Doaa
Wu, Kevin Yang
Ahmadi, Motahareh
Ducret, Maxime
Emami, Elham
Tamimi, Faleh - Abstract:
- Abstract: Clinical performance of osseointegrated implants could be compromised by the medications taken by patients. The effect of a specific medication on osseointegration can be easily investigated using traditional systematic reviews. However, assessment of all known medications requires the use of evidence mapping methods. These methods allow assessment of complex questions, but they are very resource intensive when done manually. The objective of this study was to develop a machine learning algorithm to automatically map the literature assessing the effect of medications on osseointegration. Datasets of articles classified manually were used to train a machine-learning algorithm based on Support Vector Machines. The algorithm was then validated and used to screen 599, 604 articles identified with an extremely sensitive search strategy. The algorithm included 281 relevant articles that described the effect of 31 different drugs on osseointegration. This approach achieved an accuracy of 95%, and compared to manual screening, it reduced the workload by 93%. The systematic mapping revealed that the treatment outcomes of osseointegrated medical devices could be influenced by drugs affecting homeostasis, inflammation, cell proliferation and bone remodeling. The effect of all known medications on the performance of osseointegrated medical devices can be assessed using evidence mappings executed with highly accurate machine learning algorithms. Graphical abstract: Image,Abstract: Clinical performance of osseointegrated implants could be compromised by the medications taken by patients. The effect of a specific medication on osseointegration can be easily investigated using traditional systematic reviews. However, assessment of all known medications requires the use of evidence mapping methods. These methods allow assessment of complex questions, but they are very resource intensive when done manually. The objective of this study was to develop a machine learning algorithm to automatically map the literature assessing the effect of medications on osseointegration. Datasets of articles classified manually were used to train a machine-learning algorithm based on Support Vector Machines. The algorithm was then validated and used to screen 599, 604 articles identified with an extremely sensitive search strategy. The algorithm included 281 relevant articles that described the effect of 31 different drugs on osseointegration. This approach achieved an accuracy of 95%, and compared to manual screening, it reduced the workload by 93%. The systematic mapping revealed that the treatment outcomes of osseointegrated medical devices could be influenced by drugs affecting homeostasis, inflammation, cell proliferation and bone remodeling. The effect of all known medications on the performance of osseointegrated medical devices can be assessed using evidence mappings executed with highly accurate machine learning algorithms. Graphical abstract: Image, graphical abstract … (more)
- Is Part Of:
- Acta biomaterialia. Volume 119(2021)
- Journal:
- Acta biomaterialia
- Issue:
- Volume 119(2021)
- Issue Display:
- Volume 119, Issue 2021 (2021)
- Year:
- 2021
- Volume:
- 119
- Issue:
- 2021
- Issue Sort Value:
- 2021-0119-2021-0000
- Page Start:
- 284
- Page End:
- 302
- Publication Date:
- 2021-01-01
- Subjects:
- osseointegration -- dental implants -- pharmacological agents -- artificial intelligence -- automated screening -- machine learning -- bone-implant contact -- systematic mapping -- drugs -- prosthetic implants
Biomedical materials -- Periodicals
610.28 - Journal URLs:
- http://www.sciencedirect.com/science/journal/17427061 ↗
http://www.elsevier.com/wps/find/journaldescription.cws%5Fhome/702994/description ↗
http://www.elsevier.com/journals ↗ - DOI:
- 10.1016/j.actbio.2020.11.011 ↗
- Languages:
- English
- ISSNs:
- 1742-7061
- Deposit Type:
- Legaldeposit
- View Content:
- Available online (eLD content is only available in our Reading Rooms) ↗
- Physical Locations:
- British Library DSC - 0602.900500
British Library DSC - BLDSS-3PM
British Library HMNTS - ELD Digital store - Ingest File:
- 21257.xml