Deep learning-based arterial waveform analysis for predicting postoperative cerebrovascular events in pediatric patients with Moyamoya disease.

Overview

1. Department of Anesthesiology and Pain Medicine, Seoul National University Hospital, College of Medicine, Seoul National University, Republic of Korea
2. Department of Transdisciplinary Medicine, Seoul National University Hospital, Republic of Korea
3. Interdisciplinary Program in Bio-engineering, Seoul National University, Republic of Korea
4. Department of Applied Bio-engineering, Seoul National University, Republic of Korea
5. Department of Medical Informatics, Keimyung University School of Medicine, Republic of Korea
6. Department of Neurosurgery, Seoul National University Hospital, College of Medicine, Seoul National University, Republic of Korea
7. Interdisciplinary Program in Artificial Intelligence, Seoul National University, Republic of Korea
8. Department of Medicine, College of Medicine, Seoul National University, Republic of Korea
9. Innovative Medical Technology Research Institute, Seoul National University Hospital, Seoul, Republic of Korea

Abstract

Background: Postoperative cerebrovascular events, including transient ischemic attacks, infarctions, and hemorrhages, remain a significant concern in pediatric patients with Moyamoya disease (MMD)undergoing surgical revascularization. This study aimed to develop an explainable deep learning-based classification model using intraoperative arterial blood pressure (ABP) waveform analysis for postoperative cerebrovascular events in pediatric patients undergoing surgery for MMD, with exploratory analysis of associated waveform-derived physiologic features.

Methods: This retrospective study included 181 pediatric patients (≤18 years) who underwent revascularization surgery for MMD, with an independent temporal holdout cohort of 79 patients reserved for validation. ABP signals were preprocessed using detrending, pulse segmentation, and normalization, then converted into image representations for deep learning classification. Various convolutional neural network (CNN) models, including ResNet50, ResNet34, DenseNet121, VGG16, and VGG19, were evaluated against Vision Transformer (ViT) architectures. Multiple image transformation methods were tested, and Grad-CAM analysis and statistical comparisons of waveform-derived physiologic features were conducted between patients with and without postoperative cerebrovascular events.

Results: The optimal model configuration achieved the best performance using raw pulse waveforms with three consecutive pulses per image. CNN-based models outperformed ViT-based models, with the highest internal classification performance observed using raw pulse waveforms (AUROC = 0.772, SD = 0.070).In the independent temporal validation cohort, the model achieved an AUROC of 0.738 ± 0.011 at the patient level. Grad-CAM visualization highlighted the diastolic runoff phase as a region of interest for classification. Four waveform-derived features related to arterial compliance were significantly associated with postoperative cerebrovascular events (p < 0.05).

Conclusions: In this study, CNN-based deep learning models demonstrated the feasibility of predicting postoperative cerebrovascular events from intraoperative ABP waveforms, with diastolic runoff dynamics emerging as a potentially relevant physiologic pattern. These findings are exploratory and require prospective multi-center validation before clinical application.

Reproduced under the paper's license (CC BY), from the paper cited above.

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Data Availability

The datasets generated and/or analyzed during the current study are not publicly available due to the presence of potentially identifiable patient information. Data are available upon reasonable request and with approval from the Seoul National University Hospital Institutional Review Board (IRB). Researchers who meet the criteria for access to confidential data may contact the Seoul National University Hospital IRB at . The source code used in this study, including model architecture and training pipeline, is publicly available at https://github.com/Medical-Vision-Lab/Aline_analysis.

Reproduced under the paper's license (CC BY), from the paper cited above.

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Recorded: type, language, journal, volume, issue, pages, dates, 10 authors, 12 MeSH terms, 29 references.

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This paper

Park, J.-B., Shin, Y., Kim, J., Kim, Y. J., Lee, S.-B., Kim, E.-H., Kim, J. W., Kim, S.-K., Kim, H.-S., & Kim, Y.-G. (2026). Deep learning-based arterial waveform analysis for predicting postoperative cerebrovascular events in pediatric patients with Moyamoya disease. PloS one, 21(6), e0350637. https://doi.org/10.1371/journal.pone.0350637

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