Güncel Anestezi Pratiğinde Yapay Zeka
Özet
Yapay zeka teknolojileri; anestezi derinliğinin tayini, ilaç dozajlaması ve zor hava yolu tahmini gibi kritik alanlarda hata payını minimize ederek klinik başarıyı artırmaktadır. Özellikle hipotansiyon ve hipoksemi gibi hayati komplikasyonların önceden tespit edilmesinde yüksek doğruluk oranları sunan bu sistemler, postoperatif bulantı-kusma riski ve ağrı yönetiminde de mevcut skorlama yöntemlerinden daha etkili sonuçlar vermektedir. Gelecekte farmakodinamik ve farmakokinetik prensiplere dayalı kişiselleştirilmiş anestezi uygulamalarının yaygınlaşmasıyla, hasta güvenliği ve memnuniyetinin en üst seviyeye çıkarılması hedeflenmektedir.
Artificial intelligence technologies enhance clinical success by minimizing error margins in critical areas such as anesthesia depth determination, drug dosing, and difficult airway prediction. These systems, which offer high accuracy rates in the early detection of vital complications like hypotension and hypoxemia, also yield more effective results than current scoring methods in postoperative nausea-vomiting risk and pain management. In the future, the expansion of personalized anesthesia applications based on pharmacodynamic and pharmacokinetic principles aims to maximize patient safety and satisfaction.
Referanslar
Eric J.T. High-performance medicine: the convergence of human and artificial intelligence. Naturemedicine. 2019;25:44-56.
Ibrahim A, Primakov S, Beuque M, et al. Radiomics for precision medicine: Current challenges, future prospects, and the proposal of a new framework. Elsevier. 2021;188:20-29
Aneja S, Chang E, Omuro A. Applications of artificial intelligence in neuro-oncology. Current Opinion in Neurology. 2019;32:850-856.
Lin C-S, Li Y-C, Mok MS, et al. Neural network modeling to predict the hypnotic effect of propofol bolus induction. Proc AMIA Symp. 2002;450-453.
Yi JM, Doh I, Lee SH, et al. Predictive performance of a new pharmacokinetic model for propofol in underweight patients during target-controlled infusion. Acta Anaesthesiol Scand. 2019; 63:448–454.
Sepúlveda PO, Cortínez LI, Recart A, et al. Predictive ability of propofol effect-site concentrations during fast and slow infusion rates. Acta Anaesthesiol Scand. 2010;54:447–452.
Santanen O, Svartling N. Haasio J, et al. Neural nets and prediction of the recovery rate from neuromuscular block. european Journal of Anaesthesiology. 2003;20:87-92.
Piepho T, Cavus E, Noppens R, et al. S1 guidelines on airway management. Anaesthesist 2015. 2016;64:27-40.
Heinrich S, Birkholz T, Irouschek A,et al. Incidences and predictors of difficult laryngoscopy in adult patients undergoing general anesthesia: A single-center analysis of 102,305 cases. J Anesth. 2013;27:815–821.
Connor CW. Artificial Intelligence and Machine Learning in Anesthesiology. Anesthesiology. 2019;131:1346–1359.
Hayasaka T, Kawano K, Kurihara K,et al. Creation of an artificial intelligence model for intubation difficulty classification by deep learning (convolutional neural network) using face images: an observational study. J Intensive Care. 2021;9:1–14.
Kouz K, Hoppe P, Briesenick L, et al. Intraoperative hypotension: Pathophysiology, clinical relevance, and therapeutic approaches. Indian J Anaesth. 2020;64:90-96.
Hatib F, Jian Z, Buddi S, et al. Machine-learning algorithm to predict hypotension based on high-fidelity arterial pressure waveform analysis. Anesthesiology. 2018:129:663-674.
Lundberg SM, Nair B, Vavilala MS, et al. Explainable machine-learning predictions for the prevention of hypoxaemia during surgery. Nat Biomed Eng. 2018;2:749–760.
Eberhart LHJ, Högel J, Seeling W, et al. Evaluation of three risk scores to predict postoperative, nausea and vomiting. Acta Anaesthesiol Scand. 2000;44:480–488.
Traeger M, Eberhart A, Geldner G, et al. Prediction of postoperative nausea and vomiting using an artificial neural network. Anaesthesist. 2003;52:1132–1138.
BIÇAK M, SALIK F, AKELMA H. Laparoskopik Safra Kesesi Ameliyatlarında Transversus Abdominis Plan Bloğu, İntravenöz Non-Steroid Anti İnflamatuar İlaç ve Lokal Anestezik İnfiltrasyonunun Karşılaştırılması. Harran Üniversitesi Tıp Fakültesi Derg. 2020;17:74–81.
Cowen R, Stasiowska MK, Laycock H, et al. Assessing pain objectively: the use of physiological markers. Anesthesia. 2015;70:828-847.
