Tang Yuqi, Hu Sixian, Xu Yipeng, Wang Linjia, Fang Yu, Yu Pei, Liu Yaning, Shi Jiangwei, Guan Junwen, Zhao Ling
School of Acu-Mox and Tuina, Chengdu University of Traditional Chinese Medicine, Chengdu, 610075, China.
National Clinical Research Center for Chinese Medicine Acupuncture and Moxibustion, First Teaching Hospital of Tianjin University of Traditional Chinese Medicine, Tianjin, 300074, China.
Chin Med. 2024 Nov 9;19(1):155. doi: 10.1186/s13020-024-01026-5.
This study aimed to employ machine learning techniques to predict the clinical efficacy of acupuncture as an intervention for patients with upper limb motor dysfunction following ischemic stroke, as well as to assess its potential utility in clinical practice.
Medical records and digital subtraction angiography (DSA) imaging data were collected from 735 ischemic stroke patients with upper limb motor dysfunction who were treated with standardized acupuncture at two hospitals. Following the initial screening, 314 patient datasets that met the inclusion criteria were selected. We applied three deep-learning algorithms (YOLOX, FasterRCNN, and TOOD) to develop the object detection model. Object detection results pertaining to the cerebral vessels were integrated into a clinical efficacy prediction model (random forest). This model aimed to classify patient responses to acupuncture treatment. Finally, the accuracies and discriminative capabilities of the prediction models were assessed.
The object detection model achieved an optimal recognition rate, The mean average precisions of YOLOX, TOOD, and FasterRCNN were 0.61, 0.7, and 0.68, respectively. The prediction accuracy of the clinical efficacy model reached 93.6%, with all three-treatment response classification area under the curves (AUCs) exceeding 0.95. Feature extraction using the prediction model highlighted the significant influence of various cerebral vascular stenosis sites within the internal carotid artery (ICA) on prediction outcomes. Specifically, the initial and C1 segments of the ICA had the highest predictive weights among all stenosis sites. Additionally, stenosis of the middle cerebral, anterior cerebral, and posterior cerebral arteries exerted a notable influence on the predictions. In contrast, the stenosis sites within the vertebral artery exhibited minimal impact on the model's predictive abilities.
Results underscore the substantial predictive influence of each cerebral vascular stenosis site within the ICA, with the initial and C1 segments being pivotal predictors.
本研究旨在运用机器学习技术预测针刺治疗缺血性脑卒中后上肢运动功能障碍患者的临床疗效，并评估其在临床实践中的潜在应用价值。
收集了两家医院735例接受标准化针刺治疗的缺血性脑卒中上肢运动功能障碍患者的病历和数字减影血管造影（DSA）影像数据。经过初步筛选，选取了314例符合纳入标准的患者数据集。我们应用三种深度学习算法（YOLOX、FasterRCNN和TOOD）来开发目标检测模型。将与脑血管相关的目标检测结果整合到临床疗效预测模型（随机森林）中。该模型旨在对患者对针刺治疗的反应进行分类。最后，评估了预测模型的准确性和判别能力。
目标检测模型达到了最佳识别率，YOLOX、TOOD和FasterRCNN的平均精度均值分别为0.61、0.7和0.68。临床疗效模型的预测准确率达到93.6%，所有三种治疗反应分类曲线下面积（AUC）均超过0.95。使用预测模型进行特征提取突出了颈内动脉（ICA）内不同脑血管狭窄部位对预测结果的显著影响。具体而言，ICA的起始段和C1段在所有狭窄部位中具有最高的预测权重。此外，大脑中动脉、大脑前动脉和大脑后动脉的狭窄对预测有显著影响。相比之下，椎动脉内的狭窄部位对模型的预测能力影响最小。
结果强调了ICA内每个脑血管狭窄部位的显著预测影响，起始段和C1段是关键预测因素。
