Rejyonel Anestezi Uygulamalarında Bütünleşik Yapay Zeka: Duyarlılığın, Verimliliğin, Sonuçların ve Sınırlamaların İyileştirilmesi

Abstract

Yapay zeka (YZ), birçok alanda insan yeteneklerini aşan olağanüstü ilerlemeler kaydetmiştir. YZ'nin, rejyonel anestezi de dahil olmak üzere sağlık uygulamalarında giderek daha fazla kullanılıyor olması şaşırtıcı değildir. Yapay zekadaki son gelişmeler, rejyonel anestezinin alanına entegrasyonunu mümkün kılmış ve hassasiyeti, verimliliği ve hasta sonuçlarını artırmayı vaat etmektedir. Makine öğrenimi algoritmaları ve tahmine dayalı analitikleri kullanarak, YZ'nin rejyonel anestezi prosedürlerinin yürütülme ve yönetilme biçimini devrim niteliğinde değiştirme potansiyeli bulunmaktadır. Ultrason eşliğinde yapılan rejyonel anestezi (UGRA), rejyonel blokların başarı oranlarını önemli ölçüde artırırken komplikasyon risklerini azaltmaktadır. Bu derleme, UGRA'daki Yapay Zeka'nın (YZ) artan rolünü detaylandırarak, sonografik görüntülemenin, hedef belirlemenin, iğne rehberliğinin ve lokal anestezik uygulamanın iyileştirilmesindeki kritik işlevini ve evrimini incelemektedir. Özellikle eğitimdeki uzman olmayanlar ve klinik uygulamada, ayrıca eğitim ortamlarındaki uzmanlar için YZ desteği paha biçilmezdir. Rejyonel anestezi alanındaki YZ'nin yeteneklerini ve uygulamalarını sistematik olarak analiz ederek, prosedürel hassasiyete, güvenliğe ve eğitsel ilerlemeye katkısını değerlendiriyoruz. Bulgular, YZ destekli UGRA'nın sadece anatomik tanımlamanın doğruluğunu artırarak hasta güvenliğini iyileştirmekle kalmayıp, aynı zamanda farklı uzmanlık seviyeleri arasında bakım kalitesini standartlaştırdığını ortaya koymaktadır. YZ'nin UGRA'ya entegrasyonu, artırılmış hassasiyet, verimlilik ve hasta odaklı bakım ile anestezi alanını dönüştürme vaadi taşıyan bir etki olarak ortaya çıkmaktadır.

Keywords

• Rejyonel anestezi
• yapay zeka
• ultrason eşliğinde bloklar
• makine öğrenimi
• tahmine dayalı analitik

Integrative Artificial Intelligence in Regional Anesthesia: Enhancing Precision, Efficiency, Outcomes and Limitations

Abstract

Artificial intelligence (AI) has made remarkable progress in various domains, outperforming human capabilities in many areas. It is no surprise that AI is being increasingly used in healthcare practices, including regional anesthesia. Recent advancements in AI have enabled its integration into the field of regional anesthesia, promising to enhance precision, efficiency, and patient outcomes. By utilizing machine learning algorithms and predictive analytics, AI has the potential to revolutionize the way regional anesthesia procedures are conducted and managed. Ultrasound-guided regional anesthesia (UGRA) significantly enhances the success rates of regional blocks while mitigating complication risks. This review scrutinizes the burgeoning role of artificial intelligence (AI) in UGRA, detailing its evolution and pivotal function in optimizing sonographic imaging, target delineation, needle guidance, and local anesthetic administration. AI's support is invaluable, particularly for non-experts in training and clinical practice and for experts in educational settings. By systematically analyzing the capabilities and applications of AI in regional anesthesia, we assess its contribution to procedural precision, safety, and educational advancement. The findings reveal that AI-assisted UGRA not only bolsters the accuracy of anatomical identification, thus improving patient safety, but also standardizes the quality of care across varying expertise levels. The integration of AI into UGRA emerges as a transformative influence in anesthesiology, promising to reshape the domain with enhanced precision, efficiency, and patient-centered care.

Keywords

• artificial ıntelligence
• ultrasound-guided blocks
• machine learning
• predictive analytics
• anesthesia safety
• procedural precision
• clinical decision support
• aı in healthcare
• anesthesia education

References

1. [1] J. Balavenkatasubramanian, S. Kumar, and R. Sanjayan, “Artificial intelligence in regional anaesthesia,” Indian Journal Anaesthesia, vol. 68, no. 1, pp. 100-104, 2024.
2. [2] M. Singhal, L. Gupta, Hirani K. “A comprehensive analysis and review of artificial intelligence in anaesthesia,” Cureus, vol. 15, no. 9, 2023.
3. [3] F.M. Pham. “Artificial intelligence-supported systems in anaesthesiology and its standpoint to date: a review,” Open Journal of Anesthesiology, vol. 13, no. 7, pp. 140-168, 2023.
4. [4] S. Garg, and M.C. Kapoor, “Role of artificial intelligence in perioperative monitoring in anaesthesia,” Indian Journal Anaesthesia, vol. 68, no.1, pp. 87-92, 2024.
5. [5] S. Lopes, G. Rocha, Guimarães-Pereira L. “Artificial intelligence and its clinical application in anaesthesiology: a systematic review,” Journal of Clinical Monitoring and Computing, pp. 1-13, 2023.
6. [6] P. Mathur, and M.L. Burns. “Artificial intelligence in critical care,” International Anesthesiology Clinics, vol. 57 no. 2, pp. 89-102, 2019.
7. [7] A. Singam. “Revolutionising patient care: A Comprehensive review of artificial intelligence applications in anaesthesi,” Cureus, vol. 15, no. 12, 2023.
8. [8] M. Komorowski, and A. Joosten, “AIM in anaesthesiology, in artificial intelligence in medicine,” Springer, pp. 1-16, 2021,

