Background

Structured reports have been shown to improve communication between radiologists and providers. However, some radiologists are concerned about resultant decreased workflow efficiency. We tested a machine learning-based algorithm designed to convert unstructured computed tomography pulmonary angiography (CTPA) reports into structured reports.

Methods

A self-supervised convolutional neural network-based algorithm was trained on a dataset of 475 manually structured CTPA reports. Labels for individual statements included “pulmonary arteries,” “lungs and airways,” “pleura,” “mediastinum and lymph nodes,” “cardiovascular,” “soft tissues and bones,” “upper abdomen,” and “lines/tubes.” The algorithm was applied to a test set of 400 unstructured CTPA reports, generating a predicted label for each statement, which was evaluated by two independent observers. Per-statement accuracy was calculated based on strict criteria (algorithm label counted as correct if the statement unequivocally contained content only related to that particular label) and a modified criteria, accounting for problematic statements, including typographical errors, statements that did not fit well into the classification scheme, statements containing content for multiple labels, etc.

Results

Of the 4,157 statements, 3,806 (91.6%) and 3,986 (95.9%) were correctly labeled by the algorithm using strict and modified criteria, respectively, while 274 (6.6%) were problematic for the manual observers to label, the majority of which (n = 173) were due to more than one section being included in one statement.

Conclusion

This algorithm showed high accuracy in converting free-text findings into structured reports, which could improve communication between radiologists and clinicians without loss of productivity and provide more structured data for research/data mining applications.

中文翻译：

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深度学习将非结构化CT肺血管造影报告转换为结构化报告。

背景

已经显示出结构化的报告可以改善放射科医生和提供者之间的沟通。但是，一些放射科医生担心由此导致的工作流程效率降低。我们测试了一种基于机器学习的算法，该算法旨在将非结构化计算机断层扫描肺血管造影（CTPA）报告转换为结构化报告。

方法

在475个手动构造的CTPA报告的数据集上训练了一种基于自我监督的卷积神经网络的算法。个人陈述的标签包括“肺动脉”，“肺和气道”，“胸膜”，“纵隔和淋巴结”，“心血管”，“软组织和骨骼”，“上腹部”和“管线/管”。将该算法应用于400个非结构化CTPA报告的测试集，为每个语句生成预测标签，并由两个独立的观察者进行评估。每个陈述的准确性是根据严格的标准（如果语句明确包含仅与该特定标签相关的内容，则算法标签视为正确）和修改后的标准计算得出的，以解决有问题的语句，包括印刷错误，不太适合的语句分类方案，

结果

在4,157条陈述中，算法分别使用严格和修改后的标准正确标记了3,806（91.6％）和3,986（95.9％），而对于手动观察员进行标记的问题有274（6.6％）有问题，其中大多数（n  = 173）是由于一个语句中包含多个节。

结论

该算法在将自由文本结果转换为结构化报告中显示出很高的准确性，这可以改善放射科医生和临床医生之间的沟通，而不会降低生产力，并为研究/数据挖掘应用程序提供更多的结构化数据。