Abstract: This paper focuses on testing conditional independence between two random variables ($X$ and $Y$) given a set of high-dimensional confounding variables ($Z$). The high dimensionality of these confounding variables presents a challenge, often resulting in inflated type-I errors or insufficient power in many existing tests. To address this issue, we leverage the power of Deep Neural Networks (DNNs) to handle complex, high-dimensional data while mitigating the curse of dimensionality. We propose a novel test procedure, DeepBET. First, a DNN is used on part of the data to estimate the conditional means of $X$ and $Y$ given $Z$. Then, binary expansion testing (BET) are applied to the predicted errors from the remaining data. Additionally, we implement a multiple-split procedure to further enhance the power of the test. DeepBET is computationally efficient and robust to the tuning parameters in DNNs. Interestingly, the DeepBET statistic converges at a root-$n$ rate despite the nonparametric and high-dimensional nature of the confounding effects. Our numerical results demonstrate that the proposed method controls type-I error under various scenarios and enhances both power and interpretability for conditional dependence when present, making it a robust alternative for testing conditional independence in high-dimensional settings. When applied to dry eye disease data, DeepBET reveals meaningful nonlinear relationships between the epithelial thickness and the tear production in the central region of eyes, given other regions.