Christof Hoffmann, Fatih Göksu, Isabella Klöpfer-Krämer, Julius Watrinet, Philipp Blum, Sven Hungerer, Steffen Schröter, Fabian Stuby, Peter Augat, Julian Fürmetz
{"title":"利用卷积神经网络进行下肢排列分析的准确性很高,但关节级指标需要改进。","authors":"Christof Hoffmann, Fatih Göksu, Isabella Klöpfer-Krämer, Julius Watrinet, Philipp Blum, Sven Hungerer, Steffen Schröter, Fabian Stuby, Peter Augat, Julian Fürmetz","doi":"10.1002/ksa.12481","DOIUrl":null,"url":null,"abstract":"<p><strong>Objective: </strong>Evaluation of long-leg standing radiographs (LSR) is a standardised procedure for analysis of primary or secondary deformities of the lower limbs. Deep-learning convolutional neural networks (CNN) offer the potential to enhance radiological measurement by increasing reproducibility and accuracy. This study aims to evaluate the measurement accuracy of an automated CNN-based planning tool (mediCAD® 7.0; mediCAD Hectec GmbH) of lower limb deformities.</p><p><strong>Methods: </strong>In a retrospective single-centre study, 164 pre- and postoperative bilateral LSRs with uni- or bilateral posttraumatic knee arthritis undergoing total knee arthroplasty (TKA) were enroled. Alignment parameters relevant to knee arthroplasty and deformity correction were analysed independently by two observers and a CNN. The intraclass correlation coefficient (ICC) was used to evaluate the accuracy between observers and the CNN, which was further evaluated using absolute deviations, limits of agreement (LoA) and root mean square error (RMSE).</p><p><strong>Results: </strong>CNN evaluation demonstrated high consistency in measuring leg length (ICC > 0.99) and overall lower limb alignment measures of mechanical tibio-femoral angle (mTFA) (ICC > 0.97; RMSE < 1.1°). The mean absolute difference between angular measurements were low for overall lower limb alignment (mTFA 0.49-0.61°) and high for specific joint angles (aMPFA 3.86-4.50°). Accuracy at specific joint angles like the mechanical proximal tibial angle (MPTA) and the mechanical lateral distal femur angle (mLDFA) varied between lower limbs with deformity, with and without TKA with greatest difference for TKA (ICC 0.22-0.85; RMSE 1.72-3.65°).</p><p><strong>Conclusion: </strong>Excellent accuracy was observed between manual and automated measurements for overall alignment and leg length, but joint-level metrics need further improvement especially in case of TKA similar to other existing algorithms. Despite the observed deviations, the time-efficient nature of the algorithm improves the efficiency of the preoperative planning process.</p><p><strong>Level of evidence: </strong>IV.</p>","PeriodicalId":17880,"journal":{"name":"Knee Surgery, Sports Traumatology, Arthroscopy","volume":" ","pages":""},"PeriodicalIF":3.3000,"publicationDate":"2024-09-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"High accuracy in lower limb alignment analysis using convolutional neural networks, with improvements needed for joint-level metrics.\",\"authors\":\"Christof Hoffmann, Fatih Göksu, Isabella Klöpfer-Krämer, Julius Watrinet, Philipp Blum, Sven Hungerer, Steffen Schröter, Fabian Stuby, Peter Augat, Julian Fürmetz\",\"doi\":\"10.1002/ksa.12481\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p><strong>Objective: </strong>Evaluation of long-leg standing radiographs (LSR) is a standardised procedure for analysis of primary or secondary deformities of the lower limbs. Deep-learning convolutional neural networks (CNN) offer the potential to enhance radiological measurement by increasing reproducibility and accuracy. This study aims to evaluate the measurement accuracy of an automated CNN-based planning tool (mediCAD® 7.0; mediCAD Hectec GmbH) of lower limb deformities.</p><p><strong>Methods: </strong>In a retrospective single-centre study, 164 pre- and postoperative bilateral LSRs with uni- or bilateral posttraumatic knee arthritis undergoing total knee arthroplasty (TKA) were enroled. Alignment parameters relevant to knee arthroplasty and deformity correction were analysed independently by two observers and a CNN. The intraclass correlation coefficient (ICC) was used to evaluate the accuracy between observers and the CNN, which was further evaluated using absolute deviations, limits of agreement (LoA) and root mean square error (RMSE).</p><p><strong>Results: </strong>CNN evaluation demonstrated high consistency in measuring leg length (ICC > 0.99) and overall lower limb alignment measures of mechanical tibio-femoral angle (mTFA) (ICC > 0.97; RMSE < 1.1°). The mean absolute difference between angular measurements were low for overall lower limb alignment (mTFA 0.49-0.61°) and high for specific joint angles (aMPFA 3.86-4.50°). Accuracy at specific joint angles like the mechanical proximal tibial angle (MPTA) and the mechanical lateral distal femur angle (mLDFA) varied between lower limbs with deformity, with and without TKA with greatest difference for TKA (ICC 0.22-0.85; RMSE 1.72-3.65°).