Evolution in fracture risk assessment: artificial versus augmented intelligence

Hans, Didier; Shevroja, Enisa; Leslie, William D.

doi:10.1007/s00198-020-05737-x

Cited by 6 publications

(1 citation statement)

References 18 publications

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“…There is also great interest in the use of deep learning techniques to uncover further risk factors and predict fracture risk independent of BMD and FRAX [101]. For example, Yosibash et al [102] described an autonomous algorithm combining autonomous finite element analysis and machine learning techniques for accurate prediction of future hip fracture risk assessment from CT scans of the abdomen and pelvis [102].…”

Section: Future Directions In the Use Of Ai In Osteoporosismentioning

confidence: 99%

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

Liu,

Ong,

Makmur

et al. 2024

Bioengineering

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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.

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Section: Future Directions In the Use Of Ai In Osteoporosismentioning

confidence: 99%