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Review
. 2024 May 12;11(5):484.
doi: 10.3390/bioengineering11050484.

Application of Artificial Intelligence Methods on Osteoporosis Classification with Radiographs-A Systematic Review

Ren Wei Liu  1 , Wilson Ong  1 , Andrew Makmur  1   2 , Naresh Kumar  3 , Xi Zhen Low  1   2 , Ge Shuliang  1   2 , Tan Yi Liang  1   2 , Dominic Fong Kuan Ting  1   2 , Jiong Hao Tan  3 , James Thomas Patrick Decourcy Hallinan  1   2
Affiliations
Review

Application of Artificial Intelligence Methods on Osteoporosis Classification with Radiographs-A Systematic Review

Ren Wei Liu et al. Bioengineering (Basel). .

Abstract

Osteoporosis is a complex endocrine disease characterized by a decline in bone mass and microstructural integrity. It constitutes a major global health problem. Recent progress in the field of artificial intelligence (AI) has opened new avenues for the effective diagnosis of osteoporosis via radiographs. This review investigates the application of AI classification of osteoporosis in radiographs. A comprehensive exploration of electronic repositories (ClinicalTrials.gov, Web of Science, PubMed, MEDLINE) was carried out in adherence to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses 2020 statement (PRISMA). A collection of 31 articles was extracted from these repositories and their significant outcomes were consolidated and outlined. This encompassed insights into anatomical regions, the specific machine learning methods employed, the effectiveness in predicting BMD, and categorizing osteoporosis. Through analyzing the respective studies, we evaluated the effectiveness and limitations of AI osteoporosis classification in radiographs. The pooled reported accuracy, sensitivity, and specificity of osteoporosis classification ranges from 66.1% to 97.9%, 67.4% to 100.0%, and 60.0% to 97.5% respectively. This review underscores the potential of AI osteoporosis classification and offers valuable insights for future research endeavors, which should focus on addressing the challenges in technical and clinical integration to facilitate practical implementation of this technology.

Keywords: artificial intelligence; deep learning; imaging; machine learning; osteoporosis; radiographs.

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Conflict of interest statement

The authors declare no conflicts of interest.

Figures

Figure 2
Figure 2
Diagram showing model development and application in the classification of medical images. The top row depicts the training process (A) and the bottom row the prediction process (B).
Figure 1
Figure 1
PRISMA flowchart for the literature search (adapted from the PRISMA group, 2020), which describes the selection of relevant articles.
Figure 3
Figure 3
Diagram showing the process of medical image data handling for machine learning.
See this image and copyright information in PMC

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