Assessing the effects of long-term osteoporosis treatment by using conventional spine radiographs: results from a pilot study in a sub-cohort of a large randomized controlled trial.
Hans Peter Dimai,R. Ljuhar,D. Ljuhar,B. Norman,Stefan Nehrer,Andreas Kurth,Astrid Fahrleitner-Pammer +6 more
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TLDR
This pilot study provides evidence that lumbar spine BSV as obtained from conventional radiographs constitutes a useful means for the assessment of bone-specific treatment effects in postmenopausal women with osteoporosis.Abstract:
To evaluate the clinical applicability of a software tool developed to extract bone textural information from conventional lumbar spine radiographs, and to test it in a subset of postmenopausal women treated for osteoporosis with the fully human monoclonal antibody denosumab. The software was developed based on the principles of a fractal model using pixel grey-level variations together with a specific machine-learning algorithm. The obtained dimensionless parameter, termed bone structure value (BSV), was then tested and compared to bone mineral density (BMD) in a sub-cohort of postmenopausal women with osteoporosis who were treated with the monoclonal antibody denosumab, within the framework of a large randomized controlled trial and its open-label extension phase. After 3 years and after 8 years of treatment with denosumab, mean lumbar spine BMD as well as mean lumbar BSV were significantly higher compared to study entry (one-way repeated measures ANOVA for DXA: F = 108.2, p < 0.00001; and for BSV: F = 84.3, p < 0.00001). The overall increase in DXA-derived lumbar spine BMD at year 8 was + 42% (mean ± SD; 0.725 ± 0.038 g/cm2 to 1.031 ± 0.092 g/cm2; p < 0.0001), and the overall increase of BSV was 255% (mean ± SD; 0.076 ± 0.022 to 0.270 ± 0.09, p < 0.0001). Overall, BMD and BSV were significantly correlated (R = 0.51; p < 0.0001). This pilot study provides evidence that lumbar spine BSV as obtained from conventional radiographs constitutes a useful means for the assessment of bone-specific treatment effects in postmenopausal women with osteoporosis.read more
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Deep Learning for Osteoporosis Classification Using Hip Radiographs and Patient Clinical Covariates
Norio Yamamoto,Shintaro Sukegawa,Akira Kitamura,Ryosuke Goto,Tomoyuki Noda,Keisuke Nakano,Kiyofumi Takabatake,Hotaka Kawai,Hitoshi Nagatsuka,Keisuke Kawasaki,Yoshihiko Furuki,Toshifumi Ozaki +11 more
TL;DR: The CNN models diagnosed osteoporosis from hip radiographs with high accuracy, and their performance improved further with the addition of clinical covariates from patient records, including GoogleNet and EfficientNet b3.
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Artificial intelligence, osteoporosis and fragility fractures.
TL;DR: New methods for automatic image segmentation, and prediction of fracture risk show promising clinical value, Though these recent developments have had a successful initial application to osteoporosis research, their development is still under improvement.
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Quantification in Musculoskeletal Imaging Using Computational Analysis and Machine Learning: Segmentation and Radiomics.
TL;DR: Common used segmentation methods are presented, the radiomics pipeline is described, and the advent of machine learning offers promising prospects for automatic segmentation and integration of large amounts of data.
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Artificial intelligence in spine care: current applications and future utility
Alexander L. Hornung,Christopher M. Hornung,Gary Michael Mallow,J. N. Barajas,Augustus J. Rush,Arash J. Sayari,Fabio Galbusera,Hans-Joachim Wilke,Matthew W. Colman,Frank M. Phillips,Howard S. An,Dino Samartzis +11 more
TL;DR: In the future, it will be critical for the spine specialist to be able to discern the utility of novel AI research, particularly as it continues to pervade facets of everyday spine surgery.
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Current Applications of Machine Learning in Spine: From Clinical View.
TL;DR: In this paper, the authors present current applications of machine learning (ML) in spine domain to clinicians, and conduct a comprehensive PubMed search to identify relevant applications of ML in this domain.
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