深度学习在非增强CT上急性缺血性卒中分割相较于神经放射学专家的非劣效性
Non-inferiority of Deep Learning Acute Ischemic Stroke Segmentation on Non-Contrast CT Compared to Expert Neuroradiologists
AI总结：
本研究训练三维CNN在非增强CT上分割急性缺血性卒中缺血核心，以专家A为金标准，模型与专家B、C的一致性经非劣效性检验不劣于专家间一致性，证明深度学习分割精度可与神经放射学专家相当。
摘要：
为确定卷积神经网络（CNN）深度学习模型能否在非增强CT上准确分割急性缺血性改变，并与神经放射学专家进行比较。本研究纳入了DEFUSE 3试验中232例急性缺血性卒中患者的非增强CT（NCCT）检查。三位经验丰富的神经放射学专家独立分割了每次扫描中反映缺血核心的低密度区域。经验最丰富的神经放射学专家（专家A）作为深度学习模型训练的金标准。另外两位神经放射学专家（专家B和C）的分割结果用于数据测试。232项研究被随机分为训练集和测试集。训练集进一步随机分为5折，包含训练集和验证集。训练并优化了一个三维CNN架构，以从NCCT预测专家A的分割结果。使用一组体积、重叠和距离指标评估模型性能，非劣效性阈值分别为20%、3ml和3mm。将在专家A上训练的优化模型与测试专家B和C进行比较。我们使用单侧Wilcoxon符号秩检验来检验模型-专家一致性相较于专家间一致性的非劣效性。在专家A上训练时，缺血核心分割任务的最终模型性能在容差5mm下的Surface Dice达到0.46±0.09，Dice达到0.47±0.13。与两位测试神经放射学专家相比，模型-专家一致性不劣于专家间一致性，p < 0.05。该CNN能够准确勾画急性缺血性卒中患者NCCT上的低密度缺血核心，其准确性与神经放射学专家相当。
英文摘要：
To determine if a convolutional neural network (CNN) deep learning model can accurately segment acute ischemic changes on non-contrast CT compared to neuroradiologists. Non-contrast CT (NCCT) examinations from 232 acute ischemic stroke patients who were enrolled in the DEFUSE 3 trial were included in this study. Three experienced neuroradiologists independently segmented hypodensity that reflected the ischemic core on each scan. The neuroradiologist with the most experience (expert A) served as the ground truth for deep learning model training. Two additional neuroradiologists (experts B and C) segmentations were used for data testing. The 232 studies were randomly split into training and test sets. The training set was further randomly divided into 5 folds with training and validation sets. A 3-dimensional CNN architecture was trained and optimized to predict the segmentations of expert A from NCCT. The performance of the model was assessed using a set of volume, overlap, and distance metrics using non-inferiority thresholds of 20%, 3ml, and 3mm. The optimized model trained on expert A was compared to test experts B and C. We used a one-sided Wilcoxon signed-rank test to test for the non-inferiority of the model-expert compared to the inter-expert agreement. The final model performance for the ischemic core segmentation task reached a performance of 0.46+-0.09 Surface Dice at Tolerance 5mm and 0.47+-0.13 Dice when trained on expert A. Compared to the two test neuroradiologists the model-expert agreement was non-inferior to the inter-expert agreement, p < 0.05. The CNN accurately delineates the hypodense ischemic core on NCCT in acute ischemic stroke patients with an accuracy comparable to neuroradiologists.
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