Shang-Yu Yao, Xue-Zhi Zhang, Soumyajit Podder, Chen-Te Wu, Yi-Shen Chan, Dan Berco, Cheng-Pang Yang
{"title":"与人工技术相比,基于深度学习的胫骨后斜度测量更可靠、更省时。","authors":"Shang-Yu Yao, Xue-Zhi Zhang, Soumyajit Podder, Chen-Te Wu, Yi-Shen Chan, Dan Berco, Cheng-Pang Yang","doi":"10.1002/ksa.12241","DOIUrl":null,"url":null,"abstract":"<p><strong>Purpose: </strong>Multifaceted factors contribute to inferior outcomes following anterior cruciate ligament (ACL) reconstruction surgery. A particular focus is placed on the posterior tibial slope (PTS). This study introduces the integration of machine learning and artificial intelligence (AI) for efficient measurements of tibial slopes on magnetic resonance imaging images as a promising solution. This advancement aims to enhance risk stratification, diagnostic insights, intervention prognosis and surgical planning for ACL injuries.</p><p><strong>Methods: </strong>Images and demographic information from 120 patients who underwent ACL reconstruction surgery were used for this study. An AI-driven model was developed to measure the posterior lateral tibial slope using the YOLOv8 algorithm. The accuracy of the lateral tibial slope, medial tibial slope and tibial longitudinal axis measurements was assessed, and the results reached high levels of reliability. This study employed machine learning and AI techniques to provide objective, consistent and efficient measurements of tibial slopes on MR images.</p><p><strong>Results: </strong>Three distinct models were developed to derive AI-based measurements. The study results revealed a substantial correlation between the measurements obtained from the AI models and those obtained by the orthopaedic surgeon across three parameters: lateral tibial slope, medial tibial slope and tibial longitudinal axis. Specifically, the Pearson correlation coefficients were 0.673, 0.850 and 0.839, respectively. The Spearman rank correlation coefficients were 0.736, 0.861 and 0.738, respectively. Additionally, the interclass correlation coefficients were 0.63, 0.84 and 0.84, respectively.</p><p><strong>Conclusion: </strong>This study establishes that the deep learning-based method for measuring posterior tibial slopes strongly correlates with the evaluations of expert orthopaedic surgeons. The time efficiency and consistency of this technique suggest its utility in clinical practice, promising to enhance workflow, risk assessment and the customization of patient treatment plans.</p><p><strong>Level of evidence: </strong>Level III, cross-sectional diagnostic study.</p>","PeriodicalId":17880,"journal":{"name":"Knee Surgery, Sports Traumatology, Arthroscopy","volume":" ","pages":"59-69"},"PeriodicalIF":3.3000,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Enhanced reliability and time efficiency of deep learning-based posterior tibial slope measurement over manual techniques.\",\"authors\":\"Shang-Yu Yao, Xue-Zhi Zhang, Soumyajit Podder, Chen-Te Wu, Yi-Shen Chan, Dan Berco, Cheng-Pang Yang\",\"doi\":\"10.1002/ksa.12241\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p><strong>Purpose: </strong>Multifaceted factors contribute to inferior outcomes following anterior cruciate ligament (ACL) reconstruction surgery. A particular focus is placed on the posterior tibial slope (PTS). This study introduces the integration of machine learning and artificial intelligence (AI) for efficient measurements of tibial slopes on magnetic resonance imaging images as a promising solution. This advancement aims to enhance risk stratification, diagnostic insights, intervention prognosis and surgical planning for ACL injuries.</p><p><strong>Methods: </strong>Images and demographic information from 120 patients who underwent ACL reconstruction surgery were used for this study. An AI-driven model was developed to measure the posterior lateral tibial slope using the YOLOv8 algorithm. The accuracy of the lateral tibial slope, medial tibial slope and tibial longitudinal axis measurements was assessed, and the results reached high levels of reliability. This study employed machine learning and AI techniques to provide objective, consistent and efficient measurements of tibial slopes on MR images.</p><p><strong>Results: </strong>Three distinct models were developed to derive AI-based measurements. The study results revealed a substantial correlation between the measurements obtained from the AI models and those obtained by the orthopaedic surgeon across three parameters: lateral tibial slope, medial tibial slope and tibial longitudinal axis. Specifically, the Pearson correlation coefficients were 0.673, 0.850 and 0.839, respectively. The Spearman rank correlation coefficients were 0.736, 0.861 and 0.738, respectively. Additionally, the interclass correlation coefficients were 0.63, 0.84 and 0.84, respectively.</p><p><strong>Conclusion: </strong>This study establishes that the deep learning-based method for measuring posterior tibial slopes strongly correlates with the evaluations of expert orthopaedic surgeons. The time efficiency and consistency of this technique suggest its utility in clinical practice, promising to enhance workflow, risk assessment and the customization of patient treatment plans.</p><p><strong>Level of evidence: </strong>Level III, cross-sectional diagnostic study.</p>\",\"PeriodicalId\":17880,\"journal\":{\"name\":\"Knee Surgery, Sports Traumatology, Arthroscopy\",\"volume\":\" \",\"pages\":\"59-69\"},\"PeriodicalIF\":3.3000,\"publicationDate\":\"2025-01-01\",\"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.12241\",\"RegionNum\":2,\"RegionCategory\":\"医学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"2024/5/26 0:00:00\",\"PubModel\":\"Epub\",\"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.12241","RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"2024/5/26 0:00:00","PubModel":"Epub","JCR":"Q1","JCRName":"ORTHOPEDICS","Score":null,"Total":0}
Enhanced reliability and time efficiency of deep learning-based posterior tibial slope measurement over manual techniques.
Purpose: Multifaceted factors contribute to inferior outcomes following anterior cruciate ligament (ACL) reconstruction surgery. A particular focus is placed on the posterior tibial slope (PTS). This study introduces the integration of machine learning and artificial intelligence (AI) for efficient measurements of tibial slopes on magnetic resonance imaging images as a promising solution. This advancement aims to enhance risk stratification, diagnostic insights, intervention prognosis and surgical planning for ACL injuries.
Methods: Images and demographic information from 120 patients who underwent ACL reconstruction surgery were used for this study. An AI-driven model was developed to measure the posterior lateral tibial slope using the YOLOv8 algorithm. The accuracy of the lateral tibial slope, medial tibial slope and tibial longitudinal axis measurements was assessed, and the results reached high levels of reliability. This study employed machine learning and AI techniques to provide objective, consistent and efficient measurements of tibial slopes on MR images.
Results: Three distinct models were developed to derive AI-based measurements. The study results revealed a substantial correlation between the measurements obtained from the AI models and those obtained by the orthopaedic surgeon across three parameters: lateral tibial slope, medial tibial slope and tibial longitudinal axis. Specifically, the Pearson correlation coefficients were 0.673, 0.850 and 0.839, respectively. The Spearman rank correlation coefficients were 0.736, 0.861 and 0.738, respectively. Additionally, the interclass correlation coefficients were 0.63, 0.84 and 0.84, respectively.
Conclusion: This study establishes that the deep learning-based method for measuring posterior tibial slopes strongly correlates with the evaluations of expert orthopaedic surgeons. The time efficiency and consistency of this technique suggest its utility in clinical practice, promising to enhance workflow, risk assessment and the customization of patient treatment plans.
Level of evidence: Level III, cross-sectional diagnostic study.
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).