Osteoporosis prediction for trabecular bone using machine learning:​ a review

Anam, Marrium; Ponnusamy, Vasaki a/p; Hussain, Muzammil; Nadeem, Muhammad Waqas; Javed, Mazhar; Goh, Hock Guan; Qadeer, Sadia

Affiliations

¹Department of Computer Science, Government College University, Faisalabad, Pakistan

²Faculty of Information and Communication Technology (FICT), Universiti Tunku Abdul Rahman (UTAR), Kampar, Malaysia

³Department of Computer Science, School of Systems and Technology, University of Management and Technology, Lahore, Pakistan

⁴Department of Computer Science, Lahore Garrison University, Lahore, Pakistan

Abstract and keywords

Trabecular bone holds the utmost importance due to its significance regarding early bone loss. Diseases like osteoporosis greatly affect the structure of the Trabecular bone which results in different outcomes like high risk of fracture. The objective of this paper is to inspect the characteristics of the Trabecular Bone by using the Magnetic Resonance Imaging (MRI) technique. These characteristics prove to be quite helpful in studying different studies related to Trabecular bone such as osteoporosis. The things that were considered before the selection of the articles for the systematic review were language, research field, and electronic sources. Only those articles written in the English language were selected as it is the most prominent language used in scientific, engineering, computer science, and biomedical researches. This literature review was conducted on the articles published between 2006 and 2020. A total of 62 research papers out of 1050 papers were extracted which were according to our topic of review after screening abstract and article content for the title and abstract screening. The findings from those researches were compiled at the end of the result section. This systematic literature review presents a comprehensive report on scientific researches and studies that have been done in the medical area concerning trabecular bone.

Keywords: Magnetic resonance imaging; high resolution; trabecular bone (TB); bone structure; machine learning

Links

DOI: 10.32604/cmc.2021.013159

WoS: 000608499100005

History

Received: 2020-07-28

Accepted: 2020-10-05

Cited Works (6)

Year	Entry
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Cited By (1)

Year	Entry
2022	Kunath BA. Design and Validation of an Open-Source 3D Printable Bioreactor System for Ex Vivo Bone Culture [Master's thesis]. Queen's University; June 2022.