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IF 1.4 Q3 ORTHOPEDICS

European Journal of Orthopaedic Surgery and Traumatology Pub Date : 2025-03-11 DOI:10.1007/s00590-025-04238-z

Prin Twinprai, Ong-Art Phruetthiphat, Krit Wongwises, Rit Apinyankul, Puripong Suthisopapan, Wongthawat Liawrungrueang, Nattaphon Twinprai

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引用次数: 0

摘要

目的：全膝关节置换术（TKA）被认为是治疗终末期膝关节骨性关节炎的金标准疗法。全膝关节置换术常见的并发症包括假体松动和假体周围骨折，通常需要进行翻修手术或固定。如果膝关节假体的相关医疗记录丢失，就很难有效地制定翻修手术计划。本研究旨在开发一种人工智能（AI）系统，利用平片对膝关节假体类型进行分类：这项回顾性实验研究包括本院常用的七种膝关节假体。人工智能（AI）系统使用 YOLO（You Only Look Once）第 9 版进行训练，数据集包括 3228 张膝关节置换术后和随访 X 光图像。对普通 X 光图像进行了扩增，形成了一个包含 25800 张图像的数据集。对模型参数进行了微调，以优化植入物分类的性能：结果：患者的平均年龄为 62.8 岁。48.3%的病例进行了右膝关节置换术，51.7%的病例进行了左膝关节置换术。膝关节假体图像占数据集的 50.9%，从正后方（AP）视图拍摄，49.1% 从侧方视图拍摄。人工智能模型表现出了卓越的性能指标，精确率、召回率和准确率均达到 100%，F1 得分为 1。此外，经计算，接收者操作特征曲线（ROC）的曲线下面积（AUC）为 100%：该人工智能模型成功地从普通X光片中对膝关节假体植入物进行了分类。结论：这一人工智能模型成功地从普通X光片对膝关节假体进行了分类，为外科医生提供了宝贵的工具，可精确规划翻修手术和假体周围骨折固定手术，最终改善患者的预后。该人工智能所达到的高精确度突显了它在提高手术效率和有效控制膝关节置换并发症方面的潜力。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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AI classification of knee prostheses from plain radiographs and real-world applications.

Purpose: Total knee arthroplasty (TKA) is considered the gold standard treatment for end-stage knee osteoarthritis. Common complications associated with TKA include implant loosening and periprosthetic fractures, which often require revision surgery or fixation. Challenges arise when medical records related to the knee prosthesis are lost, making it difficult to plan for revision surgery effectively. This study aims to develop an artificial intelligence (AI) system to classify the types of knee prosthetic implants using plain radiographs.

Methods: This retrospective experimental study includes seven types of knee prostheses commonly used in our hospital. The artificial intelligence (AI) system was trained using YOLO (You Only Look Once) version 9, utilizing a dataset of 3228 post-operative and follow-up knee arthroplasty X-ray images. The plain radiographic images were augmented, resulting in a dataset of 25,800 images. Model parameters were fine-tuned to optimize performance for implant classification.

Results: The mean age of the patients was 62.8 years. Right knee arthroplasty was performed in 48.3% of cases, while left knee arthroplasty was performed in 51.7%. The images of knee prostheses comprised 50.9% of the dataset from the anteroposterior (AP) view and 49.1% from the lateral view. The AI model demonstrated exceptional performance metrics, achieving precision, recall, and accuracy rates of 100%, with an F1 score of 1. Additionally, the area under the curve (AUC) of the receiver operating characteristic (ROC) curve was calculated to be 100%.

Conclusion: This AI model successfully classifies knee prosthetic implants from plain radiographs. This capability serves as a valuable tool for surgeons, enabling precise planning for revision surgeries and periprosthetic fracture fixation surgery, ultimately contributing to improved patient outcomes. The high accuracy achieved by the AI underscores its potential to enhance surgical efficiency and effectiveness in managing knee arthroplasty complications.

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来源期刊

European Journal of Orthopaedic Surgery and Traumatology 医学-整形外科

CiteScore

3.00

自引率

5.90%

发文量

265

审稿时长

3-8 weeks

期刊介绍： The European Journal of Orthopaedic Surgery and Traumatology (EJOST) aims to publish high quality Orthopedic scientific work. The objective of our journal is to disseminate meaningful, impactful, clinically relevant work from each and every region of the world, that has the potential to change and or inform clinical practice.