PDF(Pubmed)
Abstract：
The integration of artificial intelligence (AI) in healthcare has sparked interest in its potential to revolutionize medical diagnostics. This systematic review explores the application of AI and machine learning (ML) techniques in diagnosing scaphoid fractures, which account for a significant percentage of carpal bone fractures and have important implications for wrist function. Scaphoid fractures, common in young and active individuals, require an early and accurate diagnosis for effective treatment. AI has the potential to automate and improve the accuracy of scaphoid fracture detection on radiography, aiding in early diagnosis and reducing unnecessary clinical examinations. This systematic review discusses the methods used to identify relevant studies, including search criteria and quality assessment tools, and presents the results of the selected studies. The findings indicate that AI-driven methods can improve diagnostic accuracy, reducing the risk of missed fractures and complications. AI assistance can also alleviate the workload of medical professionals, improving diagnostic efficiency and reducing observer fatigue. However, challenges such as algorithm limitations and the need for continuous refinement must be addressed to ensure reliable fracture identification. This review underscores the clinical significance of AI-assisted diagnostics, especially in cases where fractures may be subtle or occult. It emphasizes the importance of integrating AI into medical education and training and calls for robust data collection and collaboration between AI developers and medical practitioners. Future research should focus on larger datasets, algorithm improvement, cost-effectiveness assessment, and international partnerships to fully harness the potential of AI in diagnosing scaphoid fractures.
摘要：
人工智能（AI）在医疗保健中的集成激发了人们对其彻底改变医疗诊断的潜力的兴趣。这篇系统的综述探讨了人工智能和机器学习（ML）技术在诊断舟骨骨折中的应用。占腕骨骨折的很大比例，对腕关节功能有重要影响。舟骨骨折,常见于年轻活跃的个体，需要早期准确的诊断才能有效治疗。AI有可能自动化并提高X线摄影上舟骨骨折检测的准确性，有助于早期诊断，减少不必要的临床检查。这篇系统综述讨论了用于识别相关研究的方法，包括搜索标准和质量评估工具，并介绍了选定研究的结果。研究结果表明，人工智能驱动的方法可以提高诊断的准确性，降低骨折和并发症的风险。人工智能辅助还可以减轻医疗专业人员的工作量，提高诊断效率，减少观察者疲劳。然而,必须解决诸如算法限制和需要连续细化等挑战，以确保可靠的裂缝识别。这篇综述强调了AI辅助诊断的临床意义，尤其是在骨折可能是微妙或隐匿的情况下。它强调了将AI整合到医学教育和培训中的重要性，并呼吁AI开发人员和医疗从业者之间进行强大的数据收集和协作。未来的研究应该集中在更大的数据集上，算法改进，成本效益评估，和国际合作伙伴关系，充分利用人工智能在诊断舟骨骨折方面的潜力。
