Digital whole-slide image analysis for automated diatom test in forensic cases of drowning using a convolutional neural network algorithm

Diatom examinations have been widely used to perform drowning diagnosis in forensic practice. However, current methods for recognizing diatoms, which use light or electron microscopy, are time-consuming and laborious and often result in false positive or negative decisions. In this study, we demonstrated an artificial intelligence (AI)-based system to automatically identify diatoms in conjunction with a classical chemical digestion approach. By employing transfer learning and data augmentation methods, we trained convolutional neural network (CNN) models on thousands or tens of thousands of tiles from digital whole-slide images of diatom smears. The results showed that the trained model identified the regions containing diatoms in the tiles. In an independent test, where the slide samples were collected in forensic casework, the best CNN model demonstrated a performance competitive with those of 5 forensic pathologists with experience in diatom quantification. This pilot study paves the way for future intelligent diatom examinations; many efficient diatom extraction methods could be incorporated into our automated system. (C) 2019 Elsevier B.V. All rights reserved.

硅藻检验在法医实践中已被广泛用于溺水诊断。然而，目前利用光学或电子显微镜识别硅藻的方法既耗时又费力，且常常导致假阳性或假阴性的判定结果。在这项研究中，我们展示了一种基于人工智能（AI）的系统，它结合经典的化学消化方法自动识别硅藻。通过采用迁移学习和数据增强方法，我们在来自硅藻涂片的数字全切片图像的数千或数万个切片上训练卷积神经网络（CNN）模型。结果表明，训练后的模型能够识别切片中含有硅藻的区域。在一项独立测试中，载玻片样本是在法医案件工作中收集的，最佳的CNN模型表现出与5名具有硅藻定量经验的法医病理学家相当的性能。这项初步研究为未来的智能硅藻检验铺平了道路；许多高效的硅藻提取方法可以纳入我们的自动化系统。（C）2019爱思唯尔有限公司。保留所有权利。