Combining artificial intelligence assisted image segmentation and ultrasound based radiomics for the prediction of carotid plaque stability.

IF 2.9 3区 医学 Q2 RADIOLOGY, NUCLEAR MEDICINE & MEDICAL IMAGING
Jiajia Song, Liwen Zou, Yu Li, Xiaoyin Wang, Junlan Qiu, Kailin Gong
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引用次数: 0

Abstract

Purpose: Utilizing artificial intelligence (AI) technology for the segmentation of plaques on ultrasound images to evaluate the stability of carotid artery plaques and analyze its diagnostic accuracy in differentiating vulnerable plaques from stable ones.

Methods: A retrospective study was conducted on 202 patients with ischemic stroke, who were divided into vulnerable plaque group (85 cases) and stable plaque group (117 cases) based on the results of carotid color Doppler ultrasound examination. From the vulnerable plaque group, 63 cases were randomly selected as the modeling group and 22 cases as the validation group; similarly, from the stable plaque group, 87 cases were randomly selected as the modeling group and 30 cases as the validation group. Based on the ultrasound images of the modeling group, plaques were segmented using artificial intelligence technology, and 1414 radiomics features were extracted. These features were then subjected to dimensionality reduction and feature selection using the least absolute shrinkage and selection operator (LASSO) method. Subsequently, a Support Vector Machine (SVM) model was constructed and validated using the selected features. The sensitivity, specificity, and Area Under the Curve (AUC) of the model were evaluated through the analysis of the receiver operating characteristic (ROC) curve.

Results: A total of 43 radiomics feature parameters were selected by the LASSO method. The training group for the SVM model had an AUC of 89.42% (95% CI: 80.74-98.10%), sensitivity of 79.84%, and specificity of 93.10%, while the validation group had an AUC of 82.73% (95% CI: 71.64-93.81%), sensitivity of 81.82%, and specificity of 80.00%.

Conclusion: The use of artificial intelligence technology for the segmentation of plaques in ultrasound images, coupled with the analysis of radiomics models, can efficiently distinguish the stability of carotid artery plaques, providing a diagnostic basis for the clinical prediction of ischemic stroke.

Clinical trial number: Not applicable.

结合人工智能辅助图像分割和基于超声的放射组学预测颈动脉斑块稳定性。
目的:利用人工智能(AI)技术对超声图像上的斑块进行分割,评估颈动脉斑块的稳定性,并分析其在区分易损斑块和稳定斑块方面的诊断准确性。方法:对202例缺血性脑卒中患者进行回顾性研究,根据颈动脉彩色多普勒超声检查结果分为易损斑块组(85例)和稳定斑块组(117例)。从易损斑块组中随机抽取63例作为建模组,22例作为验证组;同样,从稳定斑块组中随机选取87例作为建模组,30例作为验证组。基于建模组超声图像,利用人工智能技术对斑块进行分割,提取1414个放射组学特征。然后使用最小绝对收缩和选择算子(LASSO)方法对这些特征进行降维和特征选择。随后,利用所选特征构建支持向量机模型并进行验证。通过对受试者工作特征(ROC)曲线的分析,评价模型的敏感性、特异性和曲线下面积(AUC)。结果:LASSO方法共获得43个放射组学特征参数。SVM模型训练组的AUC为89.42% (95% CI: 80.74-98.10%),灵敏度为79.84%,特异性为93.10%;验证组的AUC为82.73% (95% CI: 71.64-93.81%),灵敏度为81.82%,特异性为80.00%。结论:利用人工智能技术对超声图像中的斑块进行分割,结合放射组学模型分析,可有效区分颈动脉斑块的稳定性,为缺血性脑卒中的临床预测提供诊断依据。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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