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Go to Editorial ManagerSpinal alignment examination procedures are frequently employed to assess spinal deformities. The spine plays a crucial role in maintaining the biomechanical functionality of the skeletal system. It protects the spinal cord and facilitates movement, among other vital functions. Various methods, including radiography and non-invasive techniques such as goniometer, inclinometer and kyphometer, have been employed to assess spine alignment qualitatively. Nevertheless, these methods are characterized by a high radiation dose and require significant time. Consequently, this study aimed to develop and create a portable, user-friendly, radiation-free computer-assisted electromechanical device to assess spinal deformities. This device is designed to evaluate sagittal spinal alignment by estimating the angle between two vertebrae for the segmental and global thoracic and lumbar regions, and the length of the spine. This study highlighted the importance of the method in evaluating spinal alignment. The MPU-6050 sensor was employed to record the angle between the two vertebrae, while the rotary encoder was utilized to measure the length of the spine. Subsequently, the data was transmitted to a computer over a Bluetooth module connection, following processing by the Arduino Nano microcontroller. The proposed system was employed on five healthy adult subjects to evaluate their standing posture in the sagittal plane, namely in the upright, flexion, and extension positions. The resulting parameters that define spinal alignment are provided. The suggested system offers the benefits of simplicity, portability, and cost-effectiveness, allowing for rapid and accurate assessment of sagittal spinal alignment. It enables quick clinical assessment and provides few health risks to the patient, leading to correct diagnosis.
The standardization and preprocessing decisions determine the performance, reproducibility and generalizability of Artificial Intelligence (AI) models in cervical spine Magnetic Resonance Imaging (MRI). In this research, the author suggests a six-step standardized preprocessing pipeline that can help with the reliable analysis of heterogeneous MRI data using AI. It is obtained by the pipeline based on recurrent methodological patterns found in the literature, focusing on the operations reported to stabilize image intensity distribution and reduce unwanted sources of variability. A systematic literature review was screened on 2,882 records, and 43 articles were included per the inclusion criteria. Due to the large variability of imaging procedures, preprocessing plans, and reported outcomes metrics, no quantitative meta-analysis was performed and a qualitative synthesis was done. Basic methods such as segmentation, denoising, intensity normalization, augmentation, bias-field correction were consistently reported to improve AI performance. Studies evaluating tasks such as normalization and automated segmentation reported accuracies of 94% to 99.95%. Moreover, less-specific harmonization methods like Combatting Batch Effects (ComBat), z-score normalization and histogram matching, and Generative Adversarial Network (GAN)-based ones were also often attributed with lower scanner- and site-based variations. Regardless of these improvements, the AI workflow remains unstable because of the irregularity of predefined parameters, unequal standards of acquisition, and lack of methodological reporting. In efforts to deal with such challenges, the current work proposes a methodical six-stage preprocessing model that consists of quality control, noise removal, intensity normalization, anatomical segmentation, harmonization and validation. The suggested workflow model offers a practical and transparent basis of clinically translatable AI usage in cervical spine MRI based on conventional mathematical equations and clear documentation.
Kids carrying heavy loads as a part of everyday activity may be related to bend their trunks forward to maintain body posture and balance while walking. This study was to determine a correlation between the weight of a child's backpack, their body weight, and certain features of their body posture. The study group consisted of 6 children, in age of primary school. The anthropometry (age, length, weight) were taken for each volunteers. A school backpack was specially built for the present study. Walking gait was filmed in three cases: (zero kg, 3 kg and 6 kg) backpack.Posture was analyzed by using (Kenova and MATLAB) computer programs.The results show that the forward inclination of the trunk increases when the load and the walking distance are increased, this forward inclination segment may impose greater stress over the vertebral column (ligaments and intervertebral disks) and increase the risk of back problems.Spine and back health may be adversely affected by load carriage and it may be important to use spinal curvature as a measure of posture for load carriage. This study shows that the backpack load cause a lumbar asymmetry by 10 to 20 degree according to the load which has a significant amount of back pain in kids.