Harvard Medical School, 25 Shattuck St, Boston, MA, 02115, USA.
Department of Neurosurgery, Brigham and Women's Hospital, 60 Fenwood Rd, Boston, MA, 02115, USA.
Sci Rep. 2024 Sep 27;14(1):22166. doi: 10.1038/s41598-024-73167-4.
While ventricular shunts are the main treatment for adult hydrocephalus, shunt malfunction remains a common problem that can be challenging to diagnose. Computer vision-derived algorithms present a potential solution. We designed a feasibility study to see if such an algorithm could automatically predict ventriculomegaly indicative of shunt failure in a real-life adult hydrocephalus population. We retrospectively identified a consecutive series of adult shunted hydrocephalus patients over an eight-year period. Associated computed tomography scans were extracted and each scan was reviewed by two investigators. A machine learning algorithm was trained to identify the lateral and third ventricles, and then applied to test scans. Results were compared to human performance using Sørensen-Dice coefficients, calculated total ventricular volumes, and ventriculomegaly as documented in the electronic medical record. 5610 axial images from 191 patients were included for final analysis, with 52 segments (13.6% of total data) reserved for testing. Algorithmic performance on the test group averaged a Dice score of 0.809 ± 0.094. Calculated total ventricular volumes did not differ significantly between computer-derived volumes and volumes marked by either the first reviewer or second reviewer (p > 0.05). Algorithm detection of ventriculomegaly was correct in all test cases and this correlated with correct prediction of need for shunt revision in 92.3% of test cases. Though development challenges remain, it is feasible to create automated algorithms that detect ventriculomegaly in adult hydrocephalus shunt malfunction with high reliability and accuracy.
尽管脑室分流术是成人脑积水的主要治疗方法，但分流管故障仍然是一个常见的问题，诊断起来具有挑战性。基于计算机视觉的算法为此提供了一种潜在的解决方案。我们设计了一项可行性研究，以观察这种算法是否可以自动预测脑室扩大，从而提示分流失败，该研究纳入了一个真实的成人脑积水患者队列。我们回顾性地确定了 8 年内连续的成人分流脑积水患者系列。提取了相关的计算机断层扫描（CT）图像，并由两名研究人员对每一次扫描进行了评估。训练了一种机器学习算法来识别侧脑室和第三脑室，然后将其应用于测试扫描。使用 Sørensen-Dice 系数、计算的总脑室体积以及电子病历中记录的脑室扩大来比较算法的表现和人类表现。共纳入 191 名患者的 5610 个轴向图像，其中 52 个节段（总数据的 13.6%）用于测试。测试组算法的平均 Dice 得分为 0.809±0.094。计算机生成的总脑室体积与第一或第二评估者标记的体积之间没有显著差异（p>0.05）。算法在所有测试病例中均正确检测到脑室扩大，这与正确预测 92.3%的测试病例中分流管需要修订的情况相关。虽然仍然存在开发挑战，但创建可以高度可靠和准确地检测成人脑积水分流管故障导致的脑室扩大的自动算法是可行的。
