Lee Seungmin, Park Jeong Soo, Hong Ji Hye, Woo Hyowon, Lee Chang-Hyun, Yoon Ju Hwan, Lee Ki-Baek, Chung Seok, Yoon Dae Sung, Lee Jeong Hoon
KU-KIST Graduate School of Converging Science and Technology, Korea University, 145 Anam-ro, Seongbuk-gu, Seoul, 02841, Republic of Korea; School of Biomedical Engineering, Korea University, 145 Anam-ro, Seongbuk, Seoul, 02841, Republic of Korea.
KU-KIST Graduate School of Converging Science and Technology, Korea University, 145 Anam-ro, Seongbuk-gu, Seoul, 02841, Republic of Korea; School of Mechanical Engineering, Korea University, 145 Anam-ro, Seoungbuk-gu, Seoul, 02841, Republic of Korea.
Biosens Bioelectron. 2025 Jul 15;280:117399. doi: 10.1016/j.bios.2025.117399. Epub 2025 Mar 19.
Recently, artificial intelligence (AI) has emerged as a transformative tool, enhancing the speed, accuracy, and scalability of bacterial diagnostics. This review explores the role of AI in revolutionizing bacterial detection and antimicrobial susceptibility testing (AST) by leveraging machine learning models, including Random Forest, Support Vector Machines (SVM), and deep learning architectures such as Convolutional Neural Networks (CNNs) and transformers. The integration of AI into these methods promises to address the current limitations of traditional techniques, offering a path toward more efficient, accessible, and reliable diagnostic solutions. In particular, AI-based approaches have demonstrated significant potential in resource-limited settings by enabling cost-effective and portable diagnostic solutions, reducing dependency on specialized infrastructure, and facilitating remote bacterial detection through smartphone-integrated platforms and telemedicine applications. This review highlights AI's transformative role in automating data analysis, minimizing human error, and delivering real-time diagnostic results, ultimately improving patient outcomes and optimizing healthcare efficiency. In addition, we not only examine the current advances in machine learning and deep learning but also review their applications in plate counting, mass spectrometry, morphology-based and motion-based microscopic detection, holographic microscopy, colorimetric and fluorescence detection, electrochemical sensors, Raman and Surface-Enhanced Raman Spectroscopy (SERS), and Atomic Force Microscopy (AFM) for bacterial diagnostics and AST. Finally, we discuss the future directions and potential advancements in AI-driven bacterial diagnostics.
最近，人工智能（AI）已成为一种变革性工具，提高了细菌诊断的速度、准确性和可扩展性。本综述探讨了人工智能在利用机器学习模型（包括随机森林、支持向量机（SVM））以及深度学习架构（如卷积神经网络（CNN）和变换器）来彻底改变细菌检测和抗菌药物敏感性测试（AST）方面的作用。将人工智能集成到这些方法中有望解决传统技术当前的局限性，为实现更高效、可及和可靠的诊断解决方案提供一条途径。特别是，基于人工智能的方法通过实现经济高效且便携的诊断解决方案、减少对专业基础设施的依赖以及通过集成智能手机的平台和远程医疗应用促进远程细菌检测，在资源有限的环境中展现出了巨大潜力。本综述强调了人工智能在自动化数据分析、最大限度减少人为错误以及提供实时诊断结果方面的变革性作用，最终改善患者预后并优化医疗效率。此外，我们不仅研究了机器学习和深度学习的当前进展，还回顾了它们在平板计数、质谱分析、基于形态学和基于运动的显微镜检测、全息显微镜、比色和荧光检测、电化学传感器、拉曼和表面增强拉曼光谱（SERS）以及原子力显微镜（AFM）用于细菌诊断和AST方面的应用。最后，我们讨论了人工智能驱动的细菌诊断的未来方向和潜在进展。
