Centro de Investigación CITIC, Universidade da Coruña, Campus de Elviña, A Coruña, 15071, Spain.
Grupo VARPA, Instituto de Investigación Biomédica de A Coruña (INIBIC), Universidade da Coruña, A Coruña, 15006, Spain.
BMC Med Res Methodol. 2022 Apr 28;22(1):125. doi: 10.1186/s12874-022-01578-w.
The health crisis resulting from the global COVID-19 pandemic highlighted more than ever the need for rapid, reliable and safe methods of diagnosis and monitoring of respiratory diseases. To study pulmonary involvement in detail, one of the most common resources is the use of different lung imaging modalities (like chest radiography) to explore the possible affected areas.
The study of patient characteristics like sex and age in pathologies of this type is crucial for gaining knowledge of the disease and for avoiding biases due to the clear scarcity of data when developing representative systems. In this work, we performed an analysis of these factors in chest X-ray images to identify biases. Specifically, 11 imbalance scenarios were defined with female and male COVID-19 patients present in different proportions for the sex analysis, and 6 scenarios where only one specific age range was used for training for the age factor. In each study, 3 different approaches for automatic COVID-19 screening were used: Normal vs COVID-19, Pneumonia vs COVID-19 and Non-COVID-19 vs COVID-19. The study was validated using two public chest X-ray datasets, allowing a reliable analysis to support the clinical decision-making process.
The results for the sex-related analysis indicate this factor slightly affects the system in the Normal VS COVID-19 and Pneumonia VS COVID-19 approaches, although the identified differences are not relevant enough to worsen considerably the system. Regarding the age-related analysis, this factor was observed to be influencing the system in a more consistent way than the sex factor, as it was present in all considered scenarios. However, this worsening does not represent a major factor, as it is not of great magnitude.
Multiple studies have been conducted in other fields in order to determine if certain patient characteristics such as sex or age influenced these deep learning systems. However, to the best of our knowledge, this study has not been done for COVID-19 despite the urgency and lack of COVID-19 chest x-ray images. The presented results evidenced that the proposed methodology and tested approaches allow a robust and reliable analysis to support the clinical decision-making process in this pandemic scenario.
全球 COVID-19 大流行带来的健康危机前所未有地凸显了对快速、可靠和安全的呼吸道疾病诊断和监测方法的需求。为了详细研究肺部受累情况，最常用的资源之一是使用不同的肺部成像方式（如胸部 X 射线）来探索可能受影响的区域。
研究此类疾病中患者的特征，如性别和年龄，对于了解疾病和避免因开发代表性系统时数据明显不足而导致的偏差至关重要。在这项工作中，我们对胸部 X 射线图像中的这些因素进行了分析，以识别偏差。具体来说，我们定义了 11 种不平衡情况，在性别分析中，COVID-19 男性和女性患者的比例不同；在年龄因素中，仅使用 6 种特定的年龄范围进行训练。在每项研究中，我们使用了 3 种不同的自动 COVID-19 筛查方法：正常与 COVID-19、肺炎与 COVID-19 和非 COVID-19 与 COVID-19。该研究使用了两个公共的胸部 X 射线数据集进行验证，这允许进行可靠的分析，以支持临床决策过程。
性别相关分析的结果表明，该因素在正常与 COVID-19 和肺炎与 COVID-19 方法中对系统略有影响，尽管识别出的差异并不足以显著恶化系统。关于年龄相关分析，与性别因素相比，该因素以更一致的方式影响系统，因为它存在于所有考虑的场景中。然而，这种恶化并不是一个主要因素，因为它的程度不大。
为了确定某些患者特征（如性别或年龄）是否会影响这些深度学习系统，已经在其他领域进行了多项研究。然而，据我们所知，尽管 COVID-19 胸部 X 射线图像短缺且紧迫，但针对 COVID-19 尚未进行这项研究。所提出的结果表明，所提出的方法和测试的方法允许进行稳健可靠的分析，以支持这种大流行情况下的临床决策过程。
