Currently, the results of lung sound auscultation with either physical or electronic stethoscopes still rely mainly on the doctor's professional auscultation identification ability, which has not yet been able to realise intelligent diagnosis and interpretation. When patients are affected by lung diseases at home, they are unable to detect lung abnormalities on their own and delay treatment; when they are in the process of rescue and treatment of respiratory infectious diseases, in-ear stethoscopes are easily contaminated and cause nosocomial infections. Although stethoscopic sounds contain a wealth of information about health status, the lack of standardised collection methods, classification criteria and analysis tools has limited the objective analysis and application of stethoscopic sounds in practice. In this study, the data collection, arrangement and database design of the lung auscultation sound were carried out by using the unified auscultation sound collection equipment and process. The study used the software MetlabR2017a for data management and analysis to create a database of lung auscultation sounds in a healthy group and a group of patients with lung disease. A database of lung auscultation sounds was established for healthy groups and groups of patients with lung diseases. A standard set of classification of auscultatory tones, labelling specifications, audio characteristic signal parameters were developed. Building a system for storing, managing and analysing lung auscultation sound data to provide important data support for research related to the screening and monitoring of lung diseases and the translation of medical artificial intelligence applications. The study accumulated the experience of building an audio database of lung auscultation sounds, provided a useful reference for the management and analysis of the audio database, and laied the foundation for supporting the subsequent application of medical artificial intelligence-assisted auscultation in the screening and monitoring of lung diseases, which was of great medical value and practical application.

当前无论是物理听诊器亦或是电子听诊器的肺音听诊结果仍然主要依靠医生专业的听诊鉴别能力，尚未能够实现智能诊断判读。当患者在家受到肺部疾病影响时，无法自行发现肺部异常而耽误治疗；当处于呼吸道传染病救治过程中，入耳式的听诊器容易受到污染而造成院内感染。尽管听诊音包含了丰富的健康状态信息，由于缺乏标准化的采集方法、分类标准和分析工具，使得听诊音的客观分析和应用在实践中受到了限制。本研究通过采用统一的听诊音采集设备和流程进行肺部听诊音数据采集、整理、数据库设计，使用软件MatlabR2017a进行数据管理和分析，建立了健康群体和肺部疾病患者群体的肺部听诊音数据库，制订一套标准的听诊音分类、标注规范、音频特征信号参数，构建一个用于存储、管理和分析肺部听诊音数据的系统，为肺部疾病的筛查、监测以及医学人工智能应用转化等相关研究提供重要的数据支持。本研究为肺部听诊音音频数据库建库积累了经验，为音频类数据库管理和分析提供有益的参考和借鉴，为支持后续医学人工智能辅助听诊应用于肺部疾病筛查与监测奠定基础，具有重要的医学价值和实际应用意义。