We would like to comment on “Validation of the visual body image classification in adolescent idiopathic scoliosis: a retrospective study [1].” The study described in this abstract investigates a unique method for using body imaging to identify scoliosis. This strategy might lessen the need for radiography, which is crucial for kids in particular. While this idea is intriguing, there are a few significant problems that require more research. The intrinsic vulnerability of visual body image assessment to observer bias is one of the main causes for concern. Classification methods that have been classified as having “good” intraobserver reliability and “moderate” interobserver reliability are typical of disorders like scoliosis, which frequently manifest as subtle or asymmetrical traits. Different observers do not consistently classify the same photos in the same way, according to the modest interobserver reliability (κ=0.751) [1], which suggests that the method’s practical use in clinical practice may be limited. Elaborating on the necessity of conducting validation studies with diverse observer cohorts, including observers with varying experience levels, could significantly enhance the reliability of this method. This approach would help address the limitation related to observer and approve the consistency of results cross different clinicians, leading to a more robust and standardized classification system [2].

Although the study’s comparison of visual and radiographic body images is one of its strongest points, the conclusion offers comprehensive details on the forms of scoliosis that were evaluated and how well the images were displayed to correspond with various curvatures. The ability to discern between the lumbar and thoracic spines is not mentioned. Future studies could provide additional light on whether this approach works better for some forms of scoliosis or if it works equally well for others. Furthermore, the possibility of false negatives or false positives in clinical practice is not addressed by these metrics alone, even in the case of positive outcomes in sensitivity and specificity. Patient outcomes may be significantly impacted by this. To evaluate its feasibility, a more thorough analysis of these figures would be necessary, taking into account situations in which scoliosis cannot be readily seen.

Incorporating artificial intelligence (AI) and machine learning techniques to support body image classification could be advantageous for future studies. Interobserver reliability may be improved and the categorization process standardized with the help of AI algorithms trained on large body image datasets. Additionally, using dynamic imaging methods, such three-dimensional scanning, offers a more comprehensive diagnostic capacity and could help identify scoliosis early on, when treatment is more successful. For example, AI-based model could be developed to automatically identify few features of body symmetry improving consistency in diagnosis and reduction human error [3].

To summarize, the concept of using body images to detect scoliosis is promising, but the approach’s reliability and accuracy need to be improved before it can progress from proof of concept to widespread clinical implementation. Future studies should focus on adapting the method for a broader range of scoliosis severity and demographic factors. Standardizing protocols for image capture and ensuring the AI tools are trained on diverse dataset would likely improve the robustness and applicability of the system. Future study should focus on adapting it to diverse patient populations and analyzing the technique’s effectiveness across different types of scoliosis. Use advanced AI techniques to increase categorization objectivity and consistency.