Lung size measurements play an important role in transplantation, as optimal donor-recipient size matching is necessary to ensure the best possible outcome. Although several strategies for size matching are currently used, all have limitations, and none has proven superior. In this pilot study, we leveraged deep learning and computer vision to develop an automated system for generating standardized lung size measurements using portable chest radiographs to improve accuracy, reduce variability, and streamline donor/recipient matching. We developed a 2-step framework involving lung mask extraction from chest radiographs followed by feature point detection to generate 6 distinct lung height and width measurements, which we validated against measurements reported by 2 radiologists (T.A. and W.B.G.) for 50 lung transplant recipients. Our system demonstrated <2.5% error (<7.0 mm) with robust interrater and intrarater agreement compared with an expert radiologist review. This is especially promising given that the radiographs used in this study were purposely chosen to include images with technical challenges such as consolidations, effusions, and patient rotation. Although validation in a larger cohort is necessary, this study highlights artificial intelligence’s potential to both provide reproducible lung size assessment in real patients and enable studies on the effect of lung size matching on transplant outcomes in large data sets.

中文翻译：

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人工智能驱动的胸片自动肺量测


肺大小测量在移植中起着重要作用，因为最佳的供体-受体大小匹配对于确保最佳结果是必要的。尽管目前使用了几种大小匹配策略，但所有策略都有局限性，并且没有一种策略被证明是更好的。在这项试点研究中，我们利用深度学习和计算机视觉开发了一个自动化系统，使用便携式胸片生成标准化的肺大小测量值，以提高准确性、减少变异性并简化供体/受体匹配。我们开发了一个 2 步框架，包括从胸片中提取肺面罩，然后进行特征点检测以生成 6 个不同的肺高度和宽度测量值，我们根据 2 名放射科医生 （TA 和 WBG） 报告的 50 名肺移植受者的测量值进行了验证。与放射科医生专家审查相比，我们的系统显示 <2.5% 误差 （<7.0 mm） 具有稳健的评分者和评分者内一致性。鉴于本研究中使用的 X 光片是特意选择的，以包括具有技术挑战的图像，例如实变、积液和患者旋转，这一点尤其有希望。尽管在更大的队列中进行验证是必要的，但这项研究强调了人工智能的潜力，它既可以在真实患者中提供可重复的肺大小评估，又可以在大型数据集中研究肺大小匹配对移植结果的影响。