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ORIGINAL RESEARCH article

Front. Neurol.
Sec. Artificial Intelligence in Neurology
Volume 15 - 2024 | doi: 10.3389/fneur.2024.1441886
This article is part of the Research Topic Clinical Applications of Data-enabled Intelligence in Neurology View all 8 articles

Prediction of clinical efficacy of acupuncture intervention on upper limb dysfunction after ischemic stroke based on machine learning: a study driven by DSA diagnostic reports data

Provisionally accepted
Yaning Liu Yaning Liu 1Yuqi Tang Yuqi Tang 1Zechen Li Zechen Li 2Pei Yu Pei Yu 1Jing Yuan Jing Yuan 1Lichuan Zeng Lichuan Zeng 1Can Wang Can Wang 1Su Li Su Li 2Ling Zhao Ling Zhao 1*
  • 1 Chengdu University of Traditional Chinese Medicine, Chengdu, China
  • 2 Chongqing University, Chongqing, China

The final, formatted version of the article will be published soon.

    Objective: To develop a machine learning-based model for predicting the clinical efficacy of acupuncture intervention in patients with upper limb dysfunction following ischemic stroke, and to assess its potential role in guiding clinical practice.Methods: Data from 1375 ischemic stroke patients with upper limb dysfunction were collected from two hospitals, including medical records and Digital Subtraction Angiography (DSA) reports. All patients received standardized acupuncture treatment.After screening, 616 datasets were selected for analysis. A prediction model was developed using the AutoGluon framework, with three outcome measures as endpoints:the National Institutes of Health Stroke Scale (NIHSS), Fugl-Meyer Assessment for Upper Extremity (FMA-UE), and the Modified Barthel Index (MBI).The prediction model demonstrated high accuracy for the three endpoints, with prediction accuracies of 84.3% for NIHSS, 77.8% for FMA-UE, and 88.1% for MBI. Feature importance analysis identified the M1 segment of the Middle Cerebral Artery (MCA), the origin of the Internal Carotid Artery (ICA), and the C1 segment of the ICA as the most critical factors influencing the model's predictions. Notably, the MBI emerged as the most sensitive outcome measure for evaluating patient response to acupuncture treatment. Additionally, secondary analysis revealed that the number of sites with cerebral vascular stenosis (specifically 1 and 3 sites) had a significant impact on the final model's predictions.This study highlights the M1 segment, the origin of the ICA, and the C1 segment as key stenotic sites affecting acupuncture treatment efficacy in stroke 3 patients with upper limb dysfunction. The MBI was found to be the most responsive outcome measure for evaluating treatment sensitivity in this cohort.

    Keywords: Stroke, machine learning, dsa, AutoGluon, upper limb dysfunction

    Received: 31 May 2024; Accepted: 09 Dec 2024.

    Copyright: © 2024 Liu, Tang, Li, Yu, Yuan, Zeng, Wang, Li and Zhao. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

    * Correspondence: Ling Zhao, Chengdu University of Traditional Chinese Medicine, Chengdu, China

    Disclaimer: All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.