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001484471 0247_ $$a10.1007/978-981-99-5925-9$$2doi
001484471 035__ $$aSP(OCoLC)1411310943
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001484471 050_4 $$aRD80.95$$b.X53 2024
001484471 08204 $$a617.96$$223/eng/20231208
001484471 24500 $$aArtificial intelligence in anesthesiology /$$cMing Xia, Hong Jiang, editors.
001484471 264_1 $$aSingapore :$$bSpringer,$$c2024.
001484471 300__ $$a1 online resource (113 p.)
001484471 336__ $$atext$$btxt$$2rdacontent
001484471 337__ $$acomputer$$bc$$2rdamedia
001484471 338__ $$aonline resource$$bcr$$2rdacarrier
001484471 500__ $$aArtificial Intelligence in Prediction for Intraoperative Hypotension and Hypoxemia
001484471 5050_ $$aIntro -- Contents -- Artificial Intelligence: An Overview -- 1 History of Artificial Intelligence in Medicine -- 2 Common Technologies of AIM -- 2.1 Machine Learning (ML) -- 2.2 Deep Learning (DL) -- 2.3 Expert System -- 2.4 Intelligent Robots -- 3 Healthcare Applications of AI -- 3.1 AI in Medical Imaging and Diagnostics -- 3.2 AI in Risk Prediction -- 3.3 AI as Assistive Therapy Tools -- 4 Applications of AI in Anesthesiology -- 4.1 Ultrasound-Guided Anesthesia -- 4.2 Anesthesia Monitoring -- 4.3 Anesthesia Event Prediction -- 4.4 Pain Management -- 4.5 Airway Management
001484471 5058_ $$a4.6 Clinical Decision Support System -- 4.7 Clinical Skill Training and Assessment -- 5 Challenges to AI Adoption in Medicine and the Way Forward -- 5.1 Limitations and Challenges -- 5.1.1 Role of Anesthesiologists -- 5.1.2 Data -- 5.1.3 Ethical Implications -- 5.2 Future Prospects -- 6 Conclusion -- References -- Machine Learning and Other Techniques in Artificial Intelligence -- 1 Machine Learning, a Key Subfield of AI -- 1.1 Types of Machine Learning -- 1.1.1 Supervised Learning -- 1.1.2 Unsupervised Learning -- 1.1.3 Reinforcement Learning
001484471 5058_ $$a2 Techniques and Models within Machine Learning -- 2.1 Fuzzy Logic -- 2.2 Classical Machine Learning -- 2.3 Neural Networks and Deep Learning -- 2.4 Bayesian Methods -- 3 Conclusion -- References -- Assistance of Artificial Intelligence in Ultrasound-Based Procedures -- 1 US Image Preprocessing -- 2 Algorithms for US Imaging Analysis -- 3 Clinical Application of US Images Using AI Techniques -- 3.1 Vessel Detection in US Images -- 3.1.1 Vessel Model -- 3.1.2 Vessel Candidate Search -- 3.1.3 Vessel Classifier -- 3.1.4 Outcomes
001484471 5058_ $$a3.2 Automatic Localization of the Needle Target for Ultrasound-Guided Epidural Injections -- References -- Artificial Intelligence in Anesthesia Control and Monitoring -- 1 Application of AI in BIS -- 2 EEG with a Deep Learning Approach -- 2.1 Common Spatial Patterns -- 2.2 Principal Component Analysis -- 2.3 Common Average Reference -- 2.4 Adaptive Filter -- 2.5 Independent Component Analysis -- 2.6 Power Spectrum Density -- 2.7 Auto Regressive Analysis -- 2.8 Wavelet Transform and Wavelet Packet Transform -- 2.9 Fast Fourier Transform -- 2.10 Linear Discriminant Analysis
001484471 5058_ $$a2.11 Support Vector Machine -- 2.12 Naive Bayes -- 2.13 Artificial Neural Network -- References -- Artificial Intelligence in Airway Management -- 1 AI in Prediction of Difficult Airway -- 1.1 Facial Analysis Techniques in Difficult Airway Prediction -- 1.2 Speech Features Analysis in Difficult Airway Prediction -- 2 AI and Intraoperative Airway Maintenance -- 2.1 Intraoperative Monitoring -- 2.2 Drug Administration -- 2.2.1 Closed-Loop Target Controlled Injection -- 2.2.2 Real-Time Control of Drug Administration -- 3 Clinical Decision Support System -- References
001484471 506__ $$aAccess limited to authorized users.
001484471 520__ $$aConsidering the rapid developments in digital and information technologies, artificial intelligence has long been a hot topic in medicine. This book discusses applications of artificial intelligence in anaesthesiology, including control of anesthesia, risk prediction, ultrasound guidance, pain management, and operating room logistics. This book first defines basic concepts of AI, and give a brief overview of a few algorithms frequently used in AI and machine learning. A review of current AI and machine learning applications for the prediction of anesthesia conditions is also discussed, including those for the prediction of difficult airways before surgery, of adverse events and sedation effects during surgery, and of vomiting and pain after surgery. Even without extensive promotion and clinical application, AI is in development in anesthesiology; furthermore, it has a great deal of potential to maintain further development in the future. Lastly, ethical and safety considerations are discussed alongside AI limitations and challenges in anesthesiology.
001484471 650_6 $$aIntelligence artificielle en médecine.
001484471 650_0 $$aAnesthesiology$$xResearch.
001484471 650_0 $$aArtificial intelligence$$xMedical applications.$$xMedical applications$$0(DLC)sh 88003000
001484471 655_0 $$aElectronic books.
001484471 7001_ $$aXia, Ming.
001484471 7001_ $$aJiang, Hong.
001484471 77608 $$iPrint version:$$aXia, Ming$$tArtificial Intelligence in Anesthesiology$$dSingapore : Springer,c2024
001484471 852__ $$bebk
001484471 85640 $$3Springer Nature$$uhttps://univsouthin.idm.oclc.org/login?url=https://link.springer.com/10.1007/978-981-99-5925-9$$zOnline Access$$91397441.1
001484471 909CO $$ooai:library.usi.edu:1484471$$pGLOBAL_SET
001484471 980__ $$aBIB
001484471 980__ $$aEBOOK
001484471 982__ $$aEbook
001484471 983__ $$aOnline
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