AI post-intervention operational and functional outcomes prediction in ischemic stroke patients using MRIs.

IF 3.2 3区 医学 Q2 RADIOLOGY, NUCLEAR MEDICINE & MEDICAL IMAGING
Emily Wittrup, John Reavey-Cantwell, Aditya S Pandey, Dennis J Rivet Ii, Kayvan Najarian
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引用次数: 0

Abstract

Background: Despite the potential clinical utility for acute ischemic stroke patients, predicting short-term operational outcomes like length of stay (LOS) and long-term functional outcomes such as the 90-day Modified Rankin Scale (mRS) remain a challenge, with limited current clinical guidance on expected patient trajectories. Machine learning approaches have increasingly aimed to bridge this gap, often utilizing admission-based clinical features; yet, the integration of imaging biomarkers remains underexplored, especially regarding whole 2.5D image fusion using advanced deep learning techniques.

Methods: This study introduces a novel method leveraging autoencoders to integrate 2.5D diffusion weighted imaging (DWI) with clinical features for refined outcome prediction.

Results: Results on a comprehensive dataset of AIS patients demonstrate that our autoencoder-based method has comparable performance to traditional convolutional neural networks image fusion methods and clinical data alone (LOS > 8 days: AUC 0.817, AUPRC 0.573, F1-Score 0.552; 90-day mRS > 2: AUC 0.754, AUPRC 0.685, F1-Score 0.626).

Conclusion: This novel integration of imaging and clinical data for post-intervention stroke prognosis has numerous computational and operational advantages over traditional image fusion methods. While further validation of the presented models is necessary before adoption, this approach aims to enhance personalized patient management and operational decision-making in healthcare settings.

Clinical trial number: Not applicable.

应用mri预测缺血性脑卒中患者干预后手术和功能结局。
背景:尽管对急性缺血性脑卒中患者具有潜在的临床应用价值,但预测短期手术结果(如住院时间(LOS))和长期功能结果(如90天修正兰金量表(mRS))仍然是一个挑战,目前对预期患者轨迹的临床指导有限。机器学习方法越来越多地旨在弥合这一差距,通常利用基于入院的临床特征;然而,成像生物标志物的整合仍未得到充分探索,特别是在使用先进的深度学习技术进行全2.5D图像融合方面。方法:本研究引入了一种利用自动编码器将2.5D弥散加权成像(DWI)与临床特征相结合的新方法,以精确预测预后。结果:基于AIS患者综合数据集的结果表明,基于自编码器的方法与传统的卷积神经网络图像融合方法和单独的临床数据具有相当的性能(LOS > 8天:AUC 0.817, AUPRC 0.573, F1-Score 0.552;90天mRS >2: AUC 0.754, AUPRC 0.685, F1-Score 0.626)。结论:与传统的图像融合方法相比,这种新型的脑卒中干预后预后的影像学和临床数据整合具有许多计算和操作优势。虽然在采用之前需要进一步验证所提出的模型,但该方法旨在增强医疗保健环境中的个性化患者管理和操作决策。临床试验号:不适用。
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来源期刊
BMC Medical Imaging
BMC Medical Imaging RADIOLOGY, NUCLEAR MEDICINE & MEDICAL IMAGING-
CiteScore
4.60
自引率
3.70%
发文量
198
审稿时长
27 weeks
期刊介绍: BMC Medical Imaging is an open access journal publishing original peer-reviewed research articles in the development, evaluation, and use of imaging techniques and image processing tools to diagnose and manage disease.
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