基于人工智能的新算法用于自动计算青少年特发性脊柱侧凸患者的冠状面参数:对 100 张术前全脊 X 光片的验证研究

IF 2.6 3区 医学 Q2 CLINICAL NEUROLOGY Global Spine Journal Pub Date : 2024-07-01 Epub Date: 2023-01-28 DOI:10.1177/21925682231154543
Clara Berlin, Sonja Adomeit, Priyanka Grover, Marcel Dreischarf, Henry Halm, Oliver Dürr, Peter Obid
{"title":"基于人工智能的新算法用于自动计算青少年特发性脊柱侧凸患者的冠状面参数:对 100 张术前全脊 X 光片的验证研究","authors":"Clara Berlin, Sonja Adomeit, Priyanka Grover, Marcel Dreischarf, Henry Halm, Oliver Dürr, Peter Obid","doi":"10.1177/21925682231154543","DOIUrl":null,"url":null,"abstract":"<p><strong>Study design: </strong>Retrospective, mono-centric cohort research study.</p><p><strong>Objectives: </strong>The purpose of this study is to validate a novel artificial intelligence (AI)-based algorithm against human-generated ground truth for radiographic parameters of adolescent idiopathic scoliosis (AIS).</p><p><strong>Methods: </strong>An AI-algorithm was developed that is capable of detecting anatomical structures of interest (clavicles, cervical, thoracic, lumbar spine and sacrum) and calculate essential radiographic parameters in AP spine X-rays fully automatically. The evaluated parameters included T1-tilt, clavicle angle (CA), coronal balance (CB), lumbar modifier, and Cobb angles in the proximal thoracic (C-PT), thoracic, and thoracolumbar regions. Measurements from 2 experienced physicians on 100 preoperative AP full spine X-rays of AIS patients were used as ground truth and to evaluate inter-rater and intra-rater reliability. The agreement between human raters and AI was compared by means of single measure Intra-class Correlation Coefficients (ICC; absolute agreement; >.75 rated as excellent), mean error and additional statistical metrics.</p><p><strong>Results: </strong>The comparison between human raters resulted in excellent ICC values for intra- (range: .97-1) and inter-rater (.85-.99) reliability. The algorithm was able to determine all parameters in 100% of images with excellent ICC values (.78-.98). Consistently with the human raters, ICC values were typically smallest for C-PT (eg, rater 1A vs AI: .78, mean error: 4.7°) and largest for CB (.96, -.5 mm) as well as CA (.98, .2°).</p><p><strong>Conclusions: </strong>The AI-algorithm shows excellent reliability and agreement with human raters for coronal parameters in preoperative full spine images. The reliability and speed offered by the AI-algorithm could contribute to the efficient analysis of large datasets (eg, registry studies) and measurements in clinical practice.</p>","PeriodicalId":12680,"journal":{"name":"Global Spine Journal","volume":null,"pages":null},"PeriodicalIF":2.6000,"publicationDate":"2024-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11268300/pdf/","citationCount":"0","resultStr":"{\"title\":\"Novel AI-Based Algorithm for the Automated Computation of Coronal Parameters in Adolescent Idiopathic Scoliosis Patients: A Validation Study on 100 Preoperative Full Spine X-Rays.\",\"authors\":\"Clara Berlin, Sonja Adomeit, Priyanka Grover, Marcel Dreischarf, Henry Halm, Oliver Dürr, Peter Obid\",\"doi\":\"10.1177/21925682231154543\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p><strong>Study design: </strong>Retrospective, mono-centric cohort research study.</p><p><strong>Objectives: </strong>The purpose of this study is to validate a novel artificial intelligence (AI)-based algorithm against human-generated ground truth for radiographic parameters of adolescent idiopathic scoliosis (AIS).</p><p><strong>Methods: </strong>An AI-algorithm was developed that is capable of detecting anatomical structures of interest (clavicles, cervical, thoracic, lumbar spine and sacrum) and calculate essential radiographic parameters in AP spine X-rays fully automatically. The evaluated parameters included T1-tilt, clavicle angle (CA), coronal balance (CB), lumbar modifier, and Cobb angles in the proximal thoracic (C-PT), thoracic, and thoracolumbar regions. Measurements from 2 experienced physicians on 100 preoperative AP full spine X-rays of AIS patients were used as ground truth and to evaluate inter-rater and intra-rater reliability. The agreement between human raters and AI was compared by means of single measure Intra-class Correlation Coefficients (ICC; absolute agreement; >.75 rated as excellent), mean error and additional statistical metrics.</p><p><strong>Results: </strong>The comparison between human raters resulted in excellent ICC values for intra- (range: .97-1) and inter-rater (.85-.99) reliability. The algorithm was able to determine all parameters in 100% of images with excellent ICC values (.78-.98). Consistently with the human raters, ICC values were typically smallest for C-PT (eg, rater 1A vs AI: .78, mean error: 4.7°) and largest for CB (.96, -.5 mm) as well as CA (.98, .2°).</p><p><strong>Conclusions: </strong>The AI-algorithm shows excellent reliability and agreement with human raters for coronal parameters in preoperative full spine images. The reliability and speed offered by the AI-algorithm could contribute to the efficient analysis of large datasets (eg, registry studies) and measurements in clinical practice.</p>\",\"PeriodicalId\":12680,\"journal\":{\"name\":\"Global Spine Journal\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":2.6000,\"publicationDate\":\"2024-07-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11268300/pdf/\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Global Spine Journal\",\"FirstCategoryId\":\"3\",\"ListUrlMain\":\"https://doi.org/10.1177/21925682231154543\",\"RegionNum\":3,\"RegionCategory\":\"医学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"2023/1/28 0:00:00\",\"PubModel\":\"Epub\",\"JCR\":\"Q2\",\"JCRName\":\"CLINICAL NEUROLOGY\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Global Spine Journal","FirstCategoryId":"3","ListUrlMain":"https://doi.org/10.1177/21925682231154543","RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"2023/1/28 0:00:00","PubModel":"Epub","JCR":"Q2","JCRName":"CLINICAL NEUROLOGY","Score":null,"Total":0}
引用次数: 0

