A deep learning-enhanced surface acoustic wave platform for contact-free in situ biomechanical characterization of embryos.

Understanding the mechanical strength of embryos is essential for evaluating their viability in developmental biology. This study introduces a novel biosensor designed for in situ, non-contact mechanical characterization. The platform offers several unique contributions, including: (1) a microfluidic system combining SAW and deep learning for the automated quantification of embryonic mechanical strength; (2) a quantitative fitting model relating SAW power to acoustic radiation force for precise mechanical loading; (3) the first quantitative atlas of embryonic mechanical thresholds, achieved through the systematic measurement of two key parameters: the apparent maximum compressive stress (MCS) causing developmental arrest and the apparent ultimate compressive strength (UCS) leading to embryonic rupture. The results demonstrate consistent embryonic strengthening from zygote to pharyngula stages, with MCS increasing from 8.113 kPa to 11.407 kPa and UCS from 10.256 kPa to 14.219 kPa. This automated platform advances the understanding of embryonic structural integrity for developmental biology and tissue engineering.