School of Electronic Engineering and Computer Science, Queen Mary University of London, Mile End, London, United Kingdom.
Centre for Neuroscience, Surgery and Trauma, The Blizard Institute, Barts and The London School of Medicine and Dentistry, Queen Mary University of London, London, United Kingdom.
PLoS One. 2022 Jun 15;17(6):e0268962. doi: 10.1371/journal.pone.0268962. eCollection 2022.
The early detection of traumatic brain injuries can directly impact the prognosis and survival of patients. Preceding attempts to automate the detection and the assessment of the severity of traumatic brain injury continue to be based on clinical diagnostic methods, with limited tools for disease outcomes in large populations. Despite advances in machine and deep learning tools, current approaches still use simple trends of statistical analysis which lack generality. The effectiveness of deep learning to extract information from large subsets of data can be further emphasised through the use of more elaborate architectures. We therefore explore the use of a multiple input, convolutional neural network and long short-term memory (LSTM) integrated architecture in the context of traumatic injury detection through predicting the presence of brain injury in a murine preclinical model dataset. We investigated the effectiveness and validity of traumatic brain injury detection in the proposed model against various other machine learning algorithms such as the support vector machine, the random forest classifier and the feedforward neural network. Our dataset was acquired using a home cage automated (HCA) system to assess the individual behaviour of mice with traumatic brain injury or non-central nervous system (non-CNS) injured controls, whilst housed in their cages. Their distance travelled, body temperature, separation from other mice and movement were recorded every 15 minutes, for 72 hours weekly, for 5 weeks following intervention. The HCA behavioural data was used to train a deep learning model, which then predicts if the animals were subjected to a brain injury or just a sham intervention without brain damage. We also explored and evaluated different ways to handle the class imbalance present in the uninjured class of our training data. We then evaluated our models with leave-one-out cross validation. Our proposed deep learning model achieved the best performance and showed promise in its capability to detect the presence of brain trauma in mice.
外伤性脑损伤的早期检测可以直接影响患者的预后和生存。以前尝试自动化检测和评估外伤性脑损伤的方法仍然基于临床诊断方法，对于大人群的疾病结果的工具有限。尽管在机器和深度学习工具方面取得了进展，但目前的方法仍然使用缺乏通用性的简单统计分析趋势。通过使用更精细的架构，可以进一步强调深度学习从大数据集中提取信息的有效性。因此，我们探索了在预测啮齿动物临床前模型数据集中脑损伤存在的情况下，使用多输入卷积神经网络和长短期记忆（LSTM）集成架构进行外伤性损伤检测的方法。我们研究了所提出的模型在创伤性脑损伤检测中的有效性和有效性，针对各种其他机器学习算法，如支持向量机、随机森林分类器和前馈神经网络。我们的数据集是使用家庭笼自动化（HCA）系统获取的，用于评估外伤性脑损伤或非中枢神经系统（非 CNS）损伤对照小鼠的个体行为，同时将它们饲养在笼子中。在干预后 5 周内，每周 72 小时，每 15 分钟记录它们的行进距离、体温、与其他小鼠的分离和运动情况。HCA 行为数据用于训练深度学习模型，该模型然后预测动物是否受到脑损伤或只是没有脑损伤的假干预。我们还探索和评估了处理训练数据中未受伤类别的类别不平衡的不同方法。然后，我们使用留一法交叉验证来评估我们的模型。我们提出的深度学习模型取得了最佳性能，并在其检测小鼠脑外伤存在的能力方面表现出了前景。
