Tingsheng Lu, Chunshan Luo, Shudan Yao, Xingwei Pu, Qiling Chen, Minglu Yang, Lu Chen, Lihang Wang
Department of Spine Surgery, Beijing Jishuitan Hospital Guizhou Hospital, Guiyang, China.
Orthop Surg. 2024 Aug;16(8):2040-2051. doi: 10.1111/os.14144. Epub 2024 Jul 3.
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.
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.
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).
The AI system has high reliability for the Lenke classification and is a potential auxiliary tool for spinal surgeons.
准确测量Cobb角对于青少年特发性脊柱侧凸(AIS)患者的有效临床管理至关重要。Lenke分类系统在确定治疗计划的合适融合水平方面起着关键作用。然而,观察者间的差异以及耗时的操作给临床医生带来了挑战。本研究的目的是比较我们开发的人工智能测量系统与手动测量在AIS患者中测量Cobb角和Lenke分类的准确性,以验证其可行性。
一个人工智能(AI)系统使用卷积神经网络测量AIS患者的Cobb角,该网络识别椎体边界和序列,识别上下终椎,并依次估计近端胸椎、主胸椎和胸腰段/腰段曲线的Cobb角。相应地,脊柱侧凸的Lenke分类由波形图划分并由AI系统定义。此外,对资深脊柱外科医生(n = 2)、初级脊柱外科医生(n = 2)和AI系统进行了人机比较(n = 300),以测量近端胸椎(PT)、主胸椎(MT)、胸腰段/腰段(TL/L)、胸椎矢状面T5 - T12、PT弯曲位、MT弯曲位、TL/L弯曲位、Lenke分类系统、腰椎修正因子和胸椎矢状面排列的图像。
在AI系统中,每个患者数据的计算时间为0.2秒,而每个外科医生的测量时间为23.6分钟。AI系统在Lenke分类识别方面显示出高准确性,与资深医生相比具有高可靠性(ICC 0.962)。
AI系统在Lenke分类方面具有高可靠性,是脊柱外科医生的潜在辅助工具。