Elsheikh Samer, Elbaz Ahmed, Rau Alexander, Demerath Theo, Kellner Elias, Watzlawick Ralf, Würtemberger Urs, Urbach Horst, Reisert Marco
Department of Neuroradiology, Medical Center-University of Freiburg, Faculty of Medicine, University of Freiburg, Freiburg, Germany.
Medical Physics, Department of Diagnostic and Interventional Radiology, Medical Center, Faculty of Medicine, University of Freiburg, Freiburg, Germany.
PLoS One. 2024 Dec 26;19(12):e0316003. doi: 10.1371/journal.pone.0316003. eCollection 2024.
External drainage represents a well-established treatment option for acute intracerebral hemorrhage. The current standard of practice includes post-operative computer tomography imaging, which is subjectively evaluated. The implementation of an objective, automated evaluation of postoperative studies may enhance diagnostic accuracy and facilitate the scaling of research projects. The objective is to develop and validate a fully automated pipeline for intracerebral hemorrhage and drain detection, quantification of intracerebral hemorrhage coverage, and detection of malpositioned drains.
In this retrospective study, we selected patients (n = 68) suffering from supratentorial intracerebral hemorrhage treated by minimally invasive surgery, from years 2010-2018. These were divided into training (n = 21), validation (n = 3) and testing (n = 44) datasets. Mean age (SD) was 70 (±13.56) years, 32 female. Intracerebral hemorrhage and drains were automatically segmented using a previously published artificial intelligence-based approach. From this, we calculated coverage profiles of the correctly detected drains to quantify the drains' coverage by the intracerebral hemorrhage and classify malpositioning. We used accuracy measures to assess detection and classification results and intraclass correlation coefficient to assess the quantification of the drain coverage by the intracerebral hemorrhage.
In the test dataset, the pipeline showed a drain detection accuracy of 0.97 (95% CI: 0.92 to 0.99), an agreement between predicted and ground truth coverage profiles of 0.86 (95% CI: 0.85 to 0.87) and a drain position classification accuracy of 0.88 (95% CI: 0.77 to 0.95) resulting in area under the receiver operating characteristic curve of 0.92 (95% CI: 0.85 to 0.99).
We developed and statistically validated an automated pipeline for evaluating computed tomography scans after minimally invasive surgery for intracerebral hemorrhage. The algorithm reliably detects drains, quantifies drain coverage by the hemorrhage, and uses machine learning to detect malpositioned drains. This pipeline has the potential to impact the daily clinical workload, as well as to facilitate the scaling of data collection for future research into intracerebral hemorrhage and other diseases.
外部引流是急性脑出血一种成熟的治疗选择。当前的实践标准包括术后计算机断层扫描成像，其评估具有主观性。实施术后研究的客观、自动化评估可能会提高诊断准确性，并促进研究项目的规模化。目的是开发并验证一个用于脑出血和引流管检测、脑出血覆盖范围量化以及引流管位置不当检测的全自动流程。
在这项回顾性研究中，我们选取了2010年至2018年期间接受微创手术治疗的幕上脑出血患者（n = 68）。这些患者被分为训练组（n = 21）、验证组（n = 3）和测试组（n = 44）数据集。平均年龄（标准差）为70（±13.56）岁，女性32名。使用先前发表的基于人工智能的方法对脑出血和引流管进行自动分割。据此，我们计算正确检测到的引流管的覆盖情况，以量化脑出血对引流管的覆盖程度并对位置不当进行分类。我们使用准确性指标来评估检测和分类结果，并使用组内相关系数来评估脑出血对引流管覆盖情况的量化。
在测试数据集中，该流程显示引流管检测准确率为0.97（95%置信区间：0.92至0.99），预测的和真实的覆盖情况之间的一致性为0.86（95%置信区间：0.85至0.87），引流管位置分类准确率为0.88（95%置信区间：0.77至0.95），受试者操作特征曲线下面积为0.92（95%置信区间：0.85至0.99）。
我们开发并通过统计学验证了一个用于评估脑出血微创手术后计算机断层扫描的自动化流程。该算法能可靠地检测引流管，量化出血对引流管的覆盖情况，并利用机器学习检测位置不当的引流管。此流程有可能影响日常临床工作量，并有助于扩大脑出血及其他疾病未来研究的数据收集规模。
