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Schmierer T, Li T, Li Y. Harnessing machine learning for EEG signal analysis: Innovations in depth of anaesthesia assessment. Artif Intell Med 2024; 151:102869. [PMID: 38593683 DOI: 10.1016/j.artmed.2024.102869] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/28/2023] [Revised: 01/31/2024] [Accepted: 04/03/2024] [Indexed: 04/11/2024]
Abstract
Anaesthesia, crucial to surgical practice, is undergoing renewed scrutiny due to the integration of artificial intelligence in its medical use. The precise control over the temporary loss of consciousness is vital to ensure safe, pain-free procedures. Traditional methods of depth of anaesthesia (DoA) assessment, reliant on physical characteristics, have proven inconsistent due to individual variations. In response, electroencephalography (EEG) techniques have emerged, with indices such as the Bispectral Index offering quantifiable assessments. This literature review explores the current scope and frontier of DoA research, emphasising methods utilising EEG signals for effective clinical monitoring. This review offers a critical synthesis of recent advances, specifically focusing on electroencephalography (EEG) techniques and their role in enhancing clinical monitoring. By examining 117 high-impact papers, the review delves into the nuances of feature extraction, model building, and algorithm design in EEG-based DoA analysis. Comparative assessments of these studies highlight their methodological approaches and performance, including clinical correlations with established indices like the Bispectral Index. The review identifies knowledge gaps, particularly the need for improved collaboration for data access, which is essential for developing superior machine learning models and real-time predictive algorithms for patient management. It also calls for refined model evaluation processes to ensure robustness across diverse patient demographics and anaesthetic agents. The review underscores the potential of technological advancements to enhance precision, safety, and patient outcomes in anaesthesia, paving the way for a new standard in anaesthetic care. The findings of this review contribute to the ongoing discourse on the application of EEG in anaesthesia, providing insights into the potential for technological advancement in this critical area of medical practice.
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Affiliation(s)
- Thomas Schmierer
- School of Mathematics, Physics and Computing, University of Southern Queensland, Australia.
| | - Tianning Li
- School of Mathematics, Physics and Computing, University of Southern Queensland, Australia.
| | - Yan Li
- School of Mathematics, Physics and Computing, University of Southern Queensland, Australia.
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Lopes S, Rocha G, Guimarães-Pereira L. Artificial intelligence and its clinical application in Anesthesiology: a systematic review. J Clin Monit Comput 2024; 38:247-259. [PMID: 37864754 PMCID: PMC10995017 DOI: 10.1007/s10877-023-01088-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/11/2023] [Accepted: 10/04/2023] [Indexed: 10/23/2023]
Abstract
PURPOSE Application of artificial intelligence (AI) in medicine is quickly expanding. Despite the amount of evidence and promising results, a thorough overview of the current state of AI in clinical practice of anesthesiology is needed. Therefore, our study aims to systematically review the application of AI in this context. METHODS A systematic review was conducted according to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. We searched Medline and Web of Science for articles published up to November 2022 using terms related with AI and clinical practice of anesthesiology. Articles that involved animals, editorials, reviews and sample size lower than 10 patients were excluded. Characteristics and accuracy measures from each study were extracted. RESULTS A total of 46 articles were included in this review. We have grouped them into 4 categories with regard to their clinical applicability: (1) Depth of Anesthesia Monitoring; (2) Image-guided techniques related to Anesthesia; (3) Prediction of events/risks related to Anesthesia; (4) Drug administration control. Each group was analyzed, and the main findings were summarized. Across all fields, the majority of AI methods tested showed superior performance results compared to traditional methods. CONCLUSION AI systems are being integrated into anesthesiology clinical practice, enhancing medical professionals' skills of decision-making, diagnostic accuracy, and therapeutic response.
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Affiliation(s)
- Sara Lopes
- Department of Anesthesiology, Centro Hospitalar Universitário São João, Porto, Portugal.
| | - Gonçalo Rocha
- Surgery and Physiology Department, Faculty of Medicine, University of Porto, Porto, Portugal
| | - Luís Guimarães-Pereira
- Department of Anesthesiology, Centro Hospitalar Universitário São João, Porto, Portugal
- Surgery and Physiology Department, Faculty of Medicine, University of Porto, Porto, Portugal
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Jiang Y, Sleigh J. Consciousness and General Anesthesia: Challenges for Measuring the Depth of Anesthesia. Anesthesiology 2024; 140:313-328. [PMID: 38193734 DOI: 10.1097/aln.0000000000004830] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/10/2024]
Abstract
The optimal consciousness level required for general anesthesia with surgery is unclear, but in existing practice, anesthetic oblivion, may be incomplete. This article discusses the concept of consciousness, how it is altered by anesthetics, the challenges for assessing consciousness, currently used technologies for assessing anesthesia levels, and future research directions. Wakefulness is marked by a subjective experience of existence (consciousness), perception of input from the body or the environment (connectedness), the ability for volitional responsiveness, and a sense of continuity in time. Anesthetic drugs may selectively impair some of these components without complete extinction of the subjective experience of existence. In agreement with Sanders et al. (2012), the authors propose that a state of disconnected consciousness is the optimal level of anesthesia, as it likely avoids both awareness and the possible dangers of oversedation. However, at present, there are no reliably tested indices that can discriminate between connected consciousness, disconnected consciousness, and complete unconsciousness.
