Capability of CBCT to identify patients with low bone mineral density: a systematic review.
Eliete N S GuerraFabiana T AlmeidaFernanda V BezerraPaulo T D S FigueiredoMaria A G SilvaGraziela De Luca CantoCamila Pachêco-PereiraAndre Ferreira LeitePublished in: Dento maxillo facial radiology (2017)
The aim of this study was to systematically review the literature about the capability of CBCT images to identify individuals with low bone mineral density (BMD). As the literature is scarce regarding this topic, the purpose of this systematic review is also to guide future research in this area. A detailed search was performed in five databases without restrictions of time or languages. Additionally, a grey literature search was conducted. The Quality Assessment Tool for Diagnostic Accuracy Studies-2 was applied to evaluate the methodological design of selected studies. With the inclusion of only six studies, the evidence is limited to endorse the use of CBCT assertively as a diagnostic tool for low BMD. All of the three studies that analyzed radiomorphometric indices found that the linear measurements of the mandibular inferior cortex were lower in osteoporotic individuals. CBCT-derived radiographic density vertebral and mandibular measurements were also capable for differentiating individuals with osteoporosis from individuals with normal BMD. The analysis of the cervical vertebrae showed high accuracy measurements. This systematic review indicates a scarcity of studies regarding the potential of CBCT for screening individuals with low BMD. However, the studies indicate that radiomorphometric indices and CBCT-derived radiographic density should be promising tools for differentiating individuals with osteoporosis from individuals with normal BMD.
Keyphrases
- bone mineral density
- systematic review
- postmenopausal women
- cone beam computed tomography
- body composition
- case control
- image quality
- meta analyses
- computed tomography
- current status
- magnetic resonance imaging
- white matter
- quality improvement
- convolutional neural network
- magnetic resonance
- multiple sclerosis
- artificial intelligence