Since the 1960s, neonatal clinicians have known that newborns suffering from certain neurological conditions exhibit altered crying patterns such as the high-pitched cry in birth asphyxia. Despite an annual burden of over 1.5 million infant deaths and disabilities, early detection of neonatal brain injuries due to asphyxia remains a challenge, particularly in developing countries where the majority of births are not attended by a trained physician. Here we report on the first inter-continental clinical study to demonstrate that neonatal brain injury can be reliably determined from recorded infant cries using an AI algorithm we call Roseline. Previous and recent work has been limited by the lack of a large, high-quality clinical database of cry recordings, constraining the application of state-of-the-art machine learning. We develop a new training methodology for audio-based pathology detection models and evaluate this system on a large database of newborn cry sounds acquired from geographically diverse settings -- 5 hospitals across 3 continents. Our system extracts interpretable acoustic biomarkers that support clinical decisions and is able to accurately detect neurological injury from newborns' cries with an AUC of 92.5% (88.7% sensitivity at 80% specificity). Cry-based neurological monitoring opens the door for low-cost, easy-to-use, non-invasive and contact-free screening of at-risk babies, especially when integrated into simple devices like smartphones or neonatal ICU monitors. This would provide a reliable tool where there are no alternatives, but also curtail the need to regularly exert newborns to physically-exhausting or radiation-exposing assessments such as brain CT scans. This work sets the stage for embracing the infant cry as a vital sign and indicates the potential of AI-driven sound monitoring for the future of affordable healthcare.

翻译：自20世纪60年代以来，新生儿临床医生已发现某些神经系统疾病患儿会表现出异常啼哭模式，例如出生窒息时的高音调哭声。尽管每年有超过150万婴儿死亡或残疾，但由窒息导致的新生儿脑损伤早期检测仍面临挑战，尤其是在发展中国家——大多数分娩缺乏训练有素的医生在场。本研究首次通过跨洲临床研究证明，我们开发的Roseline人工智能算法能够可靠地从记录到的婴儿啼哭中判定新生儿脑损伤。既往及近期研究因缺乏大规模、高质量哭声音频临床数据库而受限，导致最先进的机器学习方法难以应用。我们开发了一套新的音频病理检测模型训练方法，并在跨地理分布的庞大新生儿哭声数据库（覆盖三大洲五家医院）上评估该系统。该算法可提取可解释的声学生物标志物以支持临床决策，能准确检测新生儿哭声中的神经损伤，AUC达92.5%（80%特异性下灵敏度为88.7%）。基于哭声的神经监测为高危婴儿的低成本、易操作、无创非接触式筛查开辟了新途径，尤其适合集成至智能手机或新生儿重症监护监护仪等简易设备。在缺乏替代方案的场景下，该系统可提供可靠工具，同时减少对新生儿进行脑部CT扫描等体力消耗或辐射暴露检查的需求。本研究为将婴儿啼哭视为生命体征奠定了基础，并揭示了AI驱动的声音监测在推动普惠医疗方面的潜力。