Retrospective Study
Copyright ©The Author(s) 2024. Published by Baishideng Publishing Group Inc. All rights reserved.
World J Radiol. Aug 28, 2024; 16(8): 329-336
Published online Aug 28, 2024. doi: 10.4329/wjr.v16.i8.329
Artificial intelligence software for assessing brain ischemic penumbra/core infarction on computed tomography perfusion: A real-world accuracy study
Zhu-Qin Li, Wu Liu, Wei-Liang Luo, Su-Qin Chen, Yu-Ping Deng
Zhu-Qin Li, Wu Liu, Wei-Liang Luo, Su-Qin Chen, Yu-Ping Deng, Department of Neurology, Huizhou Central People’s Hospital, Huizhou 516001, Guangdong Province, China
Co-first authors: Zhu-Qin Li and Wu Liu.
Co-corresponding authors: Wei-Liang Luo and Su-Qin Chen.
Author contributions: Li ZQ was responsible for patient screening, enrollment, data analysis and prepared the first draft of the manuscript; Liu W contributed to the collection of clinical data, supervised the mechanical thrombectomy and imaging analysis; Both authors have made crucial and indispensable contributions towards the completion of the project and thus qualified as the co-first authors of the paper. Both Luo WL and Chen SQ have played important and indispensable roles in the experimental design, data interpretation and manuscript preparation as the co-corresponding authors. Luo WL conceptualized, designed, and supervised the whole process of the project. Chen SQ searched the literature, revised the manuscript, edited images and submitted the manuscript; Deng YP participated in mechanical thrombectomy surgery; All the authors have read and approved the final version of the manuscript.
Institutional review board statement: The studies involving human participants were reviewed and approved by the Institutional Review Board of Huizhou Central People’s Hospital and Guangdong Medical University, No. kyll2022037.
Informed consent statement: The patients/participants provided written informed consent to participate in this study.
Conflict-of-interest statement: The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.
Data sharing statement: The anonymized data used to support the findings of this study are available from the corresponding author upon request at lwl306@126.com.
Open-Access: This article is an open-access article that was selected by an in-house editor and fully peer-reviewed by external reviewers. It is distributed in accordance with the Creative Commons Attribution NonCommercial (CC BY-NC 4.0) license, which permits others to distribute, remix, adapt, build upon this work non-commercially, and license their derivative works on different terms, provided the original work is properly cited and the use is non-commercial. See: https://creativecommons.org/licenses/by-nc/4.0/
Corresponding author: Wei-Liang Luo, MD, Chief Doctor, Department of Neurology, Huizhou Central People’s Hospital, No. 41 Eling North Road, Huizhou 516001, Guangdong Province, China. lwl306@126.com
Received: January 30, 2024
Revised: July 22, 2024
Accepted: August 5, 2024
Published online: August 28, 2024
Processing time: 211 Days and 1.4 Hours
Abstract
BACKGROUND

With the increasingly extensive application of artificial intelligence (AI) in medical systems, the accuracy of AI in medical diagnosis in the real world deserves attention and objective evaluation.

AIM

To investigate the accuracy of AI diagnostic software (Shukun) in assessing ischemic penumbra/core infarction in acute ischemic stroke patients due to large vessel occlusion.

METHODS

From November 2021 to March 2022, consecutive acute stroke patients with large vessel occlusion who underwent mechanical thrombectomy (MT) post-Shukun AI penumbra assessment were included. Computed tomography angiography (CTA) and perfusion exams were analyzed by AI, reviewed by senior neurointerventional experts. In the case of divergences among the three experts, discussions were held to reach a final conclusion. When the results of AI were inconsistent with the neurointerventional experts’ diagnosis, the diagnosis by AI was considered inaccurate.

RESULTS

A total of 22 patients were included in the study. The vascular recanalization rate was 90.9%, and 63.6% of patients had modified Rankin scale scores of 0-2 at the 3-month follow-up. The computed tomography (CT) perfusion diagnosis by Shukun (AI) was confirmed to be invalid in 3 patients (inaccuracy rate: 13.6%).

CONCLUSION

AI (Shukun) has limits in assessing ischemic penumbra. Integrating clinical and imaging data (CT, CTA, and even magnetic resonance imaging) is crucial for MT decision-making.

Keywords: Artificial intelligence; Acute ischemic stroke; Penumbra; Core infarction; Computed tomography perfusion

Core Tip: Shukun CerebralDoc system is a Chinese artificial intelligence (AI) post-processing software for computed tomography angiography (CTA) and cerebral perfusion. It is currently the most widely used vascular post-processing AI software in hospitals in mainland China. There is still a lack of relevant laws or regulations in China regarding the clinical application of AI. We found that Shukun AI computed tomography (CT) perfusion imaging has certain limitations in assessing the ischemic penumbra with an inaccuracy rate of 13.6%. We highlight that comprehensive evaluation of the ischemic penumbra in combination with the clinical symptoms, signs, and imaging findings (CT, CTA, and even magnetic resonance imaging), rather than totally relying on AI results, is a key imperative prior to decision-making for mechanical thrombectomy.