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Published on: October 16, 2013
Validation of the visual body image classification in adolescent idiopathic scoliosis: a retrospective study
Han Sol Kim1, Jae Yoon Jeong1, Yoon Jae Cho1
1Department of Orthopaedic Surgery, Biomedical Research Institute, Pusan National University Hospital, Pusan National University, Busan, Korea.
Study Design:
A prospective study.
Purpose:
To diagnose scoliosis, a visit to the hospital for radiography is typically necessary. In such cases, children with scoliosis are exposed to radiation, which may place their health at risk. Therefore, we sought to determine whether a classification method based on visual body images obtained through photography can be used to diagnose scoliosis.
Overview Of Literature:
Scoliosis can be diagnosed and classified into various types using radiographs. However, no studies have attempted to classify scoliosis based on visual body images.
Methods:
From January 1, 2019 to December 31, 2022, 136 patients newly diagnosed with Adolescent idiopathic scoliosis and 124 healthy candidates from our institution were enrolled. This study classified body images into five types based on visual confirmation of the positional relationship of the body. The accuracy of this classification method was identified by calculating its sensitivity, specificity, and reproducibility of this classification method within and between observers according to kappa value.
Results:
Overall, 136 patients and 124 control subjects who visited the Pusan National University Hospital, Busan, Korea were photographed and compared by obtaining back images and X-ray radiographs. The sensitivity and specificity of the classification method showed a satisfactory-to-good degree of accuracy, although the degree varies depending on the visual body image type. The classification methods exhibited good intraobserver reliability (κ=0.855) and moderate interobserver reliability (κ=0.751).
Conclusions:
Our classification method showed a high degree of sensitivity and specificity (98.1% sensitivity, 98.9% specificity, and 98.4% accuracy) while exhibiting high reproducibility and ease of access. Based on our findings, we believe that our classification method can be used for scoliosis screening.
