| Autor: |
Fabio Massimo Ulivieri, Luca Rinaudo, Carmelo Messina, Luca Petruccio Piodi, Davide Capra, Barbara Lupi, Camilla Meneguzzo, Luca Maria Sconfienza, Francesco Sardanelli, Andrea Giustina, Enzo Grossi |
| Jazyk: |
angličtina |
| Rok vydání: |
2021 |
| Předmět: |
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| Zdroj: |
European Radiology Experimental, Vol 5, Iss 1, Pp 1-11 (2021) |
| Druh dokumentu: |
article |
| ISSN: |
2509-9280 |
| DOI: |
10.1186/s41747-021-00242-0 |
| Popis: |
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. |
| Databáze: |
Directory of Open Access Journals |
| Externí odkaz: |
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