利用基于深度学习的人工智能技术自动评估急性缺血性脑卒中多期 CTA 的侧支状态。

BACKGROUND: Collateral status is an important predictor for the outcome of acute ischemic stroke with large vessel occlusion. Multiphase computed-tomography angiography (mCTA) is useful to evaluate the collateral status, but visual evaluation of this examination is time-consuming. This study aims to use an artificial intelligence (AI) technique to develop an automatic AI prediction model for the collateral status of mCTA. METHODS: This retrospective study enrolled subjects with acute ischemic stroke receiving endovascular thrombectomy between January 2015 and June 2020 in a tertiary referral hospital. The demographic data and images of mCTA were collected. The collateral status of all mCTA was visually evaluated. Images at the basal ganglion and supraganglion levels of mCTA were selected to produce AI models using the convolutional neural network (CNN) technique to automatically predict the collateral status of mCTA. RESULTS: A total of 82 subjects were enrolled. There were 57 cases randomly selected for the training group and 25 cases for the validation group. In the training group, there were 40 cases with a positive collateral result (good or intermediate) and 17 cases with a negative collateral result (poor). In the validation group, there were 21 cases with a positive collateral result and 4 cases with a negative collateral result. During training for the CNN prediction model, the accuracy of the training group could reach 0.999 ± 0.015, whereas the prediction model had a performance of 0.746 ± 0.008 accuracy on the validation group. The area under the ROC curve was 0.7. CONCLUSIONS: This study suggests that the application of the AI model derived from mCTA images to automatically evaluate the collateral status is feasible.

背景：侧支循环状态是评估急性大脑中动脉闭塞性卒中患者血管再通后结局的重要预测因素。多期 CT 血管造影（mCTA）可用于评估侧支循环状态，但该检查的视觉评估耗时较长。本研究旨在使用人工智能（AI）技术开发一种基于 mCTA 的自动 AI 预测模型来预测侧支循环状态。

方法：本回顾性研究纳入了 2015 年 1 月至 2020 年 6 月在一家三级转诊医院接受血管内血栓切除术治疗的急性缺血性卒中患者。收集了患者的人口统计学数据和 mCTA 图像。对所有 mCTA 的侧支循环状态进行了视觉评估。使用卷积神经网络（CNN）技术，从 mCTA 的基底节和基底节以上水平选择图像来制作 AI 模型，以自动预测 mCTA 的侧支循环状态。

结果：共纳入 82 例患者。其中 57 例被随机分配到训练组，25 例分配到验证组。在训练组中，40 例侧支循环结果为阳性（良好或中等），17 例为阴性（差）。在验证组中，21 例侧支循环结果为阳性，4 例为阴性。在 CNN 预测模型的训练过程中，训练组的准确率可达 0.999±0.015，而预测模型在验证组的准确率为 0.746±0.008。ROC 曲线下面积为 0.7。

结论：本研究表明，基于 mCTA 图像的 AI 模型在自动评估侧支循环状态方面具有一定的可行性。