两部分内容：其一为声带状态对照图，以并置方式呈现正常声带与息肉、结节、单侧麻痹等病变示意（图内标注为英文）；其二为约 5 秒的发声主题静音动画。相关综述讨论器质性病变的声学特征与人工智能在嗓音研究中的应用，不代表本站提供诊断功能。
图背后的故事
目的：良性或恶性器质性声带病变引起的嗓音障碍通常缺乏可靠的无创诊断工具，可能造成治疗延误。研究旨在识别具有区分能力的声学标志物，建立用于病变分类与恶性预测的机器学习模型；比较器质性声带病变患者中良性与恶性病例的发声特征，并评估模型区分健康与病理嗓音的潜力。方法：共纳入 157 名参与者，包括 127 名器质性声带病变患者（109 例良性、18 例恶性）及 30 名健康对照。研究对元音进行声学分析，评估声带振动参数，训练 XGBoost、LightGBM 模型进行病变分类与恶性预测，并通过受试者工作特征分析识别关键参数。结果：健康组与病变组之间有 63 项声学参数差异具有统计学意义，偏度与峰度的区分能力尤其突出。六项参数（/u/skew、/i/skew、/o/kurt、/o/shapefactor、/o/impulsefactor、/a/peak2valley）对区分良恶性病变具有较高诊断价值。XGBoost 在声带病变分类中表现最佳，曲线下面积为 0.735；LightGBM 在恶性预测中表现最佳，曲线下面积为 0.924。年龄与特定声学参数是模型中的显著预测因素。结论：声学分析结合机器学习可提高声带病变的诊断准确性，尤其有助于区分良性与恶性病例。研究显示人工智能辅助嗓音分析有望成为早期发现及临床决策的无创工具；仍需更大队列验证，以完善算法并支持更广泛的临床应用。
论文摘要（中文译文 · 原文）
中文译文 摘要 目的 器质性声带病变（无论良性或恶性）所致嗓音障碍常缺乏可靠的无创诊断工具，可能导致治疗延误。本研究旨在识别有鉴别意义的声学生物标志物，并建立用于病变准确分类与恶性预测的机器学习模型：研究器质性声带病变患者嗓音产生的声学特征、比较良性与恶性病例，并评估机器学习模型区分健康与病理嗓音的诊断潜力。方法 共纳入157名受试者，包括127名器质性声带病变患者（良性109例、恶性18例）与30名健康对照。对元音进行声学分析，评估声带振动参数；训练机器学习模型（极限梯度提升 XGBoost、轻量梯度提升机 LightGBM）对病变类型分类并预测恶性；以受试者工作特征（ROC）分析确定关键诊断参数。结果 比较分析显示健康组与病变组间有63项声学参数差异具有统计学意义，其中偏度与峰度的鉴别力尤为突出；六个关键参数（/u/偏度、/i/偏度、/o/峰度、/o/波形因子、/o/脉冲因子与/a/峰谷值）在区分良恶性病变中具有较高诊断价值。XGBoost 模型在声带病变分类中表现最佳（曲线下面积 AUC = 0.735），LightGBM 在恶性预测中表现突出（AUC = 0.924）；年龄与特定声学参数是模型中的重要预测因子。结论 声学分析与机器学习的结合显著提高了声带病变的诊断准确性，尤其在良恶性鉴别方面；这些发现凸显了人工智能（AI）辅助嗓音分析作为早期检出与临床决策无创工具的潜力，仍需在更大规模人群中进一步验证，以完善预测算法、支持更广的临床应用。关键词：声带病变；声学分析；客观评估；机器学习模型；嗓音障碍诊断
Abstract Objective Voice disorders resulting from organic vocal cord lesions, whether benign or malignant, often lack reliable non-invasive diagnostic tools, which can lead to delays in treatment. This study aims to identify distinctive acoustic biomarkers and develop machine learning models for accurate classification of these lesions and prediction of malignancy. We investigated the acoustic characteristics of voice production in patients with organic vocal cord lesions, comparing benign and malignant cases, and evaluated the diagnostic potential of machine learning models in distinguishing between healthy and pathological voices. Methods A total of 157 participants were enrolled, including 127 patients with organic vocal cord lesions (109 benign, 18 malignant) and 30 healthy controls. Acoustic analysis was performed on vowel sounds, assessing vocal fold vibration parameters. Machine learning models (eXtreme Gradient Boosting (XGBoost), Light Gradient-Boosting Machine (LightGBM)) were trained to classify lesion types and predict malignancy. Receiver operating characteristic analysis identified key diagnostic parameters. Results Comparative analysis revealed 63 statistically significant differences in acoustic parameters between healthy and lesion-affected groups, with skewness and kurtosis being particularly discriminative. Six key parameters (/u/skew, /i/skew, /o/kurt, /o/shapefactor, /o/impulsefactor, and /a/peak2valley) demonstrated high diagnostic value in distinguishing benign from malignant lesions. The XGBoost model achieved the best performance in classifying vocal cord lesions (area under the curve (AUC) = 0.735), while LightGBM excelled in malignancy prediction (AUC = 0.924). Age and specific acoustic parameters were significant predictors in the models. Conclusion The integration of acoustic analysis with machine learning significantly enhances the diagnostic accuracy for vocal cord lesions, particularly in differentiating between benign and malignant cases. These findings underscore the potential of artificial intelligence (AI)-assisted voice analysis as a non-invasive tool for early detection and clinical decision-making. Further validation in larger cohorts is necessary to refine predictive algorithms for broader clinical application. Keywords: Vocal cord lesions, acoustic analysis, objective evaluation, machine learning models, voice disorder diagnostics