通过 X 光平片预测原生髋关节的髋臼形态:卷积神经网络模型与当前黄金标准的比较分析,以及对髋关节置换术的启示和影响。

IF 1.6 4区 医学 Q2 SURGERY Frontiers in Surgery Pub Date : 2024-10-15 eCollection Date: 2024-01-01 DOI:10.3389/fsurg.2024.1329085
Ata Jodeiri, Hadi Seyedarabi, Parmida Shahbazi, Fatemeh Shahbazi, Seyed Mohammad Mahdi Hashemi, Seyed Mohammad Javad Mortazavi, Seyyed Hossein Shafiei
{"title":"通过 X 光平片预测原生髋关节的髋臼形态:卷积神经网络模型与当前黄金标准的比较分析,以及对髋关节置换术的启示和影响。","authors":"Ata Jodeiri, Hadi Seyedarabi, Parmida Shahbazi, Fatemeh Shahbazi, Seyed Mohammad Mahdi Hashemi, Seyed Mohammad Javad Mortazavi, Seyyed Hossein Shafiei","doi":"10.3389/fsurg.2024.1329085","DOIUrl":null,"url":null,"abstract":"<p><strong>Introduction: </strong>This study presents the development and validation of a Deep Learning Convolutional Neural Network (CNN) model for estimating acetabular version (AV) from native hip plain radiographs.</p><p><strong>Methods: </strong>Utilizing a dataset comprising 300 participants with unrelated pelvic complaints, the CNN model was trained and evaluated against CT-Scans, considered the gold standard, using a 5-fold cross-validation.</p><p><strong>Results: </strong>Notably, the CNN model exhibited a robust performance, demonstrating a strong Pearson correlation with CT-Scans (right hip: <i>r</i> = 0.70, <i>p</i> < 0.001; left hip: <i>r</i> = 0.71, <i>p</i> < 0.001) and achieving a mean absolute error of 2.95°. Remarkably, over 83% of predictions yielded errors ≤5°, highlighting the model's high precision in AV estimation.</p><p><strong>Discussion: </strong>The model holds promise in preoperative planning for hip arthroplasty, potentially reducing complications like recurrent dislocation and component wear. Future directions include further refinement of the CNN model, with ongoing investigations aimed at enhancing preoperative planning potential and ensuring comprehensive assessment across diverse patient populations, particularly in diseased cases. Additionally, future research could explore the model's potential value in scenarios necessitating minimized ionizing radiation exposure, such as post-operative evaluations.</p>","PeriodicalId":12564,"journal":{"name":"Frontiers in Surgery","volume":null,"pages":null},"PeriodicalIF":1.6000,"publicationDate":"2024-10-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11518832/pdf/","citationCount":"0","resultStr":"{\"title\":\"Predicting acetabular version in native hip joints through plain x-ray radiographs: a comparative analysis of convolutional neural network model and the current gold standard, with insights and implications for hip arthroplasty.\",\"authors\":\"Ata Jodeiri, Hadi Seyedarabi, Parmida Shahbazi, Fatemeh Shahbazi, Seyed Mohammad Mahdi Hashemi, Seyed Mohammad Javad Mortazavi, Seyyed Hossein Shafiei\",\"doi\":\"10.3389/fsurg.2024.1329085\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p><strong>Introduction: </strong>This study presents the development and validation of a Deep Learning Convolutional Neural Network (CNN) model for estimating acetabular version (AV) from native hip plain radiographs.</p><p><strong>Methods: </strong>Utilizing a dataset comprising 300 participants with unrelated pelvic complaints, the CNN model was trained and evaluated against CT-Scans, considered the gold standard, using a 5-fold cross-validation.</p><p><strong>Results: </strong>Notably, the CNN model exhibited a robust performance, demonstrating a strong Pearson correlation with CT-Scans (right hip: <i>r</i> = 0.70, <i>p</i> < 0.001; left hip: <i>r</i> = 0.71, <i>p</i> < 0.001) and achieving a mean absolute error of 2.95°. Remarkably, over 83% of predictions yielded errors ≤5°, highlighting the model's high precision in AV estimation.</p><p><strong>Discussion: </strong>The model holds promise in preoperative planning for hip arthroplasty, potentially reducing complications like recurrent dislocation and component wear. Future directions include further refinement of the CNN model, with ongoing investigations aimed at enhancing preoperative planning potential and ensuring comprehensive assessment across diverse patient populations, particularly in diseased cases. Additionally, future research could explore the model's potential value in scenarios necessitating minimized ionizing radiation exposure, such as post-operative evaluations.</p>\",\"PeriodicalId\":12564,\"journal\":{\"name\":\"Frontiers in Surgery\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":1.6000,\"publicationDate\":\"2024-10-15\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11518832/pdf/\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Frontiers in Surgery\",\"FirstCategoryId\":\"3\",\"ListUrlMain\":\"https://doi.org/10.3389/fsurg.2024.1329085\",\"RegionNum\":4,\"RegionCategory\":\"医学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"2024/1/1 0:00:00\",\"PubModel\":\"eCollection\",\"JCR\":\"Q2\",\"JCRName\":\"SURGERY\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Frontiers in Surgery","FirstCategoryId":"3","ListUrlMain":"https://doi.org/10.3389/fsurg.2024.1329085","RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"2024/1/1 0:00:00","PubModel":"eCollection","JCR":"Q2","JCRName":"SURGERY","Score":null,"Total":0}
引用次数: 0

摘要

简介:本研究介绍了一种深度学习卷积神经网络(CNN)模型的开发和验证情况,该模型可用于从原生髋关节平片估算髋臼版本(AV):本研究介绍了深度学习卷积神经网络(CNN)模型的开发和验证情况,该模型用于根据髋关节原位平片估算髋臼版本(AV):方法:利用一个由 300 名骨盆无相关症状的参与者组成的数据集,采用 5 倍交叉验证的方法训练 CNN 模型,并对照被视为黄金标准的 CT 扫描进行评估:值得注意的是,CNN 模型表现出强劲的性能,与 CT 扫描显示出很强的皮尔逊相关性(右髋:r = 0.70,p < 0.001;左髋:r = 0.71,p < 0.001),平均绝对误差为 2.95°。值得注意的是,超过 83% 的预测结果误差小于 5°,这凸显了该模型在动静脉估计方面的高精度:讨论:该模型有望用于髋关节置换术的术前规划,减少复发性脱位和组件磨损等并发症。未来的发展方向包括进一步完善 CNN 模型,目前正在进行的研究旨在提高术前规划的潜力,确保对不同患者群体进行全面评估,尤其是在患病病例中。此外,未来的研究还可以探索该模型在需要尽量减少电离辐射暴露的情况下(如术后评估)的潜在价值。
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
Predicting acetabular version in native hip joints through plain x-ray radiographs: a comparative analysis of convolutional neural network model and the current gold standard, with insights and implications for hip arthroplasty.

Introduction: This study presents the development and validation of a Deep Learning Convolutional Neural Network (CNN) model for estimating acetabular version (AV) from native hip plain radiographs.

Methods: Utilizing a dataset comprising 300 participants with unrelated pelvic complaints, the CNN model was trained and evaluated against CT-Scans, considered the gold standard, using a 5-fold cross-validation.

Results: Notably, the CNN model exhibited a robust performance, demonstrating a strong Pearson correlation with CT-Scans (right hip: r = 0.70, p < 0.001; left hip: r = 0.71, p < 0.001) and achieving a mean absolute error of 2.95°. Remarkably, over 83% of predictions yielded errors ≤5°, highlighting the model's high precision in AV estimation.

Discussion: The model holds promise in preoperative planning for hip arthroplasty, potentially reducing complications like recurrent dislocation and component wear. Future directions include further refinement of the CNN model, with ongoing investigations aimed at enhancing preoperative planning potential and ensuring comprehensive assessment across diverse patient populations, particularly in diseased cases. Additionally, future research could explore the model's potential value in scenarios necessitating minimized ionizing radiation exposure, such as post-operative evaluations.

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
Frontiers in Surgery
Frontiers in Surgery Medicine-Surgery
CiteScore
1.90
自引率
11.10%
发文量
1872
审稿时长
12 weeks
期刊介绍: Evidence of surgical interventions go back to prehistoric times. Since then, the field of surgery has developed into a complex array of specialties and procedures, particularly with the advent of microsurgery, lasers and minimally invasive techniques. The advanced skills now required from surgeons has led to ever increasing specialization, though these still share important fundamental principles. Frontiers in Surgery is the umbrella journal representing the publication interests of all surgical specialties. It is divided into several “Specialty Sections” listed below. All these sections have their own Specialty Chief Editor, Editorial Board and homepage, but all articles carry the citation Frontiers in Surgery. Frontiers in Surgery calls upon medical professionals and scientists from all surgical specialties to publish their experimental and clinical studies in this journal. By assembling all surgical specialties, which nonetheless retain their independence, under the common umbrella of Frontiers in Surgery, a powerful publication venue is created. Since there is often overlap and common ground between the different surgical specialties, assembly of all surgical disciplines into a single journal will foster a collaborative dialogue amongst the surgical community. This means that publications, which are also of interest to other surgical specialties, will reach a wider audience and have greater impact. The aim of this multidisciplinary journal is to create a discussion and knowledge platform of advances and research findings in surgical practice today to continuously improve clinical management of patients and foster innovation in this field.
期刊最新文献
Coverage of large soft tissue defects of the lower limb and foot with superficial inferior epigastric artery flap. Robotic colorectal surgery in Latin America: a systematic review on surgical outcomes. Integrating 3D technology with the Sampaio classification for enhanced percutaneous nephrolithotomy in complex renal calculi treatment. Integrative prognostic modeling for stage III lung adenosquamous carcinoma post-tumor resection: machine learning insights and web-based implementation. Extrarenal renal cell carcinoma in the adrenal region: a case report.
×
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