标题： 移植可用？ 评估严重肺部疾病患者中的AI肺分割模型 显示英文标题

标题： Transplant-Ready? Evaluating AI Lung Segmentation Models in Candidates with Severe Lung Disease

摘要： 这项研究评估了可用于移植适格患者的公开深度学习肺部分割模型，以确定它们在疾病严重程度水平、病理类别和肺部两侧的表现，并识别影响其在肺移植术前规划中使用局限性。 这项回顾性研究包括了2017年至2019年间在杜克大学健康系统接受胸部CT扫描的32名患者（总共3,645张二维轴向切片）。 根据存在两种或多种不同严重程度的肺部病理情况，选择了标准轴向CT扫描的患者。 使用三种先前开发的深度学习模型进行了肺部分割：Unet-R231、TotalSegmentator、MedSAM。 通过定量指标（体积相似性、Dice相似性系数、Hausdorff距离）和定性指标（四点临床可接受性量表）评估了性能。 总体而言，Unet-R231在不同严重程度水平和病理类别中均优于TotalSegmentator和MedSAM（p<0.05）。 所有模型在从轻度到中度至重度病例中的表现均有显著下降，特别是在体积相似性方面（p<0.05），但在肺部两侧或病理类型之间没有显著差异。 在评估的模型中，Unet-R231提供了最准确的自动肺部分割，TotalSegmentator紧随其后，尽管它们在中度至重度病例中的表现显著下降，强调了在严重病理情况下需要对模型进行专门的微调。 显示英文摘要

摘要： This study evaluates publicly available deep-learning based lung segmentation models in transplant-eligible patients to determine their performance across disease severity levels, pathology categories, and lung sides, and to identify limitations impacting their use in preoperative planning in lung transplantation. This retrospective study included 32 patients who underwent chest CT scans at Duke University Health System between 2017 and 2019 (total of 3,645 2D axial slices). Patients with standard axial CT scans were selected based on the presence of two or more lung pathologies of varying severity. Lung segmentation was performed using three previously developed deep learning models: Unet-R231, TotalSegmentator, MedSAM. Performance was assessed using quantitative metrics (volumetric similarity, Dice similarity coefficient, Hausdorff distance) and a qualitative measure (four-point clinical acceptability scale). Unet-R231 consistently outperformed TotalSegmentator and MedSAM in general, for different severity levels, and pathology categories (p<0.05). All models showed significant performance declines from mild to moderate-to-severe cases, particularly in volumetric similarity (p<0.05), without significant differences among lung sides or pathology types. Unet-R231 provided the most accurate automated lung segmentation among evaluated models with TotalSegmentator being a close second, though their performance declined significantly in moderate-to-severe cases, emphasizing the need for specialized model fine-tuning in severe pathology contexts.