Abstract：
OBJECTIVE: The accurate measurement of Cobb angles is crucial for the effective clinical management of patients with adolescent idiopathic scoliosis (AIS). The Lenke classification system plays a pivotal role in determining the appropriate fusion levels for treatment planning. However, the presence of interobserver variability and time-intensive procedures presents challenges for clinicians. The purpose of this study is to compare the measurement accuracy of our developed artificial intelligence measurement system for Cobb angles and Lenke classification in AIS patients with manual measurements to validate its feasibility.
METHODS: An artificial intelligence (AI) system measured the Cobb angle of AIS patients using convolutional neural networks, which identified the vertebral boundaries and sequences, recognized the upper and lower end vertebras, and estimated the Cobb angles of the proximal thoracic, main thoracic, and thoracolumbar/lumbar curves sequentially. Accordingly, the Lenke classifications of scoliosis were divided by oscillogram and defined by the AI system. Furthermore, a man-machine comparison (n = 300) was conducted for senior spine surgeons (n = 2), junior spine surgeons (n = 2), and the AI system for the image measurements of proximal thoracic (PT), main thoracic (MT), thoracolumbar/lumbar (TL/L), thoracic sagittal profile T5-T12, bending views PT, bending views MT, bending views TL/L, the Lenke classification system, the lumbar modifier, and sagittal thoracic alignment.
RESULTS: In the AI system, the calculation time for each patient\'s data was 0.2 s, while the measurement time for each surgeon was 23.6 min. The AI system showed high accuracy in the recognition of the Lenke classification and had high reliability compared to senior doctors (ICC 0.962).
CONCLUSIONS: The AI system has high reliability for the Lenke classification and is a potential auxiliary tool for spinal surgeons.
METHODS: An artificial intelligence (AI) system measured the Cobb angle of AIS patients using convolutional neural networks, which identified the vertebral boundaries and sequences, recognized the upper and lower end vertebras, and estimated the Cobb angles of the proximal thoracic, main thoracic, and thoracolumbar/lumbar curves sequentially. Accordingly, the Lenke classifications of scoliosis were divided by oscillogram and defined by the AI system. Furthermore, a man-machine comparison (n = 300) was conducted for senior spine surgeons (n = 2), junior spine surgeons (n = 2), and the AI system for the image measurements of proximal thoracic (PT), main thoracic (MT), thoracolumbar/lumbar (TL/L), thoracic sagittal profile T5-T12, bending views PT, bending views MT, bending views TL/L, the Lenke classification system, the lumbar modifier, and sagittal thoracic alignment.
RESULTS: In the AI system, the calculation time for each patient\'s data was 0.2 s, while the measurement time for each surgeon was 23.6 min. The AI system showed high accuracy in the recognition of the Lenke classification and had high reliability compared to senior doctors (ICC 0.962).
CONCLUSIONS: The AI system has high reliability for the Lenke classification and is a potential auxiliary tool for spinal surgeons.
摘要：
目的：Cobb角的准确测量对于青少年特发性脊柱侧凸（AIS）患者的有效临床治疗至关重要。Lenke分类系统在确定治疗计划的适当融合水平中起着关键作用。然而,观察者间变异性和时间密集型程序的存在给临床医生带来了挑战.这项研究的目的是将我们开发的用于AIS患者Cobb角和Lenke分类的人工智能测量系统的测量精度与手动测量进行比较，以验证其可行性。
方法：一个人工智能（AI）系统使用卷积神经网络测量了AIS患者的Cobb角，确定了椎骨的边界和序列，识别出上端和下端椎骨，估计了胸膜近端的Cobb角,主胸,和胸腰椎/腰椎曲线顺序。因此，脊柱侧凸的Lenke分类通过示波图进行划分，并通过AI系统进行定义。此外，对高级脊柱外科医生(n=2)进行了人机比较(n=300)，初级脊柱外科医生(n=2)，和用于近端胸部（PT）图像测量的AI系统，主胸（MT），胸腰椎/腰椎(TL/L),胸廓矢状面T5-T12，弯曲视图PT，弯曲视图MT,弯曲视图TL/L,伦克分类系统，腰椎修改器，和矢状胸部对齐。
结果：在AI系统中，每个患者数据的计算时间为0.2s，而每位外科医生的测量时间为23.6min。与高级医生（ICC0.962）相比，AI系统对Lenke分类的识别具有很高的准确性和可靠性。
结论：AI系统对Lenke分类具有很高的可靠性，是脊柱外科医生的潜在辅助工具。
方法：一个人工智能（AI）系统使用卷积神经网络测量了AIS患者的Cobb角，确定了椎骨的边界和序列，识别出上端和下端椎骨，估计了胸膜近端的Cobb角,主胸,和胸腰椎/腰椎曲线顺序。因此，脊柱侧凸的Lenke分类通过示波图进行划分，并通过AI系统进行定义。此外，对高级脊柱外科医生(n=2)进行了人机比较(n=300)，初级脊柱外科医生(n=2)，和用于近端胸部（PT）图像测量的AI系统，主胸（MT），胸腰椎/腰椎(TL/L),胸廓矢状面T5-T12，弯曲视图PT，弯曲视图MT,弯曲视图TL/L,伦克分类系统，腰椎修改器，和矢状胸部对齐。
结果：在AI系统中，每个患者数据的计算时间为0.2s，而每位外科医生的测量时间为23.6min。与高级医生（ICC0.962）相比，AI系统对Lenke分类的识别具有很高的准确性和可靠性。
结论：AI系统对Lenke分类具有很高的可靠性，是脊柱外科医生的潜在辅助工具。