9. [9] F.T., Sönmez, “Emerging Technologies in Emergency Medicine: The Role of Artificial Intelligence and Robotics in Emergency Situations,” All Rights Reserved It may not be reproduced in any way without the written permission of the publisher and the editor, except for short excerpts for promotion by reference, p. 337, 2021.
10. [10] D. Viderman et al., “Artificial intelligence in ultrasound-guided regional anaesthesia: A scoping review,” Frontiers in Medicine, vol. 9, p. 994805, 2022.
11. [11] Hashimoto D.A., et al. “Artificial intelligence in anaesthesiology: current techniques, clinical applications, and limitations,” Anesthesiology, vol. 132, no. 2, pp. 379-394, 2020.
12. [12] M. Singh, Nath G. “Artificial intelligence and anaesthesia: A narrative review,” Saudi Journal of Anaesthesia, vol. 16, no. 1, p. 86, 2022.
13. [13] A. Karmakar, et al. “Advances and utility of artificial intelligence and robotics in regional anaesthesia: an overview of recent developments,” Cureus, vol. 15, no. 8, 2023.
14. [14] J. Lloyd, et al.. “Artificial intelligence: innovation to assist in the identification of sono-anatomy for ultrasound-guided regional anaesthesia,” Biomedical Visualisation: Volume 11, pp. 117-140, 2022.
15. [15] J.S. Bowness, et al. “Variability between human experts and artificial intelligence in identification of anatomical structures by ultrasound in regional anaesthesia: a framework for evaluation of assistive artificial intelligence,” British Journal of Anesthesia, 2023.
16. [16] J.S. Bowness et al.. “Assistive artificial intelligence for ultrasound image interpretation in regional anaesthesia: an external validation study,” British journal of Anesthesia, vol. 130, no. 2, pp. 217-225, 2023.
17. [17] Ε. Μoka, and J. Bowness, “Artificial Intelligence and Robotics in Regional Anaesthesia: Do they have a role?” Signa Vitae, vol. 17, p. 1. 2021.
18. [18] J.S. Bowness et al. “Evaluation of the impact of assistive artificial intelligence on ultrasound scanning for regional anaesthesia,” British Journal of Anesthesia, vol. 130, no. 2, pp. 226-233. 2023.
19. [19] S.V. Bersenev, J.M. Konyaeva, and O.M. Yurchenko, “Possibilities of using regional anesthesia with diode-laser transscleral cyclophotocoagulation in children: description of clinical cases.” Regional Anesthesia and Acute Pain Management, Vol. 17, no. 2, pp. 125-134, 2023.
20. [20] Z. Wu, and Y. Wang, “Development of guidance techniques for regional anesthesia: past, present and future,” Journal of pain research, pp. 1631-1641, 2021.
21. [21] W. Fan, et al., “Ultrasound image-guided nerve block combined with general anesthesia under an artificial intelligence algorithm on patients undergoing radical gastrectomy for gastric cancer during and after operation,” Computational and Mathematical Methods in Medicine, 2022.
22. [22] M,.Feinstein, et al., “Remote monitoring and artificial intelligence: outlook for 2050,” Anesthesia & Analgesia, vol. 138, no. 2, pp. 350-357, 2024.
23. [23] J. Bowness, et al., “ A pilot study to evaluate the utility of assistive artificial intelligence in ultrasound-guided regional anaesthesia,” Regional Anesthesia and Pain Medicine, vol. 70, no. Suppl 1, pp. A74-A75. 2021.
24. [24] J. Bowness, K. El‐Boghdadly, and D. Burckett‐St Laurent, “Artificial intelligence for image interpretation in ultrasound-guided regional anaesthesia,” Anaesthesia, vol. 76, no. 5, pp. 602-607, 2021.
25. [25] D.W. Hewson, and N.M. Bedforth, “Closing the gap: artificial intelligence applied to ultrasound-guided regional anaesthesia,” Elsevier, 2023.
26. [26] Y.M.R.A. Toble, “EP080 Artificial intelligence in regional anesthesia: current utility and limitations: Making Regional anesthesia powered by AI.” BMJ Publishing Group Ltd, 2023.
27. [27] K. James, et al., “AI-assisted ultrasound-guided regional anaesthesia for trauma patients: a service evaluation,” British Journal of Anesthesia, vol. 131, no. 3, p. e8, 2023.