There are significant gaps in both experimental and theoretical understanding of mixed-phase clouds, their impacts on the hydrological cycle as well as their effects on atmospheric radiation. Accurately identifying liquid water layers in mixed-phase clouds is crucial for estimating cloud radiative effects. A proof-of-concept study utilizing a machine-learning-based liquid-layer detection method called VOODOO is presented. This method was applied alongside a single-column radiative transfer model to compare downwelling shortwave fluxes of mixed-phase clouds detected by the standard Cloudnet processing chain and VOODOO to ground-based pyranometer observations. Our findings reveal that VOODOO creates more realistic liquid water content distributions and significantly influences profiles of heating rates. Moreover, our study demonstrates a substantial enhancement in the estimation of shortwave cloud radiative effects of VOODOO compared to conventional method Cloudnet. Specifically, we observe a remarkable reduction in the mean absolute error of simulated shortwave radiation at the surface of 70\%, particularly in homogeneous cloud conditions. The mean percentage error of SW cloud radiative effects between Cloudnet and pyranometer observations is 44\%, while VOODOO+Cloudnet reduces this error to 8\%. Overall, our results underscore the potential of VOODOO to provide new insights into deep mixed-phase clouds, which were previously inaccessible using traditional lidar-based remote sensing techniques.