## 3 Results
### Other Variability Used in This Study
From the reliability results where the ICC value achieved more than 0.9, it can be said that their methods had excellent reliability levels. While other authors abstained from drawing comparisons between their approach and alternative methods, they solely presented the accomplished measurement outcomes attained through their devised methodology. \begin{table}
\begin{tabular}{c c c c} \hline
**Authors** & **Protocol/Design of Study** & **Plane/View** & **Outcome Measures** \\ \hline \multicolumn{4}{c}{Mean pelvic incidence 54.3\({}^{\circ}\) (\(\pm\)14)} \\  & & Rotation of axial pelvic ranged between 2\({}^{\circ}\) and 6\({}^{\circ}\) \\  & & Spinal parameters (Mean \(\pm\) standard deviation) \\  & & Cobb angle of main thoracic (61.2 \(\pm\) 13\({}^{\circ}\)) \\  & Patients in standing position for less than 15 min to obtain their specific 3D spinal & Aval (19.9 \(\pm\) 7\({}^{\circ}\)) \\ [PERSON] et al. [24] & reconstruction with the EOS system. & Frontal & Torsion index 15.8 \(\pm\) 6 \\  & Thoracic parameters were computed, and spinal and pelvic parameters were & Sagittal & Proximal thoracic Cobb angle (30 \(\pm\) 11\({}^{\circ}\)) \\  & measured during the reconstructions. & Sagittal & Lumbar Cobb angle (42 \(\pm\) 11\({}^{\circ}\)) \\  & & T4-T12 kyrobosis (18 \(\pm\) 13\({}^{\circ}\)) \\  & & L1-S1 lordosis (53.7 \(\pm\) 14\({}^{\circ}\)) \\  & & Thoracic parameters (Mean \(\pm\) standard deviation) \\  & & Thoracic volume (5056 mm\({}^{3}\)\(\pm\) 869) \\  & & SPIa (13.3\% \(\pm\) 1.7) \\  & & SPIm (8.7\% \(\pm\) 1.2) \\ \hline \multicolumn{4}{c}{Accuracy for unsupervised classifier ML} \\  & Patients maintain a static stance in an & for full set \\  & upright posture at a predetermined & features achieved 61.7\% and minimal set \\  & distance from camera for 6 s. & features achieved 72.2\%. \\  & Data acquisition (sample of pictures) & Frontal & Accuracy for supervised classifier ML for full set \\  & obtained with & Symmetric 4D system. Then, & full set \\  & data underwent cleaning and & & features achieved 87.5\% and 86.3\%. While \\  & normalization before proceeding to & & accuracy of minimal set features achieved 83.7\% and 85.5\%. \\  & machine learning procedure. & & \\ \hline \multicolumn{4}{c}{Subjects need to stand naturally for data} \\  & acquisition conducted using multiple & & \\  & cameras. The patient’s back was captured & & Performance of DLAs was measured with \\  & disrobing above hip. & & accuracy, sensitivity, and specificity, which \\  & Data collected from 3240 patients with & & the results are shown as below: - \\  & images of labeled back and entire spine & & Algorithm 1: \\  & standing posterior–anterior X-ray images & & Accuracy = 75\%, Sensitivity = 80.67\%, \\ [PERSON] et al. [18] & or ultrasound images, which were used for & & Specificity = 58\% \\  & training validation dataset. For & & Algorithm 2: \\  & external validation, 400 images were used for the process. Both training and external & & Accuracy = 87\%, Sensitivity = 84\%, \\  & validation were performed for three & & Specificity = 90\% \\  & algorithms, which are cases with curve & & Algorithm 3: \\  & \(\geq\)10\({}^{\circ}\), cases with curve \(\geq\)20\({}^{\circ}\), and curve & & Accuracy = 55\% \\  & severity grading. & & \\ \hline \end{tabular}
\end{table} 
Table 4: Design Study and Outcome Measures from reviewed articles.