{"title":"基于多视角语义分割的人工智能测量青少年特发性脊柱侧凸的术前X光片","authors":"Yulei Dong, Jiahao Li, Shanqi Huang, Ling Wu, Hong Zhao, Yu Zhao","doi":"10.1177/21925682241270036","DOIUrl":null,"url":null,"abstract":"<p><strong>Study design: </strong>Cross-sectional study.</p><p><strong>Objectives: </strong>Imaging classification of adolescent idiopathic scoliosis (AIS) is directly related to the surgical strategy, but the artificial classification is complex and depends on doctors' experience. This study investigated deep learning-based automated classification methods (DL group) for AIS and validated the consistency of machine classification and manual classification (M group).</p><p><strong>Methods: </strong>A total of 506 cases (81 males and 425 females) and 1812 AIS full spine images in the anteroposterior (AP), lateral (LAT), left bending (LB) and right bending (RB) positions were retrospectively used for training. The mean age was 13.6 ± 1.8. The mean maximum Cobb angle was 46.8 ± 12.0. U-Net semantic segmentation neural network technology and deep learning methods were used to automatically segment and establish the alignment relationship between multiple views of the spine, and to extract spinal features such as the Cobb angle. The type of each test case was automatically calculated according to Lenke's rule. An additional 107 cases of adolescent idiopathic scoliosis imaging were prospectively used for testing. The consistency of the DL group and M group was compared.</p><p><strong>Results: </strong>Automatic vertebral body segmentation and recognition, multi-view alignment of the spine and automatic Cobb angle measurement were implemented. Compare to the M group, the consistency of the DL group was significantly higher in 3 aspects: type of lateral convexity (0.989 vs 0.566), lumbar curvature modifier (0.932 vs 0.738), and sagittal plane modifier (0.987 vs 0.522).</p><p><strong>Conclusions: </strong>Deep learning enables automated Cobb angle measurement and automated Lenke classification of idiopathic scoliosis whole spine radiographs with higher consistency than manual measurement classification.</p>","PeriodicalId":12680,"journal":{"name":"Global Spine Journal","volume":null,"pages":null},"PeriodicalIF":2.6000,"publicationDate":"2024-08-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Artificial Intelligence Measurement of Preoperative Radiographs in Adolescent Idiopathic Scoliosis Based on Multiple-View Semantic Segmentation.\",\"authors\":\"Yulei Dong, Jiahao Li, Shanqi Huang, Ling Wu, Hong Zhao, Yu Zhao\",\"doi\":\"10.1177/21925682241270036\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p><strong>Study design: </strong>Cross-sectional study.</p><p><strong>Objectives: </strong>Imaging classification of adolescent idiopathic scoliosis (AIS) is directly related to the surgical strategy, but the artificial classification is complex and depends on doctors' experience. This study investigated deep learning-based automated classification methods (DL group) for AIS and validated the consistency of machine classification and manual classification (M group).</p><p><strong>Methods: </strong>A total of 506 cases (81 males and 425 females) and 1812 AIS full spine images in the anteroposterior (AP), lateral (LAT), left bending (LB) and right bending (RB) positions were retrospectively used for training. The mean age was 13.6 ± 1.8. The mean maximum Cobb angle was 46.8 ± 12.0. U-Net semantic segmentation neural network technology and deep learning methods were used to automatically segment and establish the alignment relationship between multiple views of the spine, and to extract spinal features such as the Cobb angle. The type of each test case was automatically calculated according to Lenke's rule. An additional 107 cases of adolescent idiopathic scoliosis imaging were prospectively used for testing. The consistency of the DL group and M group was compared.</p><p><strong>Results: </strong>Automatic vertebral body segmentation and recognition, multi-view alignment of the spine and automatic Cobb angle measurement were implemented. 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引用次数: 0
摘要
研究设计横断面研究:青少年特发性脊柱侧凸(AIS)的影像学分类直接关系到手术策略,但人工分类非常复杂,且依赖于医生的经验。本研究探讨了基于深度学习的AIS自动分类方法(DL组),并验证了机器分类与人工分类(M组)的一致性:共有 506 个病例(81 名男性和 425 名女性)和 1812 张 AIS 全脊柱图像在前胸位(AP)、侧位(LAT)、左弯位(LB)和右弯位(RB)进行了回顾性训练。平均年龄为 13.6±1.8 岁。平均最大 Cobb 角度为 46.8 ± 12.0。U-Net 语义分割神经网络技术和深度学习方法被用于自动分割和建立脊柱多个视图之间的对齐关系,并提取脊柱特征,如 Cobb 角。每个测试病例的类型都是根据伦克法则自动计算得出的。另有 107 例青少年特发性脊柱侧凸成像病例被用于前瞻性测试。比较了 DL 组和 M 组的一致性:结果:实现了椎体自动分割和识别、脊柱多视角对齐和 Cobb 角自动测量。与 M 组相比,DL 组在 3 个方面的一致性明显更高:侧凸类型(0.989 vs 0.566)、腰椎弯曲度修饰符(0.932 vs 0.738)和矢状面修饰符(0.987 vs 0.522):深度学习可实现特发性脊柱侧弯全脊柱X光片的自动Cobb角测量和自动Lenke分类,其一致性高于人工测量分类。
Artificial Intelligence Measurement of Preoperative Radiographs in Adolescent Idiopathic Scoliosis Based on Multiple-View Semantic Segmentation.
Study design: Cross-sectional study.
Objectives: Imaging classification of adolescent idiopathic scoliosis (AIS) is directly related to the surgical strategy, but the artificial classification is complex and depends on doctors' experience. This study investigated deep learning-based automated classification methods (DL group) for AIS and validated the consistency of machine classification and manual classification (M group).
Methods: A total of 506 cases (81 males and 425 females) and 1812 AIS full spine images in the anteroposterior (AP), lateral (LAT), left bending (LB) and right bending (RB) positions were retrospectively used for training. The mean age was 13.6 ± 1.8. The mean maximum Cobb angle was 46.8 ± 12.0. U-Net semantic segmentation neural network technology and deep learning methods were used to automatically segment and establish the alignment relationship between multiple views of the spine, and to extract spinal features such as the Cobb angle. The type of each test case was automatically calculated according to Lenke's rule. An additional 107 cases of adolescent idiopathic scoliosis imaging were prospectively used for testing. The consistency of the DL group and M group was compared.
Results: Automatic vertebral body segmentation and recognition, multi-view alignment of the spine and automatic Cobb angle measurement were implemented. Compare to the M group, the consistency of the DL group was significantly higher in 3 aspects: type of lateral convexity (0.989 vs 0.566), lumbar curvature modifier (0.932 vs 0.738), and sagittal plane modifier (0.987 vs 0.522).
Conclusions: Deep learning enables automated Cobb angle measurement and automated Lenke classification of idiopathic scoliosis whole spine radiographs with higher consistency than manual measurement classification.
期刊介绍:
Global Spine Journal (GSJ) is the official scientific publication of AOSpine. A peer-reviewed, open access journal, devoted to the study and treatment of spinal disorders, including diagnosis, operative and non-operative treatment options, surgical techniques, and emerging research and clinical developments.GSJ is indexed in PubMedCentral, SCOPUS, and Emerging Sources Citation Index (ESCI).