Deparment of Information Engineering and Computer Science, Feng Chia University, Taichung, Taiwan.
Department of Industrial Engineering and Engineering Management, National Tsing Hua University, Chinchu, Taiwan.
Sci Rep. 2023 Oct 17;13(1):17659. doi: 10.1038/s41598-023-35444-6.
In this study, a novel artificial intelligence (AI) model is proposed to detect stenosis in arteriovenous fistulas (AVFs) using inexpensive and non-invasive audio recordings. The proposed model is a combination of two new input features based on short-time Fourier transform (STFT) and sample entropy, as well as two associated classification models (ResNet50 and ANN). The model's hyper-parameters were optimized through the use of the design of the experiment (DOE). The proposed AI model demonstrates high performance with all essential metrics, including sensitivity, specificity, accuracy, precision, and F1-score, exceeding 0.90 at detecting stenosis greater than 50%. These promising results suggest that our approach can lead to new insights and knowledge in this field. Moreover, the robust performance of our model, combined with the affordability of the audio recording device, makes it a valuable tool for detecting AVF stenosis in home-care settings.
在这项研究中，提出了一种新的人工智能 (AI) 模型，使用廉价且非侵入性的音频记录来检测动静脉瘘 (AVF) 中的狭窄。所提出的模型是基于短时傅里叶变换 (STFT) 和样本熵的两个新输入特征以及两个相关分类模型 (ResNet50 和 ANN) 的组合。通过使用实验设计 (DOE) 优化了模型的超参数。所提出的 AI 模型在检测大于 50%的狭窄方面表现出了很高的性能，所有关键指标（包括敏感性、特异性、准确性、精度和 F1 分数）均超过 0.90。这些有希望的结果表明，我们的方法可以为该领域带来新的见解和知识。此外，我们的模型具有强大的性能，并且音频记录设备价格低廉，这使其成为在家中检测 AVF 狭窄的有价值的工具。
