Emerging Applications for Computer Vision and Artificial Intelligence in Management of the Cardiovascular Patient

Peter Osztrogonacz; Ponraj Chinnadurai; Alan B. Lumsden

doi:10.14797/mdcvj.1263

Emerging Applications for Computer Vision and Artificial Intelligence in Management of the Cardiovascular Patient

Methodist DeBakey Cardiovascular Journal

Volume 19 (2023): Issue 4

By: Peter Osztrogonacz , Ponraj Chinnadurai and Alan B. Lumsden

Open Access

|Aug 2023

Abstract

Artificial intelligence and telemedicine promise to reshape patient care to an unprecedented extent, leading to a safer and more sustainable work environment and improved patient care. In this article, we summarize how these emerging technologies can be used in the care of cardiovascular patients in such ways as fall detection and prevention, virtual nursing, remote case support, automation of instrument counts in the operating room, and efficiency optimization in the cardiovascular suite.

References

Vozzella GM, Hehman MC. Cardiovascular Nursing Workforce Challenges: Transforming the Model of Care for the Future. Methodist Debakey Cardiovasc J. 2023;19(2):90-99. doi: 10.14797/mdcvj.1188
Open DOI Search in Google Scholar Back to article
CDC.gov [Internet]. Washington, D.C.: U.S. Department of Health & Human Services; c2023. Life expectancy at birth, age 65, and age 75, by sex, race, and Hispanic origin: United States, selected years 1900–2019; 2021 [cited 2023 Jun 28]. Available from: https://www.cdc.gov/nchs/data/hus/2020-2021/LExpMort.pdf
Search in Google Scholar Back to article
WHO.int [Internet]. Geneva, Switzerland: World Health Organization; c2023. WHO Fact sheets - Falls; 2021 April 26 [cited 2023 Jun 28]. Available from: https://www.who.int/news-room/fact-sheets/detail/falls
Search in Google Scholar Back to article
WHO.int [Internet]. Geneva, Switzerland: World Health Organization; c2023. WHO Mortality Database; 2023 [cited 2023 June 27]. Available from: https://platform.who.int/mortality/themes/theme-details/topics/indicator-groups/indicator-group-details/MDB/falls
Search in Google Scholar Back to article
Florence CS, Bergen G, Atherly A, Burns E, Stevens J, Drake C. Medical Costs of Fatal and Nonfatal Falls in Older Adults. J Am Geriatr Soc. 2018 Apr;66(4):693-698. doi: 10.1111/jgs.15304
Open DOI Search in Google Scholar Back to article
Sciubba JD. Population Aging as a Global Issue. Oxford University Press; 2020.
Search in Google Scholar Back to article
Usmani S, Saboor A, Haris M, Khan MA, Park H. Latest Research Trends in Fall Detection and Prevention Using Machine Learning: A Systematic Review. Sensors (Basel). 2021 Jul 29;21(15):5134. doi: 10.3390/s21155134
Open DOI Search in Google Scholar Back to article
Lou Z, Wang L, Jiang K, Wei Z, Shen G. Reviews of wearable healthcare systems: Materials, devices and system integration. Mater Sci Eng R-Rep. 2020 Apr 1;140. doi: 10.1016/j.mser.2019.100523
Open DOI Search in Google Scholar Back to article
Kumar V, Ha S, Sawicki G, Liu C. Learning a Control Policy for Fall Prevention on an Assistive Walking Device. Presented at: 2020 IEEE International Conference on Robotics and Automation (ICRA); 2020; Paris, France.
Search in Google Scholar Back to article
Wang L, Xue Z, Ezeana C, et al. Preventing inpatient falls with injuries using integrative machine learning prediction: a cohort study. NPJ Digit Med. 2019 Dec 12;2:127. doi: 10.1038/s41746-019-0200-3
Open DOI Search in Google Scholar Back to article
Lu LH, Chiang SL, Wei SH, Lin CH, Sung WH. Development and clinical application of a computer-aided real-time feedback system for detecting in-bed physical activities. Comput Methods Programs Biomed. 2017 Aug;147:11-17. doi: 10.1016/j.cmpb.2017.05.014
Open DOI Search in Google Scholar Back to article
Cournan M, Fusco-Gessick B, Wright L. Improving Patient Safety Through Video Monitoring. Rehabil Nurs. 2018 Mar/Apr;43(2):111-115. doi: 10.1097/RNJ.0000000000000089
Open DOI Search in Google Scholar Back to article
Votruba L, Graham B, Wisinski J, Syed A. Video Monitoring to Reduce Falls And Patient Companion Costs For Adult Inpatients Nurs Econ. 2016 Jul-Aug;34(4):185-9.
Search in Google Scholar Back to article
Oh-Park M, Doan T, Dohle C, Vermiglio-Kohn V, Abdou A. Technology Utilization in Fall Prevention. Am J Phys Med Rehabil. 2021 Jan 1;100(1):92-99. doi: 10.1097/PHM.0000000000001554
Open DOI Search in Google Scholar Back to article
WHO.int [Internet]. Geneva, Switzerland: World Health Organization; c2023. Nursing and midwifery; 2022 March 18 [cited 2023 Jun 27). Available from: https://www.who.int/news-room/fact-sheets/detail/nursing-and-midwifery
Search in Google Scholar Back to article
HRSA.gov [Internet]. Rockville, MD: Health Resources & Services Administration; c2023. Nurse Workforce Projections, 2020-2035; 2022 Nov [cited 2023 Jun 27]. Available from: https://bhw.hrsa.gov/sites/default/files/bureau-health-workforce/Nursing-Workforce-Projections-Factsheet.pdf
Search in Google Scholar Back to article
Arslan F, Gerckens U. Virtual support for remote proctoring in TAVR during COVID-19. Catheter Cardiovasc Interv. 2021 Nov 1;98(5):E733-E736. doi: 10.1002/ccd.29504
Open DOI Search in Google Scholar Back to article
Dream S, Kuo JH, Wang TS. Virtual interactive presence, a novel approach to remote proctoring for the adoption of innovative technologies and interventions. Am J Surg. 2022 Mar;223(3):600-602. doi: 10.1016/j.amjsurg.2021.09.007
Open DOI Search in Google Scholar Back to article
Goel SS, Greenbaum AB, Patel A, et al. Role of Teleproctoring in Challenging and Innovative Structural Interventions Amid the COVID-19 Pandemic and Beyond. JACC Cardiovasc Interv. 2020 Aug 24;13(16):1945-1948. doi: 10.1016/j.jcin.2020.04.013
Open DOI Search in Google Scholar Back to article
Brisson P. Prevention of retained foreign objects. Bull Am Coll Surg. 2009 Nov;94(11):28-31.
Search in Google Scholar Back to article
Gawande AA, Studdert DM, Orav EJ, Brennan TA, Zinner MJ. Risk factors for retained instruments and sponges after surgery. N Engl J Med. 2003 Jan 16;348(3):229-35. doi: 10.1056/NEJMsa021721
Open DOI Search in Google Scholar Back to article
Zejnullahu VA, Bicaj BX, Zejnullahu VA, Hamza AR. Retained Surgical Foreign Bodies after Surgery. Open Access Maced J Med Sci. 2017 Mar 15;5(1):97-100. doi: 10.3889/oamjms.2017.005
Open DOI Search in Google Scholar Back to article
Smith ME, Andraska EA, Sutzko DC, Boniakowski AM, Coleman DM, Osborne NH. The decline of open abdominal aortic aneurysm surgery among individual training programs and vascular surgery trainees. J Vasc Surg. 2020 Apr;71(4):1371-1377. doi: 10.1016/j.jvs.2019.06.204
Open DOI Search in Google Scholar Back to article
Cochran K. Guidelines in Practice: Prevention of Unintentionally Retained Surgical Items. AORN J. 2022 Nov;116(5):427-440. doi: 10.1002/aorn.13804
Open DOI Search in Google Scholar Back to article
Hendricks W, Mecca J, Rahimi M, et al. Evaluation of a Novel System for RFID Intraoperative Cardiovascular Analytics. IEEE J Transl Eng Health Med. 2022 Aug 5;10:1900309. doi: 10.1109/JTEHM.2022.3196832
Open DOI Search in Google Scholar Back to article
Liu JC, Li KA, Yeh SL, Chien SY. Assessing Perceptual Load and Cognitive Load by Fixation-Related Information of Eye Movements. Sensors (Basel). 2022 Feb 4;22(3):1187. doi: 10.3390/s22031187
Open DOI Search in Google Scholar Back to article
Charles RL, Nixon J. Measuring mental workload using physiological measures: A systematic review. Appl Ergon. 2019 Jan;74:221-232. doi: 10.1016/j.apergo.2018.08.028
Open DOI Search in Google Scholar Back to article
Tao D, Tan H, Wang H, Zhang X, Qu X, Zhang T. A Systematic Review of Physiological Measures of Mental Workload. Int J Environ Res Public Health. 2019 Jul 30;16(15):2716. doi: 10.3390/ijerph16152716
Open DOI Search in Google Scholar Back to article
Dias RD, Ngo-Howard MC, Boskovski MT, Zenati MA, Yule SJ. Systematic review of measurement tools to assess surgeons’ intraoperative cognitive workload. Br J Surg. 2018 Apr;105(5):491-501. doi: 10.1002/bjs.10795
Open DOI Search in Google Scholar Back to article
Sussillo D, Barak O. Opening the black box: low-dimensional dynamics in high-dimensional recurrent neural networks. Neural Comput. 2013 Mar;25(3):626-49. doi: 10.1162/NECO_a_00409
Open DOI Search in Google Scholar Back to article
Cabitza F, Rasoini R, Gensini GF. Unintended Consequences of Machine Learning in Medicine. JAMA. 2017 Aug 8;318(6):517-518. doi: 10.1001/jama.2017.7797
Open DOI Search in Google Scholar Back to article
Safdar NM, Banja JD, Meltzer CC. Ethical considerations in artificial intelligence. Eur J Radiol. 2020 Jan;122:108768. doi: 10.1016/j.ejrad.2019.108768
Open DOI Search in Google Scholar Back to article
Naik N, Hameed BMZ, Shetty DK, et al. Legal and Ethical Consideration in Artificial Intelligence in Healthcare: Who Takes Responsibility? Front Surg. 2022 Mar 14;9:862322. doi: 10.3389/fsurg.2022.862322
Open DOI Search in Google Scholar Back to article

Articles in this issue

DOI: https://doi.org/10.14797/mdcvj.1263 | Journal eISSN: 1947-6108

Journal RSS Feed

Language: English

Page range: 17 - 23

Submitted on: May 17, 2023

Accepted on: Jun 21, 2023

Published on: Aug 1, 2023

Published by: Houston Methodist DeBakey Heart & Vascular Center

In partnership with: Paradigm Publishing Services

Keywords:

artificial intelligence,

AI,

telenursing,

telemedicine,

mental workload

© 2023 Peter Osztrogonacz, Ponraj Chinnadurai, Alan B. Lumsden, published by Houston Methodist DeBakey Heart & Vascular Center
This work is licensed under the Creative Commons Attribution-NonCommercial 4.0 License.

Volume 19 (2023): Issue 4