基于XGBoost模型和SHAP可视化分析的经颅超声对帕金森病的诊断价值及临床特征的回顾性研究

IF 4.7 2区 医学 Q1 RADIOLOGY, NUCLEAR MEDICINE & MEDICAL IMAGING
Yanan Ge, Xuelei Zhang, Xiaoming Ding, Panpan Zhang, Liyang Su, Gang Wang
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引用次数: 0

摘要

目的:帕金森病(PD)需要早期诊断以获得最佳治疗。本研究旨在评估使用可解释的机器学习模型结合经颅超声(TCS)和临床数据是否能提高诊断准确性。材料与方法:本研究回顾性收集2023年5月至2024年12月31日期间接受TCS治疗的患者资料。使用Boruta算法识别关键的临床和TCS特征。基于这些特征开发了XGBoost模型(一种先进的梯度增强算法),并应用Shapley加性解释(SHAP,一种解释机器学习预测的方法)来可视化它们对PD诊断的贡献。结果:纳入599例患者(训练数据集480例,验证数据集119例),训练数据集和验证数据集的曲线下面积(AUC)分别为0.863和0.811。SHAP分析显示双侧黑质高回声(SNHA)和黑质/中脑比值(S/M)是影响最大的预测因子。结论:通过XGBoost和SHAP将TCS与临床数据相结合,诊断效能高,可解释性清晰,可支持PD的早期诊断。TCS特征与机器学习相结合能否为PD提供可靠的诊断支持?结果结合TCS和临床特征的XGBoost模型具有较高的诊断效能(AUC = 0.811),且SHAP可视化分析结果可解释。该可解释的人工智能模型通过无创成像和常规临床参数支持PD的早期诊断和个性化决策。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Diagnostic value of transcranial ultrasonography and clinical features for Parkinson's disease based on XGBoost model and SHAP visualization analysis: a retrospective study.

Objectives: Parkinson's disease (PD) requires early diagnosis for optimal management. This study aims to evaluate whether combining transcranial ultrasonography (TCS) and clinical data using an interpretable machine learning model improves diagnostic accuracy.

Materials and methods: In this retrospective study, data from patients who underwent TCS between May 2023 and December 31, 2024, were retrospectively collected. Key clinical and TCS features were identified using the Boruta algorithm. An XGBoost model (an advanced gradient boosting algorithm) was developed based on these features, and Shapley Additive Explanations (SHAP, a method for interpreting machine learning predictions) was applied to visualize their contributions to PD diagnosis.

Results: The study included 599 patients (480 training, 119 validation) and achieved area under the curve (AUC) values of 0.863 and 0.811 in training and validation datasets, respectively. SHAP analysis revealed that bilateral substantia nigra hyperechoic (SNHA) and the substantia nigra/midbrain ratio (S/M) were the most influential predictors.

Conclusion: Integrating TCS with clinical data via XGBoost and SHAP provides high diagnostic performance and clear interpretability, supporting early PD diagnosis.

Key points: Question Can TCS features combined with machine learning provide reliable diagnostic support for PD? Findings XGBoost model integrating TCS and clinical features achieved a high diagnostic performance (AUC = 0.811) and interpretable outputs via SHAP visualization analysis. Clinical relevance This interpretable AI model supports early PD diagnosis and individualized decision making using non-invasive imaging and routine clinical parameters.

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来源期刊
European Radiology
European Radiology 医学-核医学
CiteScore
11.60
自引率
8.50%
发文量
874
审稿时长
2-4 weeks
期刊介绍: European Radiology (ER) continuously updates scientific knowledge in radiology by publication of strong original articles and state-of-the-art reviews written by leading radiologists. A well balanced combination of review articles, original papers, short communications from European radiological congresses and information on society matters makes ER an indispensable source for current information in this field. This is the Journal of the European Society of Radiology, and the official journal of a number of societies. From 2004-2008 supplements to European Radiology were published under its companion, European Radiology Supplements, ISSN 1613-3749.
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