Bone Strain Index predicts fragility fracture in osteoporotic women: an artificial intelligence-based study.
Eur Radiol Exp
; 5(1): 47, 2021 10 19.
Article
em En
| MEDLINE
| ID: mdl-34664136
ABSTRACT
BACKGROUND:
We applied an artificial intelligence-based model to predict fragility fractures in postmenopausal women, using different dual-energy x-ray absorptiometry (DXA) parameters.METHODS:
One hundred seventy-four postmenopausal women without vertebral fractures (VFs) at baseline (mean age 66.3 ± 9.8) were retrospectively evaluated. Data has been collected from September 2010 to August 2018. All subjects performed a spine x-ray to assess VFs, together with lumbar and femoral DXA for bone mineral density (BMD) and the bone strain index (BSI) evaluation. Follow-up exams were performed after 3.34 ± 1.91 years. Considering the occurrence of new VFs at follow-up, two groups were created fractured versus not-fractured. We applied an artificial neural network (ANN) analysis with a predictive tool (TWIST system) to select relevant input data from a list of 13 variables including BMD and BSI. A semantic connectivity map was built to analyse the connections among variables within the groups. For group comparisons, an independent-samples t-test was used; variables were expressed as mean ± standard deviation.RESULTS:
For each patient, we evaluated a total of n = 6 exams. At follow-up, n = 69 (39.6%) women developed a VF. ANNs reached a predictive accuracy of 79.56% within the training testing procedure, with a sensitivity of 80.93% and a specificity of 78.18%. The semantic connectivity map showed that a low BSI at the total femur is connected to the absence of VFs.CONCLUSION:
We found a high performance of ANN analysis in predicting the occurrence of VFs. Femoral BSI appears as a useful DXA index to identify patients at lower risk for lumbar VFs.Palavras-chave
Texto completo:
1
Base de dados:
MEDLINE
Assunto principal:
Fraturas da Coluna Vertebral
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Fraturas por Osteoporose
Tipo de estudo:
Observational_studies
/
Prognostic_studies
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Risk_factors_studies
Limite:
Aged
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Female
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Humans
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Middle aged
Idioma:
En
Revista:
Eur Radiol Exp
Ano de publicação:
2021
Tipo de documento:
Article
País de afiliação:
Itália