摘要

研究设计:回顾性、单中心队列研究:本研究的目的是验证一种基于人工智能(AI)的新型算法与人类生成的青少年特发性脊柱侧弯症(AIS)影像学参数的地面实况:开发的人工智能算法能够检测感兴趣的解剖结构(锁骨、颈椎、胸椎、腰椎和骶骨),并全自动计算AP脊柱X光片的基本放射学参数。评估的参数包括 T1 倾斜、锁骨角 (CA)、冠状平衡 (CB)、腰椎修正器以及近胸椎 (C-PT)、胸椎和胸腰椎区域的 Cobb 角。两名经验丰富的医生对 100 张 AIS 患者术前 AP 全脊柱 X 光片的测量结果被用作基本事实,并用于评估评分者之间和评分者内部的可靠性。通过单一测量的类内相关系数(ICC;绝对一致;>.75 为优秀)、平均误差和其他统计指标对人类评分员和人工智能之间的一致性进行了比较:结果:人类评分者之间的比较结果显示,评分者内部(范围:.97-1)和评分者之间(.85-.99)的 ICC 可靠性非常高。算法能够确定 100%图像的所有参数,ICC 值(.78-.98)极佳。与人类评分员一致的是,C-PT 的 ICC 值通常最小(例如,评分员 1A 与人工智能的比较:.78,平均误差:4.7°),CB(.96,-.5 毫米)和 CA(.98,.2°)的 ICC 值最大:结论:人工智能算法在术前全脊图像的冠状参数方面显示出极佳的可靠性,并与人类评分员的评分结果一致。人工智能算法的可靠性和速度有助于高效分析大型数据集(如登记研究)和临床实践中的测量。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
查看原文
分享 分享
微信好友 朋友圈 QQ好友 复制链接
本刊更多论文
Novel AI-Based Algorithm for the Automated Computation of Coronal Parameters in Adolescent Idiopathic Scoliosis Patients: A Validation Study on 100 Preoperative Full Spine X-Rays.

Study design: Retrospective, mono-centric cohort research study.

Objectives: The purpose of this study is to validate a novel artificial intelligence (AI)-based algorithm against human-generated ground truth for radiographic parameters of adolescent idiopathic scoliosis (AIS).

Methods: An AI-algorithm was developed that is capable of detecting anatomical structures of interest (clavicles, cervical, thoracic, lumbar spine and sacrum) and calculate essential radiographic parameters in AP spine X-rays fully automatically. The evaluated parameters included T1-tilt, clavicle angle (CA), coronal balance (CB), lumbar modifier, and Cobb angles in the proximal thoracic (C-PT), thoracic, and thoracolumbar regions. Measurements from 2 experienced physicians on 100 preoperative AP full spine X-rays of AIS patients were used as ground truth and to evaluate inter-rater and intra-rater reliability. The agreement between human raters and AI was compared by means of single measure Intra-class Correlation Coefficients (ICC; absolute agreement; >.75 rated as excellent), mean error and additional statistical metrics.

Results: The comparison between human raters resulted in excellent ICC values for intra- (range: .97-1) and inter-rater (.85-.99) reliability. The algorithm was able to determine all parameters in 100% of images with excellent ICC values (.78-.98). Consistently with the human raters, ICC values were typically smallest for C-PT (eg, rater 1A vs AI: .78, mean error: 4.7°) and largest for CB (.96, -.5 mm) as well as CA (.98, .2°).

Conclusions: The AI-algorithm shows excellent reliability and agreement with human raters for coronal parameters in preoperative full spine images. The reliability and speed offered by the AI-algorithm could contribute to the efficient analysis of large datasets (eg, registry studies) and measurements in clinical practice.

求助全文
通过发布文献求助,成功后即可免费获取论文全文。 去求助
来源期刊
Global Spine Journal
Global Spine Journal Medicine-Surgery
CiteScore
6.20
自引率
8.30%
发文量
278
审稿时长
8 weeks
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).
期刊最新文献
Prevalence and Clinical Impact of Coronal Malalignment Following Circumferential Minimally Invasive Surgery (CMIS) for Adult Spinal Deformity Correction. Current Applications and Future Implications of Artificial Intelligence in Spine Surgery and Research: A Narrative Review and Commentary. Surgical Specialty Outcome Differences for Major Spinal Procedures in Low-Acuity Patients. The Effect of Osteopenia and Osteoporosis on Screw Loosening in MIS-TLIF and Dynamic Stabilization. Learning Curve of Endoscopic Lumbar Discectomy - A Systematic Review and Meta-Analysis of Individual Participant and Aggregated Data.
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
现在去查看 取消
×
提示
确定
0
微信
客服QQ
Book学术公众号 扫码关注我们
反馈
×
意见反馈
请填写您的意见或建议
请填写您的手机或邮箱
已复制链接
已复制链接
快去分享给好友吧!
我知道了
×
扫码分享
扫码分享
Book学术官方微信
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术
文献互助 智能选刊 最新文献 互助须知 联系我们:info@booksci.cn
Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。
Copyright © 2023 Book学术 All rights reserved.
ghs 京公网安备 11010802042870号 京ICP备2023020795号-1