ONSUScan: Noninvasive ICP Monitoring with Ultrasound and AI
DOI:
https://doi.org/10.58445/rars.1314Keywords:
Intracranial Pressure, ICP, Optic Nerve Sheath Diameter, ONSD, Optic Nerve Sheath Ultrasonography, ONSUS, Non invasive, Ultrasound imaging, US, Convolutional Neural Networks, CNN, Wavelet denoising, Auto-segmentation, Transfer learningAbstract
Intracranial pressure (ICP) monitoring is critical in neurological care, but current invasive methods carry inherent risks. Optic Nerve Sheath Ultrasonography (ONSUS) offers a noninvasive alternative by measuring optic nerve sheath diameter (ONSD), which correlates with ICP. This paper introduces ONSUScan, a novel device integrating high-frequency ultrasound (US) and advanced AI algorithms for real-time image processing and precise ICP estimation. The device's design, clinical applications, and prospects are discussed, emphasizing its potential to enhance patient safety and clinical outcomes in ICP monitoring.
References
Abadi, Martín, et al. "TensorFlow: Large-scale machine learning on heterogeneous systems." TensorFlow.org, Google, 2016, https://www.tensorflow.org/.
Albert, Daniel M. and Gamm, David M.. "optic nerve". Encyclopedia Britannica, 1 Mar. 2024, https://www.britannica.com/science/optic-nerve. Accessed 12 July 2024.
Appia, F., Gastaldi, M., and Menzel, M. "Optic nerve sheath ultrasonography: A narrative review." European Journal of Trauma and Emergency Surgery, vol. 45, no. 4, 2019, pp. 529-535. doi:10.1007/s00068-018-1014-4.
Chai, T., and R. R. Draxler. "Root mean square error (RMSE) or mean absolute error (MAE)? Arguments against avoiding RMSE in the literature." Geoscientific Model Development, vol. 7, no. 3, 2014, pp. 1247-1250. doi:10.5194/gmd-7-1247-2014.
Chen, Tianqi, and Carlos Guestrin. "XGBoost: A scalable tree boosting system." Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, ACM, 2016, pp. 785-794. doi:10.1145/2939672.2939785.
Chunwei Tian, Menghua Zheng, Wangmeng Zuo, Bob Zhang, Yanning Zhang, David Zhang. “Multi-stage image denoising with the wavelet transform” Pattern Recognition, Volume 134, 2023, https://doi.org/10.1016/j.patcog.2022.109050.
F. Jia, W. H. Wong and T. Zeng, "DDUNet: Dense Dense U-Net with Applications in Image Denoising," 2021 IEEE/CVF International Conference on Computer Vision Workshops (ICCVW), Montreal, BC, Canada, 2021, pp. 354-364, doi: 10.1109/ICCVW54120.2021.00044. keywords: {Wavelet transforms;Image segmentation;Image color analysis;Image edge detection;Superresolution;Gray-scale;Image restoration},
Goodfellow, Ian, et al. "Generative adversarial nets." Advances in neural information processing systems, vol. 27, 2014, pp. 2672-2680. http://papers.nips.cc/paper/5423-generative-adversarial-nets.pdf.
James, Gareth, et al. An introduction to statistical learning: with applications in R. Springer, 2013.
Kohavi, Ron. "A study of cross-validation and bootstrap for accuracy estimation and model selection." International Joint Conference on Artificial Intelligence, 1995, pp. 1137-1143. https://www.aaai.org/Papers/IJCAI/1995/IJCAI95-185.pdf.
Lee, S. U., and H. J. Kim. "Optic nerve sheath diameter as a bedside tool to assess intracranial pressure." Journal of Neurocritical Care, vol. 13, no. 2, 2020, pp. 53-60. doi:10.18700/jnc.190129.
Linh, Tran Duy et al. “GAN-Based Noise Model for Denoising Real Images.” Asian Conference on Computer Vision (2020).
Litjens, G., et al. "A survey on deep learning in medical image analysis." Medical Image Analysis, vol. 42, 2018, pp. 60-88. doi:10.1016/j.media.2017.07.005.
Lv, J., et al. "An advanced ultrasound image despeckling method based on convolutional neural networks." Journal of Healthcare Engineering, 2019, Article ID 3560390. doi:10.1155/2019/3560390.
Mallat, Stéphane. A wavelet tour of signal processing: The sparse way. Academic Press, 2009.
Mokri B. The Monro-Kellie hypothesis: applications in CSF volume depletion. Neurology. 2001 Jun 26;56(12):1746-8. doi: 10.1212/wnl.56.12.1746. PMID: 11425944.
Pan, Sinno Jialin, and Qiang Yang. "A survey on transfer learning." IEEE Transactions on Knowledge and Data Engineering, vol. 22, no. 10, 2010, pp. 1345-1359. doi:10.1109/TKDE.2009.191.
Paganini, Giuseppe, and Peter J. Hutchinson. "Optic nerve sheath ultrasonography: A non-invasive tool for the neurointensivist." Intensive Care Medicine, vol. 42, no. 7, 2016, pp. 1193-1196. doi:10.1007/s00134-016-4346-3.
Paszke, Adam, et al. "PyTorch: An imperative style, high-performance deep learning library." PyTorch.org, 2019, https://pytorch.org/.
Ronnenberg, C., et al. "U-Net: Convolutional networks for biomedical image segmentation." Medical Image Computing and Computer-Assisted Intervention, 2015, pp. 234-241. https://arxiv.org/pdf/1505.04597.pdf.
Shorten, C., and Taghi M. Khoshgoftaar. "A survey on image data augmentation for deep learning." Journal of Big Data, vol. 6, no. 1, 2019, Article 60. doi:10.1186/s40537-019-0197-0.
Singh, G., and S. Awasthi. "An effective technique for speckle noise reduction from medical ultrasound images using wavelet transform." Journal of King Saud University - Computer and Information Sciences, 2019. Advance online publication. doi:10.1016/j.jksuci.2019.10.011.
Singh, G., and M. Kaur. "Ultrasound image despeckling using wavelet thresholding techniques." Journal of Computer Science and Information Technology, vol. 1, no. 2, 2013, pp. 60-68. http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.353.5697&rep=rep1&type=pdf.
Stevens, Raoul, et al. "An Automated Algorithm for Optic Nerve Sheath Diameter Assessment from B-mode Ultrasound Images." Journal of Neuroimaging, vol. 31, no. 4, 2021, pp. 724-732, https://doi.org/10.1111/jon.12851. Accessed 12 Jul. 2024.
Virtanen, P., et al. "SciPy 1.0: Fundamental algorithms for scientific computing in Python." Nature Methods, vol. 17, 2020, pp. 261-272. doi:10.1038/s41592-019-0686-2.
Zhang, Y., et al. "Advances in optic nerve sheath diameter ultrasonography for intracranial pressure estimation." Neurology Research International, 2019, Article ID 2185981. doi:10.1155/2019/2185981.
Downloads
Posted
Categories
License
Copyright (c) 2024 Bhavna Malladi
![Creative Commons License](http://i.creativecommons.org/l/by-nc-nd/4.0/88x31.png)
This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.