Clinical efficacy of DSA-based features in predicting outcomes of acupuncture intervention on upper limb dysfunction following ischemic stroke

• Published in: Chinese medicine Vol. 19; no. 1; pp. 155 - 13
• Main Authors: Tang, Yuqi, Hu, Sixian, Xu, Yipeng, Wang, Linjia, Fang, Yu, Yu, Pei, Liu, Yaning, Shi, Jiangwei, Guan, Junwen, Zhao, Ling
• Format: Journal Article
• Language: English
• Published: London BioMed Central Ltd 09.11.2024; BioMed Central; BMC
• Subjects: Acupuncture; Data mining; DSA; Ischemia; Machine learning; Medical imaging equipment; Object detection; Predictive model; Prognosis; Stroke; Stroke patients; Upper limb dysfunction
• ISSN: 1749-8546; 1749-8546
• DOI: 10.1186/s13020-024-01026-5

Background and objectives 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. Methods 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. Results 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. Conclusions Results underscore the substantial predictive influence of each cerebral vascular stenosis site within the ICA, with the initial and C1 segments being pivotal predictors. Keywords: Stroke, Machine learning, DSA, Object detection, Predictive model, Upper limb dysfunction