Vertebral Fractures among Patients Referred for Bone Densitometry Screening in Dubai Primary Health Care Facilities

Alshaali, Anood Jamal; Aal, Soha Abd El Aziz Abd El; AlJaziri, Amal Mohamad; Abdellatif, Tamer Mohamed Farid; Taryam, Manal Mohammad Omran; Monsef, Nahed

doi:10.1155/2019/7974534

Cited by 4 publications

(2 citation statements)

References 19 publications

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“…Our results demonstrated that a DCNN can be trained to identify VFs within image datasets with high sensitivity (84.7%) and specificity (87.3%). At present, although VFs are common, their diagnosis and treatment may be difficult, particularly in some clinical situations, such as in primary care, emergency medicine, or remote rural areas 22 – 24 . With the assistance of the DCNN, VFs can be detected immediately with a high accuracy rate (86.0%), which may be as high as the detection accuracy of expert’s clinical diagnosis.…”

Section: Discussionmentioning

confidence: 99%

Artificial intelligence for the detection of vertebral fractures on plain spinal radiography

Murata

Endo

Aihara

et al. 2020

Sci Rep

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Vertebral fractures (VFs) cause serious problems, such as substantial functional loss and a high mortality rate, and a delayed diagnosis may further worsen the prognosis. Plain thoracolumbar radiography (PTLR) is an essential method for the evaluation of VFs. Therefore, minimizing the diagnostic errors of VFs on PTLR is crucial. Image identification based on a deep convolutional neural network (DCNN) has been recognized to be potentially effective as a diagnostic strategy; however, the accuracy for detecting VFs has not been fully investigated. A DCNN was trained with PTLR images of 300 patients (150 patients with and 150 without VFs). The accuracy, sensitivity, and specificity of diagnosis of the model were calculated and compared with those of orthopedic residents, orthopedic surgeons, and spine surgeons. The DCNN achieved accuracy, sensitivity, and specificity rates of 86.0% [95% confidence interval (CI) 82.0–90.0%], 84.7% (95% CI 78.8–90.5%), and 87.3% (95% CI 81.9–92.7%), respectively. Both the accuracy and sensitivity of the model were suggested to be noninferior to those of orthopedic surgeons. The DCNN can assist clinicians in the early identification of VFs and in managing patients, to prevent further invasive interventions and a decreased quality of life.

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Section: Discussionmentioning

confidence: 99%

Artificial intelligence for the detection of vertebral fractures on plain spinal radiography

Murata

Endo

Aihara

et al. 2020

Sci Rep

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“…Osteoporotic fractures (OFs) are a common health problem, especially among the elderly. 1,2 They can lead to chronic pain, decreased mobility, and increased morbidity and mortality rates. 3 To date, the underlying mechanisms of OFs remain unclear, but emerging evidence suggests that the circRNA-miRNA-mRNA pathway may play a crucial role in bone metabolism regulation.…”

Section: Introductionmentioning

confidence: 99%

Association between circHIPK3/miR-378a-3p/HDAC4 axis and osteoporotic fractures: A comprehensive investigation

Wang,

Sheng,

Yao

2023

J Orthop Surg (Hong Kong)

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Background Osteoporotic fractures (OFs) are a significant public health issue, which can lead to pain and impaired mobility. The underlying mechanisms of OFs remain unclear, but recent studies have suggested that the circRNA-miRNA-mRNA pathway may play a crucial role. Purpose This study aimed to investigate the potential involvement of the circHIPK3/miR-378a-3p/HDAC4 pathway in the pathogenesis of OFs. Methods We collected tissue and serum samples from 10 patients with OFs and 10 healthy controls. The expression levels of circHIPK3, miR-378a-3p, and HDAC4 were measured by qPCR and WB. Additionally, we quantified the serum levels of bone metabolism-related indicators (ALP, OC, TRAP, OCIF, ODF) using ELISA. Results Our results revealed significant upregulation of circHIPK3 and HDAC4 in both tissue and serum samples from OF patients compared with controls. Simultaneously, we detected a lower expression level of miR-378a-3p in OF tissues and serum than that in the control group. Furthermore, the serum levels of bone metabolism-related indicators ALP, TRAP, OCIF, and ODF were significantly higher in OF patients than in the control group. Interestingly, the serum level of OCIF was lower in OF patients than in the control group. Conclusion Our study provides important evidence for the involvement of the circHIPK3/miR-378a-3p/HDAC4 pathway in the pathogenesis of OFs. The upregulation of circHIPK3 and HDAC4 and downregulation of miR-378a-3p observed in OF patients suggests their potential regulatory effects on bone metabolism. Meanwhile, abnormal expression of serum bone metabolism-related indicators may contribute to the development of OFs by disrupting the balance of bone remodeling.

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Osteoporotic hip and vertebral fractures in the Arab region: a systematic review

et al. 2021

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Vertebral Fractures among Patients Referred for Bone Densitometry Screening in Dubai Primary Health Care Facilities

Cited by 4 publications

References 19 publications

Artificial intelligence for the detection of vertebral fractures on plain spinal radiography

Artificial intelligence for the detection of vertebral fractures on plain spinal radiography

Association between circHIPK3/miR-378a-3p/HDAC4 axis and osteoporotic fractures: A comprehensive investigation

Osteoporotic hip and vertebral fractures in the Arab region: a systematic review

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