Li Meihui, Zheng Haiyan, Koh Jae Chul, Choe Ghee Young, Choi Eun Joo, Nahm Francis Sahngun, Lee Pyung Bok
Department of Anesthesiology and Pain Medicine, Seoul National University College of Medicine, Seoul, Korea.
Department of Anesthesiology and Pain Medicine, Seoul National University Bundang Hospital, Seongnam, Korea.
J Pain Res. 2024 Apr 6;17:1369-1380. doi: 10.2147/JPR.S444055. eCollection 2024.
To create a deep learning (DL) model that can accurately detect and classify three distinct types of rat dorsal root ganglion neurons: normal, segmental chromatolysis, and central chromatolysis. The DL model has the potential to improve the efficiency and precision of neuron classification in research related to spinal injuries and diseases.
H&E slide images were divided into an internal training set (80%) and a test set (20%). The training dataset was labeled by two pathologists using pre-defined grades. Using this dataset, a two-component DL model was developed with the first component being a convolutional neural network (CNN) that was trained to detect the region of interest (ROI) and the second component being another CNN used for classification.
A total of 240 lumbar dorsal root ganglion (DRG) pathology slide images from rats were analyzed. The internal testing results showed an accuracy of 93.13%, and the external dataset testing demonstrated an accuracy of 93.44%.
The DL model demonstrated a level of agreement comparable to that of pathologists in detecting and classifying normal and segmental chromatolysis neurons, although its agreement was slightly lower for central chromatolysis neurons. Significance: DL in improving the accuracy and efficiency of pathological analysis suggests that it may have a role in enhancing medical decision-making.
创建一种深度学习（DL）模型，该模型能够准确检测并分类三种不同类型的大鼠背根神经节神经元：正常神经元、节段性染色质溶解神经元和中央性染色质溶解神经元。该DL模型有潜力提高与脊髓损伤和疾病相关研究中神经元分类的效率和精度。
苏木精-伊红（H&E）染色切片图像被分为内部训练集（80%）和测试集（20%）。训练数据集由两名病理学家使用预定义的等级进行标注。利用该数据集，开发了一个双组件DL模型，第一个组件是一个卷积神经网络（CNN），用于训练检测感兴趣区域（ROI），第二个组件是另一个用于分类的CNN。
共分析了240张来自大鼠的腰段背根神经节（DRG）病理切片图像。内部测试结果显示准确率为93.13%，外部数据集测试显示准确率为93.44%。
DL模型在检测和分类正常和节段性染色质溶解神经元方面表现出与病理学家相当的一致性水平，尽管其对中央性染色质溶解神经元的一致性略低。意义：DL在提高病理分析的准确性和效率方面表明，它可能在增强医疗决策中发挥作用。
