发表机构
Universidad Abierta Interamericana（美洲开放大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本文系统映射了2015年以来96篇文献，综述AI和深度学习结合CNN、迁移学习及数据增强在肺癌影像检测中的应用，显示其高灵敏度与特异性，但面临数据标准化、可解释性等挑战。
AI 中文摘要
肺癌是全球主要的死亡原因之一，其早期诊断对于改善患者的预后和生活质量至关重要。然而，用于检测肺癌的医学影像解读过程复杂，需要训练有素的专家。在此背景下，人工智能（AI）和深度学习（DL）作为自动化和优化影像分析的潜在工具应运而生。本工作的目标是回顾AI和DL在放射学领域用于肺癌检测的最新和相关应用。为此，我们在PubMed、IEEEXPLORE、Scopus和Web of Science等科学数据库中进行了详尽检索，并选取了2015年至今发表的96篇涉及AI和DL在生物医学工程中应用的文章。重点强调了使用卷积神经网络（CNN）结合迁移学习和数据增强作为提高影像解读过程准确性和效率的有前景的技术。结果表明，使用AI和DL可以为肺癌的早期诊断提供有效的替代方案，具有高灵敏度和特异性。然而，也指出了当前必须解决的局限性和挑战，以确保其在临床实践中负责任和安全的应用，例如缺乏标准化数据、模型的可解释性、患者隐私以及伦理和社会影响。结论是，使用AI和DL可以对肺癌患者的护理产生积极影响，但需要进一步的研究和监管以确保其质量和可靠性。
英文摘要
Lung cancer is one of the leading causes of death worldwide, and its early diagnosis is crucial to improving patients prognosis and quality of life. However, the process of interpreting medical images for the detection of lung cancer is complex and requires trained experts. In this context, artificial intelligence (AI) and deep learning (DL) emerge as potential tools to automate and optimize image analysis. The objective of this work is to review the most recent and relevant applications of AI and DL in the field of radiology for the detection of lung cancer. To this end, an exhaustive search was carried out in scientific databases such as PubMed,IEEEXPLORE, Scopus and Web of Science, and 96 articles published from 2015 to the present addressing the use of AI and DL in biomedical engineering were selected. Emphasis is placed on the use of convolutional neural networks (CNN) with transfer learning and Data Augmentation as promising techniques to improve the accuracy and efficiency of the image interpretation process. The results show that the use of AI and DL can offer an effective alternative for the early diagnosis of lung cancer, with high sensitivity and specificity. However, current limitations and challenges that must be addressed to guarantee its responsible and safe application in clinical practice are also identified, such as the lack of standardized data, the ex plainability of the models, patient privacy, and the ethical and social implications. It is concluded that the use of AI and DL can have a positive impact on the care of patients with lung cancer, but further research and regulation are required to ensure its quality and reliability.
