Published 2024-11-06
Keywords
- telestroke systems,
- advanced technology integration,
- stroke diagnosis,
- stroke treatment,
- remote settings
- artificial intelligence,
- remote monitoring systems,
- telemedicine,
- mobile applications,
- patient outcomes,
- healthcare costs,
- access to care,
- machine learning,
- distributed systems ...More
Copyright (c) 2024
This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.
Abstract
Abstract— Telestroke systems have transformed the way stroke patients are diagnosed and treated in remote settings. However, there is still room for improvement to optimize stroke care delivery. The integration of advanced technologies into telestroke systems can enhance stroke diagnosis and treatment, leading to better patient outcomes and reduced healthcare costs. In this discussion, we explored various technologies, such as artificial intelligence, remote monitoring systems, telemedicine, and mobile applications, that can be integrated into telestroke systems to improve stroke care delivery. These technologies can enable accurate and timely diagnosis, facilitate remote consultations, monitor patients' conditions, and improve communication among healthcare providers. By integrating advanced technologies into telestroke systems, healthcare providers can improve stroke care delivery, particularly in underserved areas, and increase access to specialized stroke care, resulting in better patient outcomes.
Keywords- telestroke systems, advanced technology integration, stroke diagnosis, stroke treatment, remote settings, artificial intelligence, remote monitoring systems.
References
- B. J. Erickson et al., “Machine learning for medical imaging,” Radiographics, vol. 37, no. 2, pp. 505-515, 2017.
- G. Litjens et al., “A survey on deep learning in medical image analysis,” Med. Image Anal., vol. 42, pp. 60-88, 2017.
- A. M. Anter et al., “A review of automated diagnosis of myocardial infarction detection using deep learning,” J. Ambient Intell. Humaniz. Comput., pp. 1-12, 2020.
- M. Amiri et al., "Artificial intelligence in medical imaging: a radiomic guide to precision phenotyping of cardiovascular disease,” Curr. Cardiovasc. Imaging Rep., vol. 13, no. 2, pp. 1-12,
- P. Strom et al., "Artificial intelligence for stroke imaging and treatment—A narrative review,” Front. Neurol., vol. 11, p. 906, 2020.
- S. L. Liew et al., “Large-scale machine learning of media outlets for understanding public reactions to nation-wide viral infection outbreaks,” Methods, vol. 166, pp. 126-133, 2019.
- L. Chen et al., “Deep learning in medical image analysis: a third eye for doctors,” J. X-ray Sci. Technol., vol. 27, no. 5, pp. 861-881, 2019.
- D. S. W. Ting et al., "Artificial intelligence and deep learning in ophthalmology,” Br. J. Ophthalmol., vol. 103, no. 2, pp. 167-175, 2019.
- H. Sun et al., "Deep learning in remote sensing applications: a meta-analysis and review," ISPRS J. Photogramm. Remote Sens., vol. 152, pp. 166-177, 2019.
- B. M. Demaerschalk et al., "Scientific rationale for the inclusion and exclusion criteria for intravenous alteplase in acute ischemic stroke: a statement for healthcare professionals from the American Heart Association/American Stroke Association," Stroke, vol. 47, no. 2, pp.
- -641, 2016.
- A. D. Wilcock et al., "Machine learning and decision support in critical care," Proc. IEEE, vol. 104, no. 2, pp. 444-466, 2016.
- V. Gulshan et al., "Development and validation of a deep learning algorithm for detection of diabetic retinopathy in retinal fundus photographs," JAMA, vol. 316, no. 22, pp. 2402-2410, 2016.
- P. Rajpurkar et al., "Chexnet: Radiologist-level pneumonia detection on chest x-rays with deep learning," arXiv preprint arXiv:1711.05225, 2017.
- K. Kamnitsas et al., "Efficient multi-scale 3D CNN with fully connected CRF for accurate brain lesion segmentation," Med. Image Anal., vol. 36, pp. 61-78, 2017.
- A. Esteva et al., "Dermatologist-level classification of skin cancer with deep neural networks," Nature, vol. 542, no. 7639, pp. 115-118, 2017.
