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Ultrasound is the most commonly used diagnostic imaging technique during pregnancy. It is cheap, does not require ionizing radiation and can be performed at the bedside. Despite these advantages, it does have some drawbacks such as relatively low imaging quality, low contrast, and high variability. With these constraints, automating the interpretation of ultrasound images is challenging. In this talk, I will introduce our fully automated ultrasound screening tool OxNNet developed based on image processing, deep learning, and big data, as well as its application to the human placenta in early pregnancy. I will start with the state-of-the-art techniques for placenta analysis, and then focus on the placental perfusion estimation using our novel method svFMBV (single vessel fractional moving blood volume). Finally, I will talk about the challenges I encountered in interdisciplinary collaboration and innovation and how I overcame them.