MOMO-深度学习驱动的外部dicom研究的分类

患者定期在其他设施继续评估或治疗，接受他们以前的影像学研究，并要求新医院的临床工作人员将这些研究导入当地数据库。然而，在不同的设施之间，命名法、内容，甚至医疗程序的标准可能会有所不同，往往需要人工干预，以在接受者医院的标准的背景下对所接受的研究进行准确的分类。在本研究中，作者提出了MOMO（模态映射和编排），这是一种基于深度学习的方法，利用元数据子串匹配和神经网络集成来自动化映射过程，通过训练来识别7种不同模式的76种最常见的成像研究。我们进行了一个回顾性研究，以衡量该算法可以提供的准确性。为此，从当地医院的PACS数据库中检索了一组11,934个具有现有标签的成像系列来训练神经网络。一组843个完全匿名的外部研究被手工标记，以评估我们的算法的性能。此外，我们还进行了消融研究，以测量算法中网络集成的性能影响，并与商业产品进行了性能比较测试。与商业产品（96.20%预测能力，82.86%准确率，1.36%小错误）相比，神经网络集成单独执行分类任务的准确率较低（99.05%预测能力，72.69%准确率，10.3%小错误%）。然而，MOMO在准确性和预测能力方面更好（99.29%的预测能力，92.71%的准确性，2.63%的小误差）。

原文题目：MOMO -- Deep Learning-driven classification of external DICOM studies for PACS archivation

原文：Patients regularly continue assessment or treatment in other facilities than they began them in, receiving their previous imaging studies as a CD-ROM and requiring clinical staff at the new hospital to import these studies into their local database. However, between different facilities, standards for nomenclature, contents, or even medical procedures may vary, often requiring human intervention to accurately classify the received studies in the context of the recipient hospital's standards. In this study, the authors present MOMO (MOdality Mapping and Orchestration), a deep learning-based approach to automate this mapping process utilizing metadata substring matching and a neural network ensemble, which is trained to recognize the 76 most common imaging studies across seven different modalities. A retrospective study is performed to measure the accuracy that this algorithm can provide. To this end, a set of 11,934 imaging series with existing labels was retrieved from the local hospital's PACS database to train the neural networks. A set of 843 completely anonymized external studies was hand-labeled to assess the performance of our algorithm. Additionally, an ablation study was performed to measure the performance impact of the network ensemble in the algorithm, and a comparative performance test with a commercial product was conducted. In comparison to a commercial product (96.20% predictive power, 82.86% accuracy, 1.36% minor errors), a neural network ensemble alone performs the classification task with less accuracy (99.05% predictive power, 72.69% accuracy, 10.3% minor errors). However, MOMO outperforms either by a large margin in accuracy and with increased predictive power (99.29% predictive power, 92.71% accuracy, 2.63% minor errors).