Zhou George, Chen Yunchan, Chien Candace, Revatta Leslie, Ferdous Jannatul, Chen Michelle, Deb Shourov, De Leon Cruz Sol, Wang Alan, Lee Benjamin, Sabuncu Mert R, Browne William, Wun Herrick, Mosadegh Bobak
Weill Cornell Medicine, New York, NY, 10021, USA.
City University of New York, Hunter College, New York, NY, 10021, USA.
NPJ Digit Med. 2023 Sep 1;6(1):163. doi: 10.1038/s41746-023-00894-9.
For hemodialysis patients, arteriovenous fistula (AVF) patency determines whether adequate hemofiltration can be achieved, and directly influences clinical outcomes. Here, we report the development and performance of a deep learning model for automated AVF stenosis screening based on the sound of AVF blood flow using supervised learning with data validated by ultrasound. We demonstrate the importance of contextualizing the sound with location metadata as the characteristics of the blood flow sound varies significantly along the AVF. We found the best model to be a vision transformer trained on spectrogram images. Our model can screen for stenosis at a performance level comparable to that of a nephrologist performing a physical exam, but with the advantage of being automated and scalable. In a high-volume, resource-limited clinical setting, automated AVF stenosis screening can help ensure patient safety via early detection of at-risk vascular access, streamline the dialysis workflow, and serve as a patient-facing tool to allow for at-home, self-screening.
对于血液透析患者而言，动静脉内瘘（AVF）的通畅性决定了能否实现充分的血液滤过，并直接影响临床结局。在此，我们报告了一种深度学习模型的开发与性能，该模型基于AVF血流声音，采用监督学习，并经超声验证数据，用于自动筛查AVF狭窄。我们证明了将声音与位置元数据相结合的重要性，因为血流声音的特征沿AVF会有显著变化。我们发现最佳模型是在频谱图图像上训练的视觉Transformer。我们的模型筛查狭窄的性能水平与肾病医生进行体格检查相当，但具有自动化和可扩展的优势。在高容量、资源有限的临床环境中，自动AVF狭窄筛查有助于通过早期检测有风险的血管通路来确保患者安全，简化透析工作流程，并作为一种面向患者的工具，实现居家自我筛查。