</p><p><strong>Conclusion: </strong>Excellent accuracy was observed between manual and automated measurements for overall alignment and leg length, but joint-level metrics need further improvement especially in case of TKA similar to other existing algorithms. Despite the observed deviations, the time-efficient nature of the algorithm improves the efficiency of the preoperative planning process.</p><p><strong>Level of evidence: </strong>IV.</p>\",\"PeriodicalId\":17880,\"journal\":{\"name\":\"Knee Surgery, Sports Traumatology, Arthroscopy\",\"volume\":\" \",\"pages\":\"\"},\"PeriodicalIF\":3.3000,\"publicationDate\":\"2024-09-22\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Knee Surgery, Sports Traumatology, Arthroscopy\",\"FirstCategoryId\":\"3\",\"ListUrlMain\":\"https://doi.org/10.1002/ksa.12481\",\"RegionNum\":2,\"RegionCategory\":\"医学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q1\",\"JCRName\":\"ORTHOPEDICS\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Knee Surgery, Sports Traumatology, Arthroscopy","FirstCategoryId":"3","ListUrlMain":"https://doi.org/10.1002/ksa.12481","RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"ORTHOPEDICS","Score":null,"Total":0}
High accuracy in lower limb alignment analysis using convolutional neural networks, with improvements needed for joint-level metrics.
Objective: Evaluation of long-leg standing radiographs (LSR) is a standardised procedure for analysis of primary or secondary deformities of the lower limbs. Deep-learning convolutional neural networks (CNN) offer the potential to enhance radiological measurement by increasing reproducibility and accuracy. This study aims to evaluate the measurement accuracy of an automated CNN-based planning tool (mediCAD® 7.0; mediCAD Hectec GmbH) of lower limb deformities.
Methods: In a retrospective single-centre study, 164 pre- and postoperative bilateral LSRs with uni- or bilateral posttraumatic knee arthritis undergoing total knee arthroplasty (TKA) were enroled. Alignment parameters relevant to knee arthroplasty and deformity correction were analysed independently by two observers and a CNN. The intraclass correlation coefficient (ICC) was used to evaluate the accuracy between observers and the CNN, which was further evaluated using absolute deviations, limits of agreement (LoA) and root mean square error (RMSE).
Results: CNN evaluation demonstrated high consistency in measuring leg length (ICC > 0.99) and overall lower limb alignment measures of mechanical tibio-femoral angle (mTFA) (ICC > 0.97; RMSE < 1.1°). The mean absolute difference between angular measurements were low for overall lower limb alignment (mTFA 0.49-0.61°) and high for specific joint angles (aMPFA 3.86-4.50°). Accuracy at specific joint angles like the mechanical proximal tibial angle (MPTA) and the mechanical lateral distal femur angle (mLDFA) varied between lower limbs with deformity, with and without TKA with greatest difference for TKA (ICC 0.22-0.85; RMSE 1.72-3.65°).
Conclusion: Excellent accuracy was observed between manual and automated measurements for overall alignment and leg length, but joint-level metrics need further improvement especially in case of TKA similar to other existing algorithms. Despite the observed deviations, the time-efficient nature of the algorithm improves the efficiency of the preoperative planning process.
期刊介绍:
Few other areas of orthopedic surgery and traumatology have undergone such a dramatic evolution in the last 10 years as knee surgery, arthroscopy and sports traumatology. Ranked among the top 33% of journals in both Orthopedics and Sports Sciences, the goal of this European journal is to publish papers about innovative knee surgery, sports trauma surgery and arthroscopy. Each issue features a series of peer-reviewed articles that deal with diagnosis and management and with basic research. Each issue also contains at least one review article about an important clinical problem. Case presentations or short notes about technical innovations are also accepted for publication.
The articles cover all aspects of knee surgery and all types of sports trauma; in addition, epidemiology, diagnosis, treatment and prevention, and all types of arthroscopy (not only the knee but also the shoulder, elbow, wrist, hip, ankle, etc.) are addressed. Articles on new diagnostic techniques such as MRI and ultrasound and high-quality articles about the biomechanics of joints, muscles and tendons are included. Although this is largely a clinical journal, it is also open to basic research with clinical relevance.
Because the journal is supported by a distinguished European Editorial Board, assisted by an international Advisory Board, you can be assured that the journal maintains the highest standards.
Official Clinical Journal of the European Society of Sports Traumatology, Knee Surgery and Arthroscopy (ESSKA).