## 4 Discussion
This requires the utilization of advanced instruments or tools that are not familiar to medical practitioners and may not stimulate them to use the instruments due to the distinct and complicated procedures [40]. The most current research focuses on scoliosis diagnosis using artificial intelligence and particularly image processing approaches. Image processing involves many steps, including acquisition, enhancement, restoration, recognition, and segmentation, and the steps may engage in the process according to the desired needs, and it is the process of converting an image into a digital format and then executing various operations to extract relevant information. Studies that apply artificial intelligence (AI) [18; 19; 25; 27; 32; 33] to diagnose scoliosis can improve the accuracy and efficiency of the diagnosis outcomes. AI in medicine can analyze complicated algorithms and self-learning that can work in a manner comparable to human brain, and it can have several subfields, such as machine learning (ML), deep learning (DL), and computer vision [41]. Three studies [25; 27; 32] in this review employed machine learning where it consisted of pattern recognition and analysis that can improve with experience from provided datasets. This can be supported with another study [7; 42] in which a machine learning algorithm, such as a regression linear and support vector machine, has successfully detected scoliosis in the early stage. Study [18] diagnosed scoliosis using deep learning as an assessment mechanism, and the DL algorithms are Faster-RCNN and Resnet, which are commonly use in the medical field [43]. However, this study only classifies the scoliosis according curve severity grading and did not give specific measurements on the abnormality of the spine curve; thus, this is not quite suitable and does not help health professionals to plan treatment since they need specific data regarding the diagnosis. According to numerous studies [43; 44; 45; 46; 47], deep learning may be a strong technique with a high reputation in biomedical segmentation; however, there are several limitations in terms of execution and process resources in this vertebral segmentation study. Computer vision is a process through which a computer learns and comprehends information and understanding from a sequence of images or videos [41], and refs [21; 22; 29; 30] employ this approach. This is the highest level of difficulty since it involves building an autonomous system that can detect, diagnose, and process the provided data and then analyze them accurately, which then portrays the analyzed information to the user. Thus, this gives much help to clinicians and can facilitate their efficiency in diagnosing scoliosis. These approaches have given ease to humans and can reduce human error, especially in precise medical diagnosis, yet the challenging journey to successfully utilized these in scoliosis assessment needs to be taken into consideration. It was revealed that diagnostic assessment studies involved with artificial intelligence had average to high accuracy performance, and regarding one case of low accuracy, we speculated that the specific DL algorithm was not suitable for the dataset images, while studies involving image processing presented that most of them had good to strong correlation when compared to the manual Cobb angle, which proves the validity of their methods, and two of these studies showed excellent reliability assessment. For studies on the computer-aided systems approach, the developed systems had lower error differences between using the system and the manual method, a strong correlation coefficient, and could classify scoliosis by its severity. Overall, in the context of evaluating scoliosis deformity that can be effectively implemented in a clinical setting, it is advisable to employ X-ray as the preferred imaging modality, specifically utilizing two-dimensional frontal and sagittal plane views to capture the spine images.