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Affiliation(s)
- Yandong Jiang
- Department of Anesthesiology, Critical Care and Pain Medicine, McGovern Medical School, University of Texas Health Science Center at Houston, Houston, Texas
| | - Jamie Sleigh
- Department of Anesthesiology, University of Auckland, Hamilton, New Zealand
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Fang Z, Zou D, Xiong W, Bao H, Zhao X, Chen C, Si Y, Zou J. Dynamic prediction of hypoxemia risk at different time points based on preoperative and intraoperative features: machine learning applications in outpatients undergoing esophagogastroduodenoscopy. Ann Med 2023; 55:1156-1167. [PMID: 37140918 PMCID: PMC10161946 DOI: 10.1080/07853890.2023.2187878] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 05/05/2023] Open
Abstract
BACKGROUND Hypoxemia often occurs in outpatients undergoing anesthesia-assisted esophagogastroduodenoscopy (EGD). However, there is a scarcity in tools to predict the hypoxemia risk. We aimed to solve this problem by developing and validating machine learning (ML) models based on preoperative and intraoperative features. METHODS All data were retrospectively collected from June 2021 to February 2022. The most appropriate predictive features were selected by the least absolute shrinkage and selection operator, which were incorporated and modelled by 4 ML algorithms. The area under the precision-recall curve (AUPRC) was used as the main evaluation metric to select the best models, and the selected models were compared with the STOP-BANG score. Their predictive performance was visually interpreted by SHapley Additive exPlanations. The primary endpoint of this study was hypoxemia during the procedure, defined as at least one reading of pulse oximetry < 90% without probes misplacement from the anesthesia induction beginning to the end of EGD, while the secondary endpoint was hypoxemia during induction, from the induction beginning to the start of endoscopic intubation. RESULTS Of 1160 patients in the derivation cohort, 112 patients (9.6%) developed intraoperative hypoxemia, of which 102 (8.8%) occurred during the induction period. In temporal and external validation, no matter whether based on preoperative variables or still based on preoperative plus intraoperative variables, our models showed excellent predictive performance for the two endpoints, significantly better than STOP-BANG score. In the model interpretation section, preoperative variables (airway assessment indicators, pulse oximeter oxygen saturation and BMI) and intraoperative variables (the induced propofol dose) made the highest contribution to the predictions. To our knowledge, our ML models were the first to predict hypoxemia risk, which achieved excellent overall predictive ability integrating various clinical indicators. These models have the potential to become an effective tool for adjusting sedation strategies flexibly and reducing the workload of anesthesiologists.KEY MESSAGESThis study is the first model employing ML methods based on preoperative and preoperative plus intraoperative variables for predicting the risk of hypoxemia during induction and the whole EGD procedure respectively.Our four models achieved satisfactory predictive performance and outperformed STOP-BANG score in terms of AUPRC in the temporal and external validation cohorts respectively.We found that the relevant variables of airway assessment should be fully taken into account when analyzing the risk factor of hypoxemia, and the effect of patients' age on their hypoxemia risk should be considered in conjunction with the propofol dose.
