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ORIGINAL RESEARCH article

Front. Neurol.
Sec. Artificial Intelligence in Neurology
Volume 15 - 2024 | doi: 10.3389/fneur.2024.1394435
This article is part of the Research Topic Exploring the Future of Neurology: How AI is Revolutionizing Diagnoses, Treatments, and Beyond View all 8 articles

Identification of middle cerebral artery stenosis in transcranial Doppler using a modified VGG-16

Provisionally accepted
Dong Xu Dong Xu 1*Hao Li Hao Li 2Fanghui Su Fanghui Su 1Sizheng Qiu Sizheng Qiu 1Huixia Tong Huixia Tong 1Meifeng Huang Meifeng Huang 1Jianzhong Yao Jianzhong Yao 1
  • 1 Department of Neuroelectrophysiology，Anyang People's Hospital, Anyang, China
  • 2 Shenzhen Institute for Advanced Study,University of Electronic Science and Technology of China, Shenzhen, China

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

    Objectives: The diagnosis of Intracranial atherosclerotic stenosis (ICAS) is of great significance for the prevention of stroke. Deep learning (DL) -based artificial intelligence techniques may aid in the diagnosis. The aim of this study was to identify ICAS in the middle cerebral artery based on a modified DL model. Methods: This retrospective study included two datasets. Dataset1 was 3068 TCD images of MCA from 1729 patients, which were judged as normal or stenosis by three physicians with different experiences combined with other medical imaging data, and used to improve and train the VGG16 models. Dataset2 was TCD images of 90 physical examination people, which were used to verify the robustness of the model and compare the consistency between the model and human physicians.The accuracy, precision, specificity, sensitivity and AUC of the best model VGG16+SE+SC on dataset1 reached 85.67±0.43(%),87.23±1.17(%),87.73± 1.47(%),83.60±1.60(%),0.857±0.004 and those on dataset2 were 93.70± 2.80(%),62.65±11.27(%),93.00±3.11(%),100.00±0.00(%),0.965±0.016.The kappa coefficient shows that it reaches the recognition level of senior doctors.The improved DL model has good diagnostic effect for MCV stenosis in TCD images, which is expected to provide help in ICAS screening.

    Keywords: transcranial Doppler, ICAs, deep learning, Stroke, screening

    Received: 01 Mar 2024; Accepted: 29 Sep 2024.

    Copyright: © 2024 Xu, Li, Su, Qiu, Tong, Huang and Yao. 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: Dong Xu, Department of Neuroelectrophysiology，Anyang People's Hospital, Anyang, 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.