Department of Biomedical Engineering, University of Southern California, Los Angeles, CA 90007, USA.
Department of Biomedical Engineering, Inje University, Gimhae 50834, Republic of Korea.
Sensors (Basel). 2024 Oct 8;24(19):6471. doi: 10.3390/s24196471.
Ultrasound is a versatile and well-established technique using sound waves with frequencies higher than the upper limit of human hearing. Typically, therapeutic and diagnosis ultrasound operate in the frequency range of 500 kHz to 15 MHz with greater depth of penetration into the body. However, to achieve improved spatial resolution, high-frequency ultrasound (>15 MHz) was recently introduced and has shown promise in various fields such as high-resolution imaging for the morphological features of the eye and skin as well as small animal imaging for drug and gene therapy. In addition, high-frequency ultrasound microbeam stimulation has been demonstrated to manipulate single cells or microparticles for the elucidation of physical and functional characteristics of cells with minimal effect on normal cell physiology and activity. Furthermore, integrating machine learning with high-frequency ultrasound enhances diagnostics, including cell classification, cell deformability estimation, and the diagnosis of diabetes and dysnatremia using convolutional neural networks (CNNs). In this paper, current efforts in the use of high-frequency ultrasound from imaging to stimulation as well as the integration of deep learning are reviewed, and potential biomedical and cellular applications are discussed.
超声是一种使用频率高于人类听觉上限的声波的多功能且成熟的技术。通常，治疗和诊断超声在 500 kHz 至 15 MHz 的频率范围内运行，能够更深地穿透人体。然而，为了实现更高的空间分辨率，最近引入了高频超声（>15 MHz），并在眼部和皮肤形态特征的高分辨率成像以及药物和基因治疗的小动物成像等各个领域显示出了潜力。此外，高频超声微束刺激已被证明可用于操纵单细胞或微颗粒，以最小化对正常细胞生理和活动的影响来阐明细胞的物理和功能特性。此外，将机器学习与高频超声相结合可增强诊断，包括使用卷积神经网络 (CNN) 进行细胞分类、细胞可变形性估计以及糖尿病和电解质紊乱的诊断。本文综述了从成像到刺激的高频超声的当前应用，以及深度学习的整合，并讨论了潜在的生物医学和细胞应用。