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Affiliation(s)
- Zhaojing Fang
- Department of Anesthesiology, Nanjing First Hospital, Nanjing Medical University, Nanjing, P.R. China
| | - Daizun Zou
- School of Basic Medicine and Clinical Pharmacy, China Pharmaceutical University, Nanjing, P.R. China
- Department of Clinical Pharmacology, Nanjing First Hospital, Nanjing Medical University, Nanjing, P.R. China
| | - Weigen Xiong
- School of Basic Medicine and Clinical Pharmacy, China Pharmaceutical University, Nanjing, P.R. China
- Department of Clinical Pharmacology, Nanjing First Hospital, Nanjing Medical University, Nanjing, P.R. China
| | - Hongguang Bao
- Department of Anesthesiology, Nanjing First Hospital, Nanjing Medical University, Nanjing, P.R. China
| | - Xiuxiu Zhao
- Department of Anesthesiology, Nanjing First Hospital, Nanjing Medical University, Nanjing, P.R. China
| | - Chen Chen
- Department of Clinical Pharmacology, Nanjing First Hospital, Nanjing Medical University, Nanjing, P.R. China
- Department of Pharmacy, Nanjing First Hospital, China Pharmaceutical University, Nanjing, P.R. China
| | - Yanna Si
- Department of Anesthesiology, Nanjing First Hospital, Nanjing Medical University, Nanjing, P.R. China
| | - Jianjun Zou
- Department of Clinical Pharmacology, Nanjing First Hospital, Nanjing Medical University, Nanjing, P.R. China
- Department of Pharmacy, Nanjing First Hospital, China Pharmaceutical University, Nanjing, P.R. China
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Ren J, Xiao H. Exercise Intervention for Alzheimer's Disease: Unraveling Neurobiological Mechanisms and Assessing Effects. Life (Basel) 2023; 13:2285. [PMID: 38137886 PMCID: PMC10744739 DOI: 10.3390/life13122285] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/31/2023] [Revised: 11/26/2023] [Accepted: 11/29/2023] [Indexed: 12/24/2023] Open
Abstract
Alzheimer's disease (AD) is a progressive neurodegenerative disease and a major cause of age-related dementia, characterized by cognitive dysfunction and memory impairment. The underlying causes include the accumulation of beta-amyloid protein (Aβ) in the brain, abnormal phosphorylation, and aggregation of tau protein within nerve cells, as well as neuronal damage and death. Currently, there is no cure for AD with drug therapy. Non-pharmacological interventions such as exercise have been widely used to treat AD, but the specific molecular and biological mechanisms are not well understood. In this narrative review, we integrate the biology of AD and summarize the knowledge of the molecular, neural, and physiological mechanisms underlying exercise-induced improvements in AD progression. We discuss various exercise interventions used in AD and show that exercise directly or indirectly affects the brain by regulating crosstalk mechanisms between peripheral organs and the brain, including "bone-brain crosstalk", "muscle-brain crosstalk", and "gut-brain crosstalk". We also summarize the potential role of artificial intelligence and neuroimaging technologies in exercise interventions for AD. We emphasize that moderate-intensity, regular, long-term exercise may improve the progression of Alzheimer's disease through various molecular and biological pathways, with multimodal exercise providing greater benefits. Through in-depth exploration of the molecular and biological mechanisms and effects of exercise interventions in improving AD progression, this review aims to contribute to the existing knowledge base and provide insights into new therapeutic strategies for managing AD.
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Affiliation(s)
- Jianchang Ren
- Institute of Sport and Health, Guangdong Provincial Kay Laboratory of Development and Education for Special Needs Child, Lingnan Normal University, Zhanjiang 524037, China
- Institute of Sport and Health, South China Normal University, Guangzhou 510631, China
| | - Haili Xiao
- Institute of Sport and Health, Lingnan Normal University, Zhanjiang 524037, China;
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Chen M, He Y, Yang Z. A Deep Learning Framework for Anesthesia Depth Prediction from Drug Infusion History. SENSORS (BASEL, SWITZERLAND) 2023; 23:8994. [PMID: 37960693 PMCID: PMC10650919 DOI: 10.3390/s23218994] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/07/2023] [Revised: 10/28/2023] [Accepted: 10/31/2023] [Indexed: 11/15/2023]
Abstract
In the target-controlled infusion (TCI) of propofol and remifentanil intravenous anesthesia, accurate prediction of the depth of anesthesia (DOA) is very challenging. Patients with different physiological characteristics have inconsistent pharmacodynamic responses during different stages of anesthesia. For example, in TCI, older adults transition smoothly from the induction period to the maintenance period, while younger adults are more prone to anesthetic awareness, resulting in different DOA data distributions among patients. To address these problems, a deep learning framework that incorporates domain adaptation and knowledge distillation and uses propofol and remifentanil doses at historical moments to continuously predict the bispectral index (BIS) is proposed in this paper. Specifically, a modified adaptive recurrent neural network (AdaRNN) is adopted to address data distribution differences among patients. Moreover, a knowledge distillation pipeline is developed to train the prediction network by enabling it to learn intermediate feature representations of the teacher network. The experimental results show that our method exhibits better performance than existing approaches during all anesthetic phases in the TCI of propofol and remifentanil intravenous anesthesia. In particular, our method outperforms some state-of-the-art methods in terms of root mean square error and mean absolute error by 1 and 0.8, respectively, in the internal dataset as well as in the publicly available dataset.
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Affiliation(s)
| | | | - Zhijing Yang
- School of Information Engineering, Guangdong University of Technology, Guangzhou 510006, China; (M.C.); (Y.H.)
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Singhal M, Gupta L, Hirani K. A Comprehensive Analysis and Review of Artificial Intelligence in Anaesthesia. Cureus 2023; 15:e45038. [PMID: 37829964 PMCID: PMC10566398 DOI: 10.7759/cureus.45038] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 09/11/2023] [Indexed: 10/14/2023] Open
Abstract
In the field of anaesthesia, artificial intelligence (AI) has become a game-changing technology. Applications of AI include keeping records, monitoring patients, calculating and administering drugs, and carrying out mechanical procedures. This article explores the current uses, challenges, and prospective applications of AI in anaesthesia practices. