Smart Systems for Industrial Applications. Группа авторов

Methods proposed Performance analysis [30] Clinical applications Deep learning–based diagnosis Detects metastases in hematoxylin and eosin–stained tissue sections of lymph nodes of women with breast cancerAchieves 95% CI using 3-layer CNN [31] Clinical applications -Radiology Clinical decision-making using CNN Achieves 20% improvement over sonographer readings after training with ultrasound images of left and right carotid artery from 203 patients. [32–34] Clinical applications -survival prediction Probabilistic Neural NetworkMulti-layer PerceptronGene expression classifierSupport Vector MachineRadial Basis Neural NetworkK-means algorithm Trained with 23 demographic, tumor-related parameters and selected perioperative data from 102 patients.PNN achieves high prediction ability with an accuracy of 0.892 and sensitivity of 0.975 [35] Surgical Applications Rotational matrix and translation vector algorithm to reduce the geometric error Improves the video accuracy by 0.30–0.40 mm (in terms of overlay error)Enhances processing rate to 10–13 frames/sDepth perception is increased by 90–100 mm [36–38] Surgical Applications Feasibility of laparoscopic Sentinel Lymph Node (SLN) staging 245 SLN nodes were removed out of 370 lymph nodes from 87 patients.

References

      1. Gaddi, A., Capello, F., Manca, M., eHealthcare and Quality of Life, Springer, Verlag Italia, 2014.

      2. Oh, H., Rizo, C., Enkin, M., Jadad, A., What is ehealth (3): a systematic review of published definitions. J. Med. Internet Res., 7, 1, e1, 2005.

      3. Gurung, M.S., Dorji, G., Khetrapal, S., Ra, S., Babu, G.R., and S Krishnamurthy, R.S., Transforming healthcare through Bhutan’s digital health strategy: progress to date. WHO South-East Asia Journal of Public Health, pp. 77–82, doi: 10.4103/2224-3151.264850.

      4. Zulman, D.M., Jenchura, E.C., Cohen, D.M., Lewis, E.T., Houston, T.K., Asch, S.M., How Can eHealth Technology Address Challenges Related to Multimorbidity Perspectives from Patients with Multiple Chronic Conditions. J. Gen. Intern. Med., 30, 8, 1063–70, 2015.

      5. Laxminarayan, S. and Istepanian, R.S.H., Unwired e-med: the next generation of wireless and internet telemedicine systems. IEEE Trans. Inf. Technol. Biomed., 4, 3, 189–193, Sept 2000, https://doi.org/10.1109/TITB.2000.5956074.

      6. Germanakos, P., Mourlas, C., Samaras, G., A mobile agent approach for ubiquitous and personalized ehealth information systems, in: Proceedings of the Workshop on ‘Personalization for e-Health’ of the 10th International Conference on User Modeling (UM’05), Edinburgh, pp. 67–70, 2005.

      7. Lee, J., Smart health: concepts and status of ubiquitous health with smartphones, in: ICTC 2011, pp. 388–389, Sept 2011, https://doi.org/10.1109/ICTC.2011.6082623.

      8. Wu, G., Talwar, S., Johnsson, K., Himayat, N., Johnson, K.D., M2M: from mobile to embedded internet. IEEE Commun. Mag., 49, 4, 36–43, April 2011, https://doi.org/10.1109/MCOM.2011.5741144.

      9. Jennifer Bresnick, J., Top 12 Ways Artificial Intelligence Will Impact Healthcare, World medical Innovation Forum, 2018, accessed 30 April 2018, https://healthitanalytics.com/news/top-12-ways-artificial-intelligence-will-impact-healthcare.

      10. Micah Castelo, M., The Future of Artificial Intelligence in Healthcare, Healthtech Magazine, 2020, accessed 26 Feb 2020, https://healthtechmagazine.net/article/2020/02/future-artificial-intelligence-healthcare.

      11. Sandeep Reddy (November 5th 2018). Use of Artificial Intelligence in Healthcare Delivery, eHealth - Making Healthcare Smarter, Thomas F. Heston, IntechOpen, DOI: 10.5772/intechopen.74714. Available from: https://www.intechopen.com/books/ehealth-making-health-care-smarter/use-of-artificial-intelligence-in-healthcare-delivery.

      12. Murdoch, T.B. and Detsky, A.S., The inevitable application of big data to healthcare. JAMA, 309, 1351–2, 2013.

      13. Kolker, E., Özdemir, V., Kolker, E., How Healthcare can refocus on its Super-Customers (Patients, n=1) and Customers (Doctors and Nurses) by Leveraging Lessons from Amazon, Uber, and Watson. OMICS, 20, 329–33, 2016.

      14. Dilsizian, S.E. and Siegel, E.L., Artificial intelligence in medicine and cardiac imaging: harnessing big data and advanced computing to provide personalized medical diagnosis and treatment. Curr. Cardiol. Rep., 16, 441, 2014.

      15. Bhavnani, S.P., Narula, J., Sengupta, P.P., Mobile technology and the digitization of healthcare. Eur. Heart J., 37, 1428–1438, 2016, https://doi.org/10.1093/eurheartj/ehv770.

      16. Tison, G.H., Sanchez, J.M., Ballinger, B., Singh, A., Olgin, J.E., Pletcher, M.J., Vittinghoff, E., Lee, E.S., Fan, S.M., Gladstone, R.A. et al., Passive detection of atrial fibrillation using a commercially available smartwatch. JAMA Cardiol., 3, 409–416, 2018, https://doi.org/10.1001/jamacardio.2018.0136.

      17. Sengupta, P.P., Huang, Y.M., Bansal, M., Ashrafi, A., Fisher, M., Shameer, K., Gall, W., Dudley, J.T., Cognitive machine-learning algorithm for cardiac imaging: a pilot study for differentiating constrictive pericarditis from restrictive cardiomyopathy. Circ. Cardiovasc. Imaging, 9, e004330, 2016, https://doi.org/10.1161/CIRCIMAGING.115.004330.

      18. Tsang, W., Salgo, I.S., Medvedofsky, D., Takeuchi, M., Prater, D., Weinert, L., Yamat, M., Mor-Avi, V., Patel, A.R., Lang, R.M., Transthoracic 3D echocardiographic left heart chamber quantification using an automated adaptive analytics algorithm. JACC: Cardiovasc. Imaging, 9, 769–782, 2016, https://doi.org/10.1016/j.jcmg.2015.12.020.

      19. Lancaster, M.C., Salem Omar, A.M., Narula, S., Kulkarni, H., Narula, J., Sengupta, P.P., Phenotypic clustering of left ventricular diastolic function parameters: patterns and prognostic relevance. JACC: Cardiovasc. Imaging, 12, 7, 1149–1161, 2018, https://doi.org/10.1016/j.jcmg.2018.02.005. [epub].

      20. Zhang, J., Gajjala, S., Agrawal, P., Tison, G.H., Hallock, L.A., Beussink-Nelson, L., Lassen, M.H., Fan, E., Aras, M.A., Jordan, C. et al., Fully automated echo-cardiogram interpretation in clinical practice. Circulation, 138, 1623–1635, 2018, (https://doi.org/10.1161/CIRCULATIONAHA.118.034338).

      21.