Suppr超能文献

青少年特发性脊柱侧弯中视觉身体形象分类的验证:一项回顾性研究。

Validation of the visual body image classification in adolescent idiopathic scoliosis: a retrospective study.

作者信息

Kim Han Sol, Jeong Jae Yoon, Cho Yoon Jae, Goh Tae Sik, Lee Jung Sub

机构信息

Department of Orthopaedic Surgery, Biomedical Research Institute, Pusan National University Hospital, Pusan National University, Busan, Korea.

出版信息

Asian Spine J. 2024 Dec;18(6):829-835. doi: 10.31616/asj.2024.0201. Epub 2024 Dec 10.

Abstract

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.

摘要

研究设计

前瞻性研究。

目的

为诊断脊柱侧弯,通常需要前往医院进行X光检查。在此类情况下,脊柱侧弯患儿会受到辐射,这可能会使其健康面临风险。因此,我们试图确定基于通过摄影获得的可视身体图像的分类方法是否可用于诊断脊柱侧弯。

文献综述

脊柱侧弯可通过X光片进行诊断并分为多种类型。然而,尚无研究尝试基于可视身体图像对脊柱侧弯进行分类。

方法

从2019年1月1日至2022年12月31日,招募了136例新诊断为青少年特发性脊柱侧弯的患者以及来自本机构的124名健康对照者。本研究根据对身体位置关系的可视确认将身体图像分为五种类型。通过根据kappa值计算该分类方法在观察者内部和观察者之间的敏感性、特异性和可重复性,确定了该分类方法的准确性。

结果

总体而言,对访问韩国釜山国立大学医院的136例患者和124名对照者进行了拍照,并通过获取背部图像和X光片进行了比较。尽管准确性程度因可视身体图像类型而异,但该分类方法的敏感性和特异性显示出令人满意至良好的准确度。该分类方法表现出良好的观察者内信度(κ=0.855)和中等的观察者间信度(κ=0.751)。

结论

我们的分类方法显示出高度的敏感性和特异性(敏感性98.1%,特异性98.9%,准确度98.4%),同时具有高可重复性和易于获取性。基于我们的研究结果,我们认为我们的分类方法可用于脊柱侧弯筛查。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/cc87/11711174/619f3d7fe318/asj-2024-0201f1.jpg

文献AI研究员

20分钟写一篇综述,助力文献阅读效率提升50倍。

立即体验

用中文搜PubMed

大模型驱动的PubMed中文搜索引擎

马上搜索

文档翻译

学术文献翻译模型,支持多种主流文档格式。

立即体验