Desai Arjun D, Peng Chunlei, Fang Leyuan, Mukherjee Dibyendu, Yeung Andrew, Jaffe Stephanie J, Griffin Jennifer B, Farsiu Sina
Department of Biomedical Engineering, Duke University, Durham 27708, USA.
Department of Computer Science, Duke University, Durham 27708, USA.
Biomed Opt Express. 2018 Nov 7;9(12):6038-6052. doi: 10.1364/BOE.9.006038. eCollection 2018 Dec 1.
Gestational age estimation at time of birth is critical for determining the degree of prematurity of the infant and for administering appropriate postnatal treatment. We present a fully automated algorithm for estimating gestational age of premature infants through smartphone lens imaging of the anterior lens capsule vasculature (ALCV). Our algorithm uses a fully convolutional network and blind image quality analyzers to segment usable anterior capsule regions. Then, it extracts ALCV features using a residual neural network architecture and trains on these features using a support vector machine-based classifier. The classification algorithm is validated using leave-one-out cross-validation on videos captured from 124 neonates. The algorithm is expected to be an influential tool for remote and point-of-care gestational age estimation of premature neonates in low-income countries. To this end, we have made the software open source.
出生时的孕周估计对于确定婴儿的早产程度以及进行适当的产后治疗至关重要。我们提出了一种通过智能手机镜头对前囊膜血管系统（ALCV）进行成像来估计早产儿孕周的全自动算法。我们的算法使用全卷积网络和盲图像质量分析仪来分割可用的前囊区域。然后，它使用残差神经网络架构提取ALCV特征，并使用基于支持向量机的分类器对这些特征进行训练。该分类算法通过对从124名新生儿拍摄的视频进行留一法交叉验证来进行验证。预计该算法将成为低收入国家远程和床旁早产儿孕周估计的一项有影响力的工具。为此，我们已将该软件开源。
