PDF(Pubmed)
Abstract：
Tuberculosis (TB) remains a significant global health concern, particularly with the emergence of multidrug-resistant tuberculosis (MDR-TB) and extensively drug-resistant tuberculosis (XDR-TB). Traditional methods for diagnosing drug resistance in TB are time-consuming and often lack accuracy, leading to delays in appropriate treatment initiation and exacerbating the spread of drug-resistant strains. In recent years, artificial intelligence (AI) techniques have shown promise in revolutionizing TB diagnosis, offering rapid and accurate identification of drug-resistant strains. This comprehensive review explores the latest advancements in AI applications for the diagnosis of MDR-TB and XDR-TB. We discuss the various AI algorithms and methodologies employed, including machine learning, deep learning, and ensemble techniques, and their comparative performances in TB diagnosis. Furthermore, we examine the integration of AI with novel diagnostic modalities such as whole-genome sequencing, molecular assays, and radiological imaging, enhancing the accuracy and efficiency of TB diagnosis. Challenges and limitations surrounding the implementation of AI in TB diagnosis, such as data availability, algorithm interpretability, and regulatory considerations, are also addressed. Finally, we highlight future directions and opportunities for the integration of AI into routine clinical practice for combating drug-resistant TB, ultimately contributing to improved patient outcomes and enhanced global TB control efforts.
摘要：
结核病（TB）仍然是一个重要的全球健康问题，特别是随着耐多药结核病（MDR-TB）和广泛耐药结核病（XDR-TB）的出现。传统的结核病耐药诊断方法耗时且往往缺乏准确性,导致延迟适当的治疗开始和加剧耐药菌株的传播。近年来,人工智能（AI）技术在革新结核病诊断方面显示出了希望，提供快速准确的耐药菌株鉴定。这篇全面的综述探讨了用于诊断MDR-TB和XDR-TB的AI应用的最新进展。我们讨论了各种人工智能算法和方法，包括机器学习，深度学习,和合奏技术，以及它们在结核病诊断中的比较表现。此外，我们研究了人工智能与新的诊断方式的整合，如全基因组测序，分子测定,和放射成像，提高结核病诊断的准确性和效率。围绕在结核病诊断中实施人工智能的挑战和局限性，例如数据可用性，算法可解释性，和监管方面的考虑，也解决了。最后,我们强调未来将人工智能整合到常规临床实践中以对抗耐药结核病的方向和机会，最终有助于改善患者预后和加强全球结核病控制工作。
