Machine learning algorithms for diagnosis of hip bone osteoporosis: a systematic review and meta-analysis study
Abstract Background Osteoporosis is a significant health problem in the skeletal system, associated with bone tissue changes and its strength. Machine Learning (ML), on the other hand, has been accompanied by improvements in recent years and has been in the spotlight. This study is designed to inves...
| Main Authors: | , , , , , , , |
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| Format: | Article |
| Language: | English |
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BMC
2023-07-01
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| Series: | BioMedical Engineering OnLine |
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| Online Access: | https://doi.org/10.1186/s12938-023-01132-9 |
| _version_ | 1797778750566301696 |
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| author | Fakher Rahim Amin Zaki Zadeh Pouya Javanmardi Temitope Emmanuel Komolafe Mohammad Khalafi Ali Arjomandi Haniye Alsadat Ghofrani Kiarash Shirbandi |
| author_facet | Fakher Rahim Amin Zaki Zadeh Pouya Javanmardi Temitope Emmanuel Komolafe Mohammad Khalafi Ali Arjomandi Haniye Alsadat Ghofrani Kiarash Shirbandi |
| author_sort | Fakher Rahim |
| collection | DOAJ |
| description | Abstract Background Osteoporosis is a significant health problem in the skeletal system, associated with bone tissue changes and its strength. Machine Learning (ML), on the other hand, has been accompanied by improvements in recent years and has been in the spotlight. This study is designed to investigate the Diagnostic Test Accuracy (DTA) of ML to detect osteoporosis through the hip dual-energy X-ray absorptiometry (DXA) images. Methods The ISI Web of Science, PubMed, Scopus, Cochrane Library, IEEE Xplore Digital Library, CINAHL, Science Direct, PROSPERO, and EMBASE were systematically searched until June 2023 for studies that tested the diagnostic precision of ML model-assisted for predicting an osteoporosis diagnosis. Results The pooled sensitivity of univariate analysis of seven studies was 0.844 (95% CI 0.791 to 0.885, I 2 = 94% for 7 studies). The pooled specificity of univariate analysis was 0.781 (95% CI 0.732 to 0.824, I 2 = 98% for 7 studies). The pooled diagnostic odds ratio (DOR) was 18.91 (95% CI 14.22 to 25.14, I 2 = 93% for 7 studies). The pooled mean positive likelihood ratio (LR+) and the negative likelihood ratio (LR−) were 3.7 and 0.22, respectively. Also, the summary receiver operating characteristics (sROC) of the bivariate model has an AUC of 0.878. Conclusion Osteoporosis can be diagnosed by ML with acceptable accuracy, and hip fracture prediction was improved via training in an Architecture Learning Network (ALN). |
| first_indexed | 2024-03-12T23:22:03Z |
| format | Article |
| id | doaj.art-2fe407531a3c413bbc0328b426b4a52b |
| institution | Directory Open Access Journal |
| issn | 1475-925X |
| language | English |
| last_indexed | 2024-03-12T23:22:03Z |
| publishDate | 2023-07-01 |
| publisher | BMC |
| record_format | Article |
| series | BioMedical Engineering OnLine |
| spelling | doaj.art-2fe407531a3c413bbc0328b426b4a52b2023-07-16T11:23:09ZengBMCBioMedical Engineering OnLine1475-925X2023-07-0122111510.1186/s12938-023-01132-9Machine learning algorithms for diagnosis of hip bone osteoporosis: a systematic review and meta-analysis studyFakher Rahim0Amin Zaki Zadeh1Pouya Javanmardi2Temitope Emmanuel Komolafe3Mohammad Khalafi4Ali Arjomandi5Haniye Alsadat Ghofrani6Kiarash Shirbandi7Department of Anesthesia, Cihan University - SulaimaniyaMedical Doctor (MD), School of Medicine, Ahvaz Jondishapour University of Medical SciencesDepartment of Radiologic Technology, Faculty of Paramedicine, Ahvaz Jundishapur University of Medical SciencesSchool of Biomedical Engineering, ShanghaiTech UniversitySchool of Medicine, Tabriz University of Medical SciencesDepartment of Radiologic Technology, Faculty of Paramedicine, Ahvaz Jundishapur University of Medical SciencesDepartment of Radiologic Technology, Faculty of Paramedicine, Ahvaz Jundishapur University of Medical SciencesResearch Center for Molecular and Cellular Imaging, Tehran University of Medical SciencesAbstract Background Osteoporosis is a significant health problem in the skeletal system, associated with bone tissue changes and its strength. Machine Learning (ML), on the other hand, has been accompanied by improvements in recent years and has been in the spotlight. This study is designed to investigate the Diagnostic Test Accuracy (DTA) of ML to detect osteoporosis through the hip dual-energy X-ray absorptiometry (DXA) images. Methods The ISI Web of Science, PubMed, Scopus, Cochrane Library, IEEE Xplore Digital Library, CINAHL, Science Direct, PROSPERO, and EMBASE were systematically searched until June 2023 for studies that tested the diagnostic precision of ML model-assisted for predicting an osteoporosis diagnosis. Results The pooled sensitivity of univariate analysis of seven studies was 0.844 (95% CI 0.791 to 0.885, I 2 = 94% for 7 studies). The pooled specificity of univariate analysis was 0.781 (95% CI 0.732 to 0.824, I 2 = 98% for 7 studies). The pooled diagnostic odds ratio (DOR) was 18.91 (95% CI 14.22 to 25.14, I 2 = 93% for 7 studies). The pooled mean positive likelihood ratio (LR+) and the negative likelihood ratio (LR−) were 3.7 and 0.22, respectively. Also, the summary receiver operating characteristics (sROC) of the bivariate model has an AUC of 0.878. Conclusion Osteoporosis can be diagnosed by ML with acceptable accuracy, and hip fracture prediction was improved via training in an Architecture Learning Network (ALN).https://doi.org/10.1186/s12938-023-01132-9Bone diseasesMetabolicOsteoporosisLower extremityHipArtificial intelligence |
| spellingShingle | Fakher Rahim Amin Zaki Zadeh Pouya Javanmardi Temitope Emmanuel Komolafe Mohammad Khalafi Ali Arjomandi Haniye Alsadat Ghofrani Kiarash Shirbandi Machine learning algorithms for diagnosis of hip bone osteoporosis: a systematic review and meta-analysis study BioMedical Engineering OnLine Bone diseases Metabolic Osteoporosis Lower extremity Hip Artificial intelligence |
| title | Machine learning algorithms for diagnosis of hip bone osteoporosis: a systematic review and meta-analysis study |
| title_full | Machine learning algorithms for diagnosis of hip bone osteoporosis: a systematic review and meta-analysis study |
| title_fullStr | Machine learning algorithms for diagnosis of hip bone osteoporosis: a systematic review and meta-analysis study |
| title_full_unstemmed | Machine learning algorithms for diagnosis of hip bone osteoporosis: a systematic review and meta-analysis study |
| title_short | Machine learning algorithms for diagnosis of hip bone osteoporosis: a systematic review and meta-analysis study |
| title_sort | machine learning algorithms for diagnosis of hip bone osteoporosis a systematic review and meta analysis study |
| topic | Bone diseases Metabolic Osteoporosis Lower extremity Hip Artificial intelligence |
| url | https://doi.org/10.1186/s12938-023-01132-9 |
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