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":"11 ","pages":"1329085"},"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\":\"11 \",\"pages\":\"1329085\"},\"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}
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.
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.