Shao Wenyi
EMAI LLC, Laurel, MD 20723, USA.
Diagnostics (Basel). 2025 Apr 12;15(8):986. doi: 10.3390/diagnostics15080986.
Machine learning (ML) techniques have attracted many microwave researchers and engineers for their potential to improve performance in microwave- and millimeter-wave-based medical applications. This paper reviews ML algorithms, data acquisition, training techniques, and applications that have emerged in recent years. It also reviews state-of-the-art ML techniques applied for the detection of various organ diseases with microwave signals, achieving more successful results than using traditional methods alone, such as a higher diagnosis accuracy or spatial resolution and significantly improved efficiency. Challenges and the outlook of using ML in future microwave medical applications are also discussed.
机器学习（ML）技术因其在基于微波和毫米波的医学应用中具有提升性能的潜力，吸引了众多微波领域的研究人员和工程师。本文回顾了近年来出现的机器学习算法、数据采集、训练技术及应用。还回顾了将先进的机器学习技术应用于利用微波信号检测各种器官疾病的情况，与仅使用传统方法相比，取得了更成功的结果，如更高的诊断准确率或空间分辨率，以及显著提高的效率。此外，还讨论了在未来微波医学应用中使用机器学习所面临的挑战和前景。