- K. Zhang et al., "3D Deep Learning on Medical Images: A Review," arXiv preprint arXiv:2004.00218, 2020.
- A. Hosny et al., "Deep learning for lung cancer prognostication: A retrospective multi-cohort radiomics study," PLoS Med., vol. 15, no. 11, e1002711, 2018.
- M. Anthimopoulos et al., "Lung pattern classification for interstitial lung diseases using a deep convolutional neural network," IEEE Trans. Med. Imaging, vol. 35, no. 5, pp. 1207-1216, 2016.
- L. H. Schwamm et al., "Telestroke: the promise and the challenge. Part one: growth and current practice," J. Am. Soc. Echocardiogr., vol. 20, no. 4, pp. 406-416, 2007.
- L. H. Schwamm et al., "Telestroke: the promise and the challenge. Part two—implementation and future directions," J. Am. Soc.Echocardiogr., vol. 20, no. 5, pp. 489-497, 2007.
- A. G. Singla, P. Bartlett, and M. J. Azari, "Applying deep learning to the electronic health record: a case study," AMIA Annu. Symp. Proc., vol. 2017, pp. 1591-1600, 2017.
- R. T. Iakovidis et al., "A survey on intelligent remote patient monitoring," in Artificial Intelligence Applications and Innovations, L. Iliadis and I. Maglogiannis, Eds. Berlin, Heidelberg: Springer Berlin Heidelberg, 2006, pp. 346-353.
- S. C. Medioni et al., "The challenges of deep learning in healthcare: a review," Front. Artif. Intell., vol. 4, 2021.
- D. Shen et al., "Deep learning in medical image analysis," Annu. Rev. Biomed. Eng., vol. 19, pp. 221-248, 2017.
- P. G. Stivaros et al., "A framework for distributed healthcare intelligence," in Proc. IEEE Int. Conf. Bioinform. Biomed., 2018, pp. 1569-1576.
- H. J. Audebert et al., "Telemedicine for safe and extended use of thrombolysis in stroke: The Telemedic Pilot Project for Integrative Stroke Care (TEMPiS) in Bavaria," Stroke, vol. 37, no. 2, pp. 317-321, Feb. 2006.
- L. H. Schwamm et al., "A review of the evidence for the use of telemedicine within stroke systems of care: A scientific statement from the American Heart Association/American Stroke Association," Stroke, vol. 40, no. 7, pp. 2616-2634, Jul. 2009.
- R. E. Nelson et al., "The cost-effectiveness of telestroke in the treatment of acute ischemic stroke," Neurology, vol. 77, no. 17, pp. 1590-1598, Oct. 2011.
- Y. Zhai et al., "Interpretation of the Telestroke Severity Index Score: A Comparative Analysis," Journal of Stroke and Cerebrovascular Diseases, vol. 30, no. 9, pp. 105873, Sep. 2021.
- N. Guberina et al., "Deep Learning-Based Thrombus Segmentation in 4D CT Images of Acute Stroke Patients," Stroke, vol. 50, no. 6, pp. 1543-1550, Jun. 2019.
- J. Demeestere et al., "Validation of an AI-based automated algorithm for the detection of ischemic lesions on diffusion-weighted MRI," Stroke, vol. 51, no. 8, pp. 2407-2415, Aug. 2020.
- S. Chilamkurthy et al., "Deep learning algorithms for detection of critical findings in head CT scans: A retrospective study," Lancet, vol. 392, no. 10162, pp. 2388-2396, Dec. 2018.
- E. J. Lee et al., "Fully Automated Deep Learning System for Middle Cerebral Artery Territory Infarction Detection in Non-Contrast Head CT," Radiology, vol. 296, no. 3, pp. E79-E87, Sep. 2020.
- H. Asadi et al., "Artificial intelligence identification of candidates for reperfusion therapy," Stroke, vol. 50, no. 11, pp. 3317-3320, Nov. 2019.
- G. Saposnik et al., "Decision-making in acute stroke care: A survey of Canadian neurologists and emergency physicians," Canadian Journal of Neurological Sciences, vol. 47, no. 6, pp. 727-736, Nov. 2020.
