Shanghai Key Laboratory of Forensic Medicine, Shanghai Forensic Service Platform, Academy of Forensic Science, Ministry of Justice, Shanghai, P.R. China.
Department of Forensic Medicine, Ethics and Medical Law, Faculty of Medicine, University of Coimbra, Coimbra, Portugal.
Comput Methods Programs Biomed. 2023 Apr;232:107434. doi: 10.1016/j.cmpb.2023.107434. Epub 2023 Feb 21.
Diatom testing is supportive for drowning diagnosis in forensic medicine. However, it is very time-consuming and labor-intensive for technicians to identify microscopically a handful of diatoms in sample smears, especially under complex observable backgrounds. Recently, we successfully developed a software, named DiatomNet v1.0 intended to automatically identify diatom frustules in a whole slide under a clear background. Here, we introduced this new software and performed a validation study to elucidate how DiatomNet v1.0 improved its performance with the influence of visible impurities.
DiatomNet v1.0 has an intuitive, user-friendly and easy-to-learn graphical user interface (GUI) built in the Drupal and its core architecture for slide analysis including a convolutional neural network (CNN) is written in Python language. The build-in CNN model was evaluated for diatom identification under very complex observable backgrounds with mixtures of common impurities, including carbon pigments and sand sediments. Compared to the original model, the enhanced model following optimization with limited new datasets was evaluated systematically by independent testing and random control trials (RCTs).
In independent testing, the original DiatomNet v1.0 was moderately affected, especially when higher densities of impurities existed, and achieved a low recall of 0.817 and F1 score of 0.858 but good precision of 0.905. Following transfer learning with limited new datasets, the enhanced version had better results, with recall and F1 score values of 0.968. A comparative study on real slides showed that the upgraded DiatomNet v1.0 obtained F1 scores of 0.86 and 0.84 for carbon pigment and sand sediment, respectively, slightly worse than manual identification (carbon pigment: 0.91; sand sediment: 0.86), but much less time was needed.
The study verified that forensic diatom testing with aid of DiatomNet v1.0 is much more efficient than traditionally manual identification even under complex observable backgrounds. In terms of forensic diatom testing, we proposed a suggested standard on build-in model optimization and evaluation to strengthen the software's generalization in potentially complex conditions.
硅藻检测对法医学中的溺亡诊断具有支持作用。然而，对于技术人员来说，在样本涂片上通过显微镜识别少量硅藻是非常耗时且费力的，尤其是在复杂的可见背景下。最近，我们成功开发了一款软件，命名为 DiatomNet v1.0，旨在自动识别清晰背景下整张幻灯片中的硅藻外壳。在这里，我们介绍了这款新软件，并进行了验证研究，以阐明 DiatomNet v1.0 如何通过可见杂质的影响来提高其性能。
DiatomNet v1.0 具有直观、用户友好且易于学习的图形用户界面 (GUI)，内置在 Drupal 中，其幻灯片分析的核心架构包括一个卷积神经网络 (CNN)，使用 Python 语言编写。内置的 CNN 模型在具有常见杂质（包括碳颜料和沙沉积物）混合物的非常复杂的可见背景下进行了硅藻识别评估。与原始模型相比，经过优化并使用有限的新数据集进行增强的模型通过独立测试和随机对照试验 (RCT) 进行了系统评估。
在独立测试中，原始的 DiatomNet v1.0 受到中度影响，尤其是当杂质密度较高时，其召回率仅为 0.817，F1 得分为 0.858，但精度较好，为 0.905。在使用有限的新数据集进行迁移学习后，增强版本的效果更好，召回率和 F1 得分分别为 0.968。对真实幻灯片的比较研究表明，升级后的 DiatomNet v1.0 对碳颜料和沙沉积物的 F1 得分分别为 0.86 和 0.84，略低于手动识别（碳颜料：0.91；沙沉积物：0.86），但所需时间更少。
该研究验证了在复杂可见背景下，借助 DiatomNet v1.0 进行法医硅藻检测比传统的手动识别效率更高。在法医硅藻检测方面，我们提出了一个关于内置模型优化和评估的建议标准，以增强软件在潜在复杂条件下的泛化能力。
