PURPOSE Basal/acetazolamide brain perfusion single-photon emission computed tomography (SPECT) has been used to evaluate functional hemodynamics in patients with carotid artery stenosis. We aimed to develop a deep learning model as a support system for interpreting brain perfusion SPECT leveraging unstructured text reports. METHODS In total, 7345 basal/acetazolamide brain perfusion SPECT images and their text reports were retrospectively collected. A long short-term memory (LSTM) network was trained using 500 randomly selected text reports to predict manually labeled structured information, including abnormalities of basal perfusion and vascular reserve for each vascular territory. Using this trained LSTM model, we extracted structured information from the remaining 6845 text reports to develop a deep learning model for interpreting SPECT images. The model was based on a 3D convolutional neural network (CNN), and the performance was tested on the other 500 cases by measuring the area under the receiver-operating characteristic curve (AUC). We then applied the model to patients who underwent revascularization (n = 33) to compare the estimated output of the CNN model for pre- and post-revascularization SPECT and clinical outcomes. RESULTS The AUC of the LSTM model for extracting structured labels was 1.00 for basal perfusion and 0.99 for vascular reserve for all 9 brain regions. The AUC of the CNN model designed to identify abnormal perfusion was 0.83 for basal perfusion and 0.89 for vascular reserve. The output of the CNN model was significantly improved according to the revascularization in the target vascular territory, and its changes in brain territories were concordant with clinical outcomes. CONCLUSION We developed a deep learning model to support the interpretation of brain perfusion SPECT by converting unstructured text reports into structured labels. This model can be used as a support system not only to identify perfusion abnormalities but also to provide quantitative scores of abnormalities, particularly for patients who require revascularization.

中文翻译：

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基于非结构化阅读报告的基于深度学习的基础/乙酰唑胺脑灌注SPECT解释。

目的基础/乙酰唑胺脑灌注单光子发射计算机断层扫描（SPECT）已用于评估颈动脉狭窄患者的功能血流动力学。我们旨在开发深度学习模型作为支持系统，以利用非结构化文本报告来解释脑灌注SPECT。方法回顾性收集了7345例基础/乙酰唑胺脑灌注SPECT图像及其文本报告。使用500个随机选择的文本报告训练了一个长期短期记忆（LSTM）网络，以预测人工标记的结构化信息，包括基础灌注异常和每个血管区域的血管储备。使用此训练有素的LSTM模型，我们从其余6845个文本报告中提取了结构化信息，以开发用于解释SPECT图像的深度学习模型。该模型基于3D卷积神经网络（CNN），并通过测量接收器工作特征曲线（AUC）下的面积在其他500种情况下测试了性能。然后，我们将该模型应用于接受血运重建的患者（n = 33），以比较血运重建前后SPECT和临床结果的CNN模型的估计输出。结果LSTM模型用于提取结构标记的AUC在所有9个大脑区域的基础灌注为1.00，血管储备为0.99。用于识别异常灌注的CNN模型的AUC对于基础灌注为0.83，对于血管储备为0.89。CNN模型的输出根据目标血管区域的血运重建而显着提高，而且其脑区的变化与临床结果一致。结论我们开发了一种深度学习模型，通过将非结构化文本报告转换为结构化标签来支持脑灌注SPECT的解释。该模型不仅可以用作识别灌注异常的支持系统，而且可以提供定量的异常评分，特别是对于需要血管重建的患者。