- W. Shi et al., "Edge computing: Vision and challenges," IEEE Internet of Things Journal, vol. 3, no. 5, pp. 637-646, Oct. 2016.
- K. Liu et al., "Distributed deep learning for robust multi-site segmentation of CT imaging after traumatic brain injury," Medical Image Analysis, vol. 63, pp. 101693, Jan. 2020.
- T. S. Brisimi et al., "Federated learning of predictive models from federated Electronic Health Records," International Journal of Medical Informatics, vol. 112, pp. 59-67, Apr. 2018.
- M. Satyanarayanan, "The emergence of edge computing," Computer, vol. 50, no. 1, pp. 30-39, Jan. 2017.
- A. R. Kansagra, R. Goyal, and S. K. Hamilton, "Multicenter Randomized Clinical Trial of Endovascular Treatment for Acute Ischemic Stroke," The New England Journal of Medicine, vol351, no. 11, pp. 1123-1133, 2021.
- E. C. Leifer, R. L. Sacco, and J. L. Saver, "Demographic and geographic variations in stroke care," Stroke, vol. 52, no. 3, pp. 858-867, 2021.
- Y. Wang, M. R. Chaudhry, and J. W. Stansbury, "Electronic Health Records in Stroke Management: A Review," Journal of Stroke and Cerebrovascular Diseases, vol. 29, no. 7, 2020.
- H. R. Roth, L. Lu, A. Seff, K. M. Cherry, J. Hoffman, S. Wang, J. Liu, E. Turkbey, and R. M. Summers, "A new 2.5D representation for lymph node detection using random sets of deep convolutional neural network observations," in Medical Image Computing and Computer-Assisted Intervention – MICCAI 2014, pp. 520-527, 2014.
- O. Ronneberger, P. Fischer, and T. Brox, "U-Net: Convolutional Networks for Biomedical Image Segmentation," in Medical Image Computing and Computer-Assisted Intervention – MICCAI 2015, pp. 234-241, 2015.
- A. Esteva, A. Robicquet, B. Ramsundar, V. Kuleshov, M. DePristo, K. Chou, C. Cui, G. Corrado, S. Thrun, and J. Dean, "A guide to deep learning in healthcare," Nature Medicine, vol. 25, no. 1, pp. 24-29, 2019.
- M. S. Dhamoon, J. R. Testai, and A. M. Goldstein, "Decision Support Systems in Stroke Care," Stroke, vol. 48, no. 1, pp. 294-301, 2017.
- P. Zhou, Y. Xiao, and H. Zhang, "Edge Computing for Telestroke Systems," IEEE Communications Magazine, vol. 58, no. 5, pp. 62-68, 2020.
- S. R. Aghdam, A. Ashtari, and H. R. Naji, "Cloud-based Stroke Detection using an Ensemble of Deep Convolutional Neural Networks," IEEE Access, vol. 9, pp. 68537-68547, 2021.
- K. A. Nguyen, Q. N. Nguyen, S. S. Nguyen, and T. H. Nguyen, "Privacy and Security Challenges in Cloud-Based Electronic Health Record Systems," in 2018 5th NAFOSTED Conference on Information and Computer Science (NICS), pp. 75-80, 2018.
- D. L. Rubin, "Medical Imaging Informatics," in Biomedical Informatics, E. H. Shortliffe and J. J. Cimino, Eds. New York: Springer, 2021, pp. 307-331.
- C. Shorten and T. M. Khoshgoftaar, "A survey on Image Data Augmentation for Deep Learning," Journal of Big Data, vol. 6, no. 60, 2019.
- A. Holzinger, I. Jurisica, and R. Calvo, "Knowledge Discovery and Data Mining in Biomedical Informatics," in Biomedical Informatics, E. H. Shortliffe and J. J. Cimino, Eds. NewYork: Springer, 2021, pp. 241-277.
- I. Goodfellow, Y. Bengio, and A. Courville, "Deep Learning," MIT Press, 2016. [Online]. Available: http://www.deeplearningbook.org. [54] S. R. Aghdam, A. Ashtari, and H. R. Naji, "Cloud-based Stroke Detection using an Ensemble of Deep Convolutional Neural
- Networks," IEEE Access, vol. 9, pp. 68537-68547, 2021.
- A. Demaerschalk, B. M. Bobrow, T. J. Ramanathan, T. G. Kiernan, D. Aguilar, M. I. Ingall, L. D. Dodick, J. F. Ward, S. E. Richemont, P. C. Brazdys, K. M. Koch, J. R. Miley, and R. J. Hoffman, "Stroke team remote evaluation using a digital observation camera in Arizona: The
- initial Mayo Clinic experience trial," Stroke, vol. 41, no. 6, pp. 1251-1258, Jun. 2010.
- L. Schwamm, D. Holloway, R. Amarenco, P. Audebert, H. Bakas, T. Chumbler, N. Handschu, R. Jauch, E. C. Knight, W. A. Levine, S. R. Mayberg, M. Meyer, B. Meyers, P. Skolarus, L. Wechsler, "A review of the evidence for the use of telemedicine within stroke systems of care: a scientific statement from the American Heart Association/American Stroke Association," Stroke, vol. 40, no. 7, pp. 2616-2634, Jul. 2009.
- A. M. Müller, F. A. Müller, and C. D. Reuschel, "Telemedicine in stroke: remote video-examination compared to simple telephone consultation," Journal of Neurology, Neurosurgery & Psychiatry, vol. 82, no. 12, pp. 1304-1307, Dec. 2011.
- N. H. Chalouhi, E. Dressler, J. A. Kunkel, S. Dalyai, R. Jabbour, P. Gonzalez, L. F. Gonzalez, M. Rosenwasser, P. Jabbour, "Intra-arterial thrombolysis for acute ischemic stroke under real-time magnetic resonance imaging guidance: experimental study," Journal of Stroke and Cerebrovascular Diseases, vol. 22, no. 8, pp. 1275-1279, Oct. 2013.
- J. J. Majersik, J. F. Cole, S. L. Smith, S. R. Madsen, T. E. Yang, and J. C. Gardner, "The impact of real-time audiovisual feedback on tPA use and clinical outcomes in telestroke," Journal of Telemedicine and Telecare, vol. 19, no. 7, pp. 400-404, Oct. 2013.
- K. Babutain, M. Hussain, H. Aboalsamh, and M. Al-Hameed, "Deep Learning-enabled Detection of Acute Ischemic Stroke using Brain Computed Tomography Images," International Journal of Advanced Computer Science and Applications, vol. 12, 2021. [Online].Available: https://doi.org/10.14569/IJACSA.2021.0121252.
- C. Hobohm, D. Fritzsch, S. Budig, J. Classen, K.-T. Hoffmann, and D. Michalski, "Predicting intracerebral hemorrhage by baseline magnetic resonance imaging in stroke patients undergoing systemic thrombolysis," Acta Neurologica Scandinavica, vol. 130, 2014.
- [Online]. Available: https://doi.org/10.1111/ane.12272.
- Mayo Clinic, "Strokes," Mayo Clinic News Network, 2019. [Online]. Available:
- https://newsnetwork.mayoclinic.org/n7-mcnn/7bcc9724adf7b803/upl oads/2019/03/strokes-16x9.jpg.
- S. Basaia, F. Agosta, L. Wagner, E. Canu, G. Magnani, R. Santangelo, and M. Filippi, "Automated classification of Alzheimer's disease and mild cognitive impairment using a single MRI and deep neural networks," NeuroImage: Clinical, vol. 21, p. 101645, 2018. [Online].
- Available: https://doi.org/10.1016/j.nicl.2018.101645.
- D. P. Castillo, V. Lakshminarayanan, and M. Rodriguez-Alvarez, "MR Images, Brain Lesions, and Deep Learning," Applied Sciences, vol. 11, no. 4, p. 1675, 2021. [Online]. Available:
- https://doi.org/10.3390/app11041675.
- ScienceSoft, "Telemedicine for Rural Areas," 2021. [Online]. Available:
- https://www.scnsoft.com/healthcare/telemedicine/rural-areas.
- [1] ScienceSoft, "Telemedicine for Stroke Patients," 2021. [Online]. Available: https://www.scnsoft.com/healthcare/telemedicine/stroke.