![]() the patient is erect, left side against the upright detector.Note, such functional views should not be performed on trauma patients without the strict instructions of a qualified clinician. 2021 Aug 26 11(17):7858.These views are specialized projections often requested to assess for spinal stability. Basic knowledge and new advances in panoramic radiography imaging techniques: A narrative review on what dentists and radiologists should know. Izzetti R, Nisi M, Aringhieri G, Crocetti L, Graziani F, Nardi C. A package to compute segmentation metrics: seg-metrics. InProceedings of the IEEE conference on computer vision and pattern recognition 2017 (pp. Densely connected convolutional networks. Huang G, Liu Z, Van Der Maaten L, Weinberger KQ. InProceedings of the AAAI conference on artificial intelligence 2017 Feb 12 (Vol. Inception-v4, inception-resnet and the impact of residual connections on learning. Szegedy C, Ioffe S, Vanhoucke V, Alemi A. InInternational conference on machine learning 2019 May 24 (pp. Efficientnet: Rethinking model scaling for convolutional neural networks. InInternational Conference on Medical image computing and computer-assisted intervention 2015 Oct 5 (pp. U-net: Convolutional networks for biomedical image segmentation. Occipitocervical junction: imaging, pathology, instrumentation. Reassessment of the craniocervical junction: normal values on CT. Rojas CA, Bertozzi JC, Martinez CR, Whitlow J. Basion–Cartilaginous Dens Interval: An Imaging Parameter for Craniovertebral Junction Assessment in Children. Singh AK, Fulton Z, Tiwari R, Zhang X, Lu L, Altmeyer WB, Tantiwongkosi B. Analysis of measurement accuracy for craniovertebral junction pathology: most reliable method for cephalometric analysis. Lee HJ, Hong JT, Kim IS, Kwon JY, Lee SW. In2019 2nd International Conference on Communication, Computing and Digital systems (C-CODE) 2019 Mar 6 (pp. ![]() A novel framework to segment out cervical vertebrae. Rehman F, Shah SI, Gilani SO, Emad D, Riaz MN, Faiza R. Computer methods and programs in biomedicine. Fully automatic cervical vertebrae segmentation framework for X-ray images. A review of the diagnosis and treatment of atlantoaxial dislocations. Yang SY, Boniello AJ, Poorman CE, Chang AL, Wang S, Passias PG. Journal of the American Academy of Orthopaedic Surgeons. Occipitocervical Dissociation in Three Siblings: A Pediatric Case Report and Review of the Literature. Traumatic Atlanto-Occipital Dislocation-A Comprehensive Analysis of All Case Series Found in the Spinal Trauma Literature. Traumatic atlanto-occipital dislocation (AOD). Kim YJ, Yoo CJ, Park CW, Lee SG, Son S, Kim WK. Identifying survivors with traumatic craniocervical dissociation: a retrospective study. These findings demonstrate the potential of multiclass segmentation in automating the measurement of diagnostic metrics for cervical spine injuries and showcase the clinical potential for diagnosing cervical spine injuries and evaluating cervical surgical outcomes.Ĭooper Z, Gross JA, Lacey JM, Traven N, Mirza SK, Arbabi S. No metric showed adjusted significant differences at P < 0.05 between manual and automatic metric measuring methods. Comparison of manually measured metrics and automatically measured metrics showed high Pearson’s correlation coefficients in McGregor’s line ( r = 0.89), space available cord ( r = 0.94), cervical sagittal vertical axis ( r = 0.99), cervical lordosis ( r = 0.88), lower correlations in basion-dens interval ( r = 0.65), basion-axial interval ( r = 0.72), and Powers ratio ( r = 0.62). The three models demonstrated high average dice coefficient values for the cervical spine (C1, 0.93 C2, 0.96 C3, 0.96 C4, 0.96 C5, 0.96 C6, 0.96 C7, 0.95) and lower values for the craniofacial bones (hard palate, 0.69 basion, 0.81 opisthion, 0.71). Diagnostic metrics automatically measured using computer vision algorithms were compared with manually measured metrics through Pearson’s correlation coefficient and paired t-tests. A total of 852 cervical X-rays obtained from Gachon Medical Center were used for multiclass segmentation of the craniofacial bones (hard palate, basion, opisthion) and cervical spine (C1–C7), incorporating architectures such as EfficientNetB4, DenseNet201, and InceptionResNetV2. Such assessment can be facilitated through the use of automatic methods such as machine learning and computer vision algorithms. Accurate assessment of cervical spine X-ray images through diagnostic metrics plays a crucial role in determining appropriate treatment strategies for cervical injuries and evaluating surgical outcomes.
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