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A Systematic Review on Artificial Intelligence in Orthopedic Surgery
参考中译:人工智能在骨科手术中应用的系统评价


          

刊名：Revue d'Intelligence Artificielle
作者：Nabila Ounasser（ADMIR Laboratory, ENSIAS, Mohammed V University）
Maryem Rhanoui（ADMIR Laboratory, ENSIAS, Mohammed V University）
Mounia Mikram（LYRICA Laboratory, School of Information Sciences）
Bouchra El Asri（ADMIR Laboratory, ENSIAS, Mohammed V University）
刊号：737F0004
ISSN：0992-499X
出版年：2024
年卷期：2024, vol.38, no.4
页码：1143-1157
总页数：15
分类号：TP3
关键词：Artificial intelligence；Deep learning；Machine learning；Generative adversarial network；Convolutional neural network；Orthopedic；Anomaly diagnosis；Medical image
参考中译：人工智能;深度学习;机器学习;生成对抗网络;卷积神经网络;骨科;异常诊断;医学图像
语种：eng
文摘：This systematic review aims to assess the efficacy of Artificial Intelligence (AI) applications in orthopedic surgery, with a focus on diagnostic accuracy and outcome prediction. In this review, we expose the findings of a systematic literature review awning the papers published from 2016 to October 2023 where authors worked on the application of an AI techniques and methods to an orthopedic purpose or problem. After application of inclusion and exclusion criteria on the extracted papers from PubMed and Google Scholar databases, 75 studies were included in this review. We examined, screened, and analyzed their content according to PRISMA guidelines. We also extracted data about the study design, the datasets included in the experiment, the reported performance measures and the results obtained. In this report, we will share the results of our survey by outlining the key machine and Deep Learning (DL) techniques, such as Convolutional Neural Network (CNN), Autoencoders and Generative Adversarial Network, that were mentioned, the various application domains in orthopedics, the type of source data and its modality, as well as the overall quality of their predictive capabilities. We aim to describe the content of the articles in detail and provide insights into the most notable trends and patterns observed in the survey data.
参考中译：这项系统性综述旨在评估人工智能（AI）在骨科手术中应用的有效性，重点关注诊断准确性和结果预测。在这篇评论中，我们揭示了系统性文献评论的结果，涵盖了2016年至2023年10月发表的论文，其中作者致力于将人工智能技术和方法应用于骨科目的或问题。对从PubMed和Google Scholar数据库中提取的论文应用纳入和排除标准后，本综述纳入了75项研究。我们根据PRISMA指南检查、筛选和分析了他们的内容。我们还提取了有关研究设计、实验中包含的数据集、报告的性能指标和获得的结果的数据。在本报告中，我们将通过概述所提到的关键机器和深度学习（DL）技术（例如卷积神经网络（CNN）、自动编码器和生成对抗网络）、骨科中的各个应用领域、源数据类型及其形式以及预测能力的整体质量来分享我们的调查结果。我们的目标是详细描述文章的内容，并深入了解调查数据中观察到的最显着的趋势和模式。



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