Cobb angle measurements on digital radiographs using Bunnell scoliometer: Validation of the method

Are Craniofacial Asymmetries Related to Trunk Asymmetries?

OBJECTIVE

The aim of this study was to investigate whether craniofacial asymmetries could be a predictor of spine asymmetries or not.

METHODS

Female individuals aged between 18 and 25 years participated in this cross-sectional descriptive-analytic study. The angle of trunk rotation was measured by a scoliometer in conjunction with the Adams forward-bending test. Individuals in the control group (n = 57) had spinal curvature of 0 to 3 degrees, and individuals in the study group (n = 53) had spinal curvature of between 4 and 6 degrees. Facial anthropometric measurements of individuals were evaluated by referencing anatomical landmarks determined on the face with 2-dimensional photogrammetry using ImageJ (Version 1.53q) program.

RESULTS

Both groups were similar in terms of facial measurements (P > 0.05). Axial trunk rotation values of the mid-thoracic and thoraco-lumbar regions were significantly higher on both sides of the asymmetry group (P < 0.05). The distances between facial landmarks are not associated with asymmetries of spine regions (P > 0.05).

CONCLUSION

Although the sensitivity and predictive value of facial asymmetries in determining trunk asymmetries and sagittal spinal posture were low, it was concluded that comparative studies are needed, especially in individuals with asymmetry at the level of scoliosis, as the asymmetries of the individuals in our study were not at the level of scoliosis.

A computer vision-based mobile tool for assessing human posture: A validation study

BACKGROUND AND OBJECTIVE

Non-invasive methods for postural assessment are tools used for tracking and monitoring the progression of postural deviations. Different computer-based methods have been used to assess human posture, including mobile applications based on images and sensors. However, such solutions still require manual identification of anatomical points. This study aims to present and validate the NLMeasurer, a mobile application for postural assessment. This application takes advantage of the PoseNet, a solution based on computer vision and machine learning used to estimate human pose and identify anatomical points. From the identified points, NLMeasurer calculates postural measures.

METHODS

Twenty participants were photographed in front view while using surface markers over anatomical landmarks. Then, the surface markers were removed, and new photos were taken. The photos were analyzed by two examiners, and six postural measurements were computed with NLMeasurer and a validated biophotogrammetry software. One-sample t-test and Bland Altman procedure were used to assess agreement between the methods, and Intraclass Correlation Coefficient (ICC) was used to assess inter- and intra-rater reliability.

RESULTS

Postural measurements calculated using the NLMeasurer were in agreement with the biophotogrammetry software. Furthermore, there was good inter- and intra-rater reliability for most photos without surface markers.

CONCLUSIONS

NLMeasurer demonstrated to be a valid tool method to assess postural measurements in the frontal view. The use of surface markers on specific anatomical landmarks (i.e., ears, iliac spines and ankles) can facilitate the digital identification of these landmarks and improve the reliability of the postural measurements performed with NLMeasurer.

Mobile Applications for Assessing Human Posture: A Systematic Literature Review

Smartphones are increasingly incorporated with features such as sensors and high resolution cameras that empower their capabilities, enabling their use for varied activities including human posture assessments. Previous reviews have discussed methods used in postural assessment but none of them focused exclusively on mobile applications. This paper systematically reviews mobile applications proposed for analyzing human posture based on alignment of the body in the sagittal and coronal plane. The main digital libraries were searched, 26 articles published between 2010 and 2020 were selected, and 13 mobile applications were identified, classified and discussed. Results showed that the use of mobile applications to assist with posture assessment have been demonstrated to be reliable, and this can contribute to clinical practice of health professionals, especially the assessment and reassessment phases of treatments, despite some variations when compared to traditional methods. Moreover, in the case of image-based applications, we highlight the advantage that measurements can be taken with the assessor at a certain distance with respect to the patient’s position, which is an important function for assessments performed in pandemic times such as the outbreak of COVID-19.