{"title":"Artificial intelligence for fracture diagnosis in orthopedic X-rays: current developments and future potential.","authors":"Sanskrati Sharma","doi":"10.1051/sicotj/2023018","DOIUrl":null,"url":null,"abstract":"<p><p>The use of artificial intelligence (AI) in the interpretation of orthopedic X-rays has shown great potential to improve the accuracy and efficiency of fracture diagnosis. AI algorithms rely on large datasets of annotated images to learn how to accurately classify and diagnose abnormalities. One way to improve AI interpretation of X-rays is to increase the size and quality of the datasets used for training, and to incorporate more advanced machine learning techniques, such as deep reinforcement learning, into the algorithms. Another approach is to integrate AI algorithms with other imaging modalities, such as computed tomography (CT) scans, and magnetic resonance imaging (MRI), to provide a more comprehensive and accurate diagnosis. Recent studies have shown that AI algorithms can accurately detect and classify fractures of the wrist and long bones on X-ray images, demonstrating the potential of AI to improve the accuracy and efficiency of fracture diagnosis. These findings suggest that AI has the potential to significantly improve patient outcomes in the field of orthopedics.</p>","PeriodicalId":46378,"journal":{"name":"SICOT-J","volume":null,"pages":null},"PeriodicalIF":1.8000,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10324466/pdf/","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"SICOT-J","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1051/sicotj/2023018","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"2023/7/6 0:00:00","PubModel":"Epub","JCR":"Q2","JCRName":"ORTHOPEDICS","Score":null,"Total":0}
引用次数: 0
Abstract
The use of artificial intelligence (AI) in the interpretation of orthopedic X-rays has shown great potential to improve the accuracy and efficiency of fracture diagnosis. AI algorithms rely on large datasets of annotated images to learn how to accurately classify and diagnose abnormalities. One way to improve AI interpretation of X-rays is to increase the size and quality of the datasets used for training, and to incorporate more advanced machine learning techniques, such as deep reinforcement learning, into the algorithms. Another approach is to integrate AI algorithms with other imaging modalities, such as computed tomography (CT) scans, and magnetic resonance imaging (MRI), to provide a more comprehensive and accurate diagnosis. Recent studies have shown that AI algorithms can accurately detect and classify fractures of the wrist and long bones on X-ray images, demonstrating the potential of AI to improve the accuracy and efficiency of fracture diagnosis. These findings suggest that AI has the potential to significantly improve patient outcomes in the field of orthopedics.
人工智能(AI)在骨科 X 射线判读中的应用已显示出提高骨折诊断准确性和效率的巨大潜力。人工智能算法依靠注释图像的大型数据集来学习如何准确分类和诊断异常。改进 X 射线人工智能判读的一种方法是提高用于训练的数据集的规模和质量,并将更先进的机器学习技术(如深度强化学习)纳入算法。另一种方法是将人工智能算法与计算机断层扫描(CT)和磁共振成像(MRI)等其他成像方式相结合,以提供更全面、更准确的诊断。最近的研究表明,人工智能算法可以准确检测 X 光图像上的腕骨和长骨骨折并对其进行分类,这表明人工智能具有提高骨折诊断准确性和效率的潜力。这些研究结果表明,人工智能有可能显著改善骨科领域患者的治疗效果。