Department of Radiology, Hospital Israelita Albert Einstein, Sao Paulo, Brazil.
Institute of Informatics (INF), Federal University of Goias, Goiania, Brazil.
PLoS One. 2024 Aug 21;19(8):e0305839. doi: 10.1371/journal.pone.0305839. eCollection 2024.
This paper presents an artificial intelligence-based classification model for the detection of pulmonary embolism in computed tomography angiography. The proposed model, developed from public data and validated on a large dataset from a tertiary hospital, uses a two-dimensional approach that integrates temporal series to classify each slice of the examination and make predictions at both slice and examination levels. The training process consists of two stages: first using a convolutional neural network InceptionResNet V2 and then a recurrent neural network long short-term memory model. This approach achieved an accuracy of 93% at the slice level and 77% at the examination level. External validation using a hospital dataset resulted in a precision of 86% for positive pulmonary embolism cases and 69% for negative pulmonary embolism cases. Notably, the model excels in excluding pulmonary embolism, achieving a precision of 73% and a recall of 82%, emphasizing its clinical value in reducing unnecessary interventions. In addition, the diverse demographic distribution in the validation dataset strengthens the model's generalizability. Overall, this model offers promising potential for accurate detection and exclusion of pulmonary embolism, potentially streamlining diagnosis and improving patient outcomes.
这篇论文提出了一种基于人工智能的分类模型，用于在计算机断层血管造影（CTA）中检测肺栓塞。该模型基于公共数据开发，并在一家三甲医院的大型数据集上进行验证，采用二维方法，整合时间序列对检查的每一层切片进行分类，并在切片和检查层面进行预测。训练过程分为两个阶段：首先使用卷积神经网络 InceptionResNet V2，然后是递归神经网络长短时记忆模型。该方法在切片层面的准确率达到 93%，在检查层面的准确率达到 77%。使用医院数据集进行外部验证时，对阳性肺栓塞病例的准确率为 86%，对阴性肺栓塞病例的准确率为 69%。值得注意的是，该模型在排除肺栓塞方面表现出色，准确率为 73%，召回率为 82%，强调了其在减少不必要干预方面的临床价值。此外，验证数据集中多样化的人口统计学分布增强了模型的泛化能力。总体而言，该模型具有准确检测和排除肺栓塞的巨大潜力，可能会简化诊断并改善患者预后。
