Abstract：
OBJECTIVE: CT pulmonary angiography is the gold standard for diagnosing pulmonary embolism, and DL algorithms are being developed to manage the increase in demand. The nnU-Net is a new auto-adaptive DL framework that minimizes manual tuning, making it easier to develop effective algorithms for medical imaging even without specific expertise. This study assesses the performance of a locally developed nnU-Net algorithm on the RSPECT dataset for PE detection, clot volume measurement, and correlation with right ventricle overload.
METHODS: User input was limited to segmentation using 3DSlicer. We worked with the RSPECT dataset and trained an algorithm from 205 PE and 340 negatives. The test dataset comprised 6573 exams. Performance was tested against PE characteristics, such as central, non-central, and RV overload. Blood clot volume (BCV) was extracted from each exam. We employed ROC curves and logistic regression for statistical validation.
RESULTS: Negative studies had a median BCV of 1 μL, which increased to 345 μL in PE-positive cases and 7,378 μL in central PEs. Statistical analysis confirmed a significant BCV correlation with PE presence, central PE, and increased RV/LV ratio (p < 0.0001). The model\'s AUC for PE detection was 0.865, with an 83 % accuracy at a 55 μL threshold. Central PE detection AUC was 0.937 with 91 % accuracy at 850 μL. The RV overload AUC stood at 0.848 with 79 % accuracy.
CONCLUSIONS: The nnU-Net algorithm demonstrated accurate PE detection, particularly for central PE. BCV is an accurate metric for automated severity stratification and case prioritization.
CONCLUSIONS: The nnU-Net framework can be utilized to create a dependable DL for detecting PE. It offers a user-friendly approach to those lacking expertise in AI and rapidly extracts the Blood Clot Volume, a metric that can evaluate the PE\'s severity.
METHODS: User input was limited to segmentation using 3DSlicer. We worked with the RSPECT dataset and trained an algorithm from 205 PE and 340 negatives. The test dataset comprised 6573 exams. Performance was tested against PE characteristics, such as central, non-central, and RV overload. Blood clot volume (BCV) was extracted from each exam. We employed ROC curves and logistic regression for statistical validation.
RESULTS: Negative studies had a median BCV of 1 μL, which increased to 345 μL in PE-positive cases and 7,378 μL in central PEs. Statistical analysis confirmed a significant BCV correlation with PE presence, central PE, and increased RV/LV ratio (p < 0.0001). The model\'s AUC for PE detection was 0.865, with an 83 % accuracy at a 55 μL threshold. Central PE detection AUC was 0.937 with 91 % accuracy at 850 μL. The RV overload AUC stood at 0.848 with 79 % accuracy.
CONCLUSIONS: The nnU-Net algorithm demonstrated accurate PE detection, particularly for central PE. BCV is an accurate metric for automated severity stratification and case prioritization.
CONCLUSIONS: The nnU-Net framework can be utilized to create a dependable DL for detecting PE. It offers a user-friendly approach to those lacking expertise in AI and rapidly extracts the Blood Clot Volume, a metric that can evaluate the PE\'s severity.
摘要：
目的:CT肺动脉造影是诊断肺栓塞的金标准,正在开发DL算法来管理需求的增长。nnU-Net是一种新的自适应DL框架，可最大限度地减少手动调谐，即使没有特定的专业知识，也可以更轻松地开发有效的医学成像算法。本研究评估了在RSPECT数据集上本地开发的nnU-Net算法用于PE检测的性能，凝块体积测量,与右心室超负荷有关。
方法：用户输入仅限于使用3DSlicer进行分割。我们使用RSPECT数据集，并从205个PE和340个阴性中训练了一个算法。测试数据集包括6573项检查。针对PE特性测试了性能，如中央，非中心,RV过载。从每次检查中提取血凝块体积(BCV)。我们采用ROC曲线和逻辑回归进行统计验证。
结果：阴性研究的中位BCV为1μL，在PE阳性病例中增加到345μL，在中央PE中增加到7,378μL。统计分析证实了BCV与PE存在的显着相关性，中央PE,RV/LV比值增加（p<0.0001）。用于PE检测的模型AUC为0.865,在55μL阈值下具有83%的准确度。中心PE检测AUC为0.937,在850μL时准确度为91%。RV过载AUC为0.848,准确度为79%。
结论：nnU-Net算法证明了准确的PE检测，特别是中央PE。BCV是自动严重性分层和案例优先级排序的准确指标。
结论：nnU-Net框架可用于创建可靠的DL以检测PE。它为那些缺乏人工智能专业知识的人提供了一种用户友好的方法，并快速提取血块体积，可以评估PE严重性的度量。
方法：用户输入仅限于使用3DSlicer进行分割。我们使用RSPECT数据集，并从205个PE和340个阴性中训练了一个算法。测试数据集包括6573项检查。针对PE特性测试了性能，如中央，非中心,RV过载。从每次检查中提取血凝块体积(BCV)。我们采用ROC曲线和逻辑回归进行统计验证。
结果：阴性研究的中位BCV为1μL，在PE阳性病例中增加到345μL，在中央PE中增加到7,378μL。统计分析证实了BCV与PE存在的显着相关性，中央PE,RV/LV比值增加（p<0.0001）。用于PE检测的模型AUC为0.865,在55μL阈值下具有83%的准确度。中心PE检测AUC为0.937,在850μL时准确度为91%。RV过载AUC为0.848,准确度为79%。
结论：nnU-Net算法证明了准确的PE检测，特别是中央PE。BCV是自动严重性分层和案例优先级排序的准确指标。
结论：nnU-Net框架可用于创建可靠的DL以检测PE。它为那些缺乏人工智能专业知识的人提供了一种用户友好的方法，并快速提取血块体积，可以评估PE严重性的度量。
