Bone Strain Index predicts fragility fracture in osteoporotic women: an artificial intelligence-based study

Bone Strain Index predicts fragility fracture in osteoporotic women: an artificial intelligence-based study

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 ±...

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Bibliographic Details
Published in:European radiology experimental Vol. 5; no. 1; p. 47
Main Authors: Ulivieri, Fabio Massimo, Rinaudo, Luca, Messina, Carmelo, Piodi, Luca Petruccio, Capra, Davide, Lupi, Barbara, Meneguzzo, Camilla, Sconfienza, Luca Maria, Sardanelli, Francesco, Giustina, Andrea, Grossi, Enzo
Format: Journal Article
Language:English
Published: Cham Springer International Publishing 19-10-2021
Springer Nature B.V
SpringerOpen
Subjects:
Absorptiometry, Photon
Aged
Artificial Intelligence
Diagnostic Radiology
Dual-energy x-ray absorptiometry
Female
Finite element analysis
Fractures
Humans
Imaging
Internal Medicine
Interventional Radiology
Medicine
Medicine & Public Health
Middle Aged
Neural network models
Neuroradiology
Original
Original Article
Osteoporosis
Osteoporotic Fractures - diagnostic imaging
Osteoporotic Fractures - epidemiology
Radiology
Retrospective Studies
Semantics
Spinal Fractures
Ultrasound
Neural network models
Osteoporosis
Finite element analysis
Dual-energy x-ray absorptiometry
Artificial intelligence
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Summary: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.
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ISSN:2509-9280
2509-9280
DOI:10.1186/s41747-021-00242-0