## 1 Introduction  
Normal spinal posture is essential for maintaining spine health and biomechanical function including longevity. However, changes in physiological spinal curvature occur through natural aging or pathological processes because of various causes [1]. Early functional changes gradually accelerate, leading to irreversible and structural spinal deformation. Functional or structural spinal deformation affects spinal posture balance, causing uneven height differences between the shoulders and pelvis, and changes the axis of normal weight distribution causing deformation that protrudes unilaterally from the coronary plane of the spine. This imbalance in weight distribution further increases the difference in the asymmetric height between the shoulder and pelvis, causing structural deformation and pain in the spinal curve where pressure is concentrated [2; 3]. The normal physiological spinal curvature can be restored through functional changes by removing or improving the factors that cause posture asymmetry. Maintaining spinal health is very important to detect posture imbalance before spinal deformation progresses irreversibly and results in structural spinal deformation. Specifically, early diagnosis of scoliosis should include not only structural scoliosis but also functional posture changes, and the normal posture should be restored by mitigating the cause of spinal deformation. Some parameters are related to scoliotic detection. The parameters can be extracted through radiographic assessment and screening. Generally, the Cobb angle, age, weight, spinal vertebral rotation angle, pelvic height difference (PHD), and shoulder height difference (SHD) have been used for scoliosis diagnosis and scoring [4; 5; 6; 7]. The presence, degree, and severity of scoliosis can be determined by identifying and calculating some characteristic features among various parameters. The presence, degree, or severity of scoliosis is basically evaluated by measuring the curvature of the spine through X-ray imaging [8]. However, other studies have been conducted to evaluate the structural deformity by various methods than direct use of X-ray imaging [9; 10]. Various predictive investigations of scoliosis have been reported in recent artificial intelligence (AI) research trends [11; 12; 13; 14]. [PERSON] et al. studied scoliosis prediction by using deep learning and calculating the Cobb angle (average error of 2.9\({}^{\circ}\)) using 800 X-ray images to replace manual Cobb angle measurement through radiographic assessment [11]. [PERSON] et al. conducted deep learning research related to the screening system using non-ionizing radiation. They developed a machine learning algorithm that can identify cases with a curve \(\geq\)20\({}^{\circ}\) using 3640 unclothed back images [14]. Adolescent idiopathic scoliosis (AIS) is the most common spinal disease in adolescents, with a worldwide prevalence of 0.5-5.2% [2; 15; 16]. The aforementioned studies used artificial intelligence to develop high levels of prediction accuracy and performance but could not explain the inference process of the predictive models. Thus, it is necessary to apply the explainable artificial intelligence (XAI) technique that can explain the inference of the results like a white box given the black box characteristics of the machine learning model [17]. Spinal deformity diagnosis requires X-ray imaging to identify or monitor the disorder. During this process, the patients are exposed to radiation [8]. However, mild scoliosis can be observed without the use of regular radiation, and moderate-to-severe scoliosis can be detected at an early stage, providing an opportunity for treatment and posture correction using the detection system of postural deformity utilizing non-ionizing radiation. In this process, the aforementioned method using artificial intelligence can be applied.