Abstract:The results of the evaluation show that the presented model-based approach is very promising and also comparable to studies that partly used higher image resolutions for bone quality assessment and fracture risk prediction.
“…Apart from using InShape models for diagnostic purposes, 30,31 they can also be used for segmentation tasks 18 in combination with geodesic active contours (GAC). 20 In contrast to most other segmentation approaches, which are based on the usage of statistical shape or appearance models, this approach is less restrictive, since it is not forcing the final contour to be exactly determined by a model instance.…”
Section: E Segmenting Images Using Inshape Models and Geodesic Actmentioning
The presented framework provides accurate segmentation results for three important structures in the head neck area. Compared to a segmentation approach based on using multiple atlases in combination with label fusion, the proposed hybrid approach provided more accurate results within a clinically acceptable amount of time.
“…Apart from using InShape models for diagnostic purposes, 30,31 they can also be used for segmentation tasks 18 in combination with geodesic active contours (GAC). 20 In contrast to most other segmentation approaches, which are based on the usage of statistical shape or appearance models, this approach is less restrictive, since it is not forcing the final contour to be exactly determined by a model instance.…”
Section: E Segmenting Images Using Inshape Models and Geodesic Actmentioning
The presented framework provides accurate segmentation results for three important structures in the head neck area. Compared to a segmentation approach based on using multiple atlases in combination with label fusion, the proposed hybrid approach provided more accurate results within a clinically acceptable amount of time.
“…108 records were removed after review of titles, abstracts, and full-texts, among which 16 were irrelevant to the theme, and 92 identified as AI on osteoporosis in other fields, including 2 about applications of AI on omics, 44 about applications of AI on prediction of BMD and screening and diagnosis of osteoporosis, 24 about applications of AI on diagnosis of fracture, 17 about applications of AI on assessment of bone quality or bone microstructure, and 5 about applications of AI on iatrogenic intervention related to osteoporosis and its prognosis. A total of 19 of these records [ 36 – 54 ] concerned with the applications of AI on prediction of fracture risks were considered eligible for inclusion in the current literature review as presented in Table 1 . Fig.…”
Section: Performance Of Ai In Predicting Osteoporotic Fracturementioning
Osteoporotic fractures impose a substantial burden on patients with diabetes due to their unique characteristics in bone metabolism, limiting the efficacy of conventional fracture prediction tools. Artificial intelligence (AI) algorithms have shown great promise in predicting osteoporotic fractures. This review aims to evaluate the application of traditional fracture prediction tools (FRAX, QFracture, and Garvan FRC) in patients with diabetes and osteoporosis, review AI-based fracture prediction achievements, and assess the potential efficiency of AI algorithms in this population. This comprehensive literature search was conducted in Pubmed and Web of Science. We found that conventional prediction tools exhibit limited accuracy in predicting fractures in patients with diabetes and osteoporosis due to their distinct bone metabolism characteristics. Conversely, AI algorithms show remarkable potential in enhancing predictive precision and improving patient outcomes. However, the utilization of AI algorithms for predicting osteoporotic fractures in diabetic patients is still in its nascent phase, further research is required to validate their efficacy and assess the potential advantages of their application in clinical practice.
“…45 In summary, (micro-) structural parameters are important predictors of bone strength and fracture load. The latest high-resolution imaging devices are capable of directly assessing microstructural parameters, but are not currently suitable for in vivo imaging of the proximal femur.…”
Section: Fracture Risk Assessment Tools Being Researchedmentioning
Femoral fracture risk prediction is a necessary step preceding effective pharmacological intervention or pre-operative planning. Current clinical methods for fracture risk prediction rely on 2D imaging methods and have limited predictive value. Researchers are therefore trying to find improved methods for fracture prediction. During last few decades, many studies have focused on integration of 3D imaging techniques and the finite element (FE) method to improve the accuracy of fracture assessment techniques. In this paper, we review the recent advances in FE and other techniques for predicting the risk of femoral fractures. Based on a number of selected studies, the different steps that are involved in generation of patient-specific FE models are reviewed with particular emphasis on the fracture criteria. The inaccuracies that might arise due to the imperfections of the involved steps are also discussed. It is concluded that compared to image-and geometry-based techniques, FE is a more promising approach for prediction of fracture loads. However, certain technological advancements in FE modeling protocols are required before FE modeling can be recruited in clinical settings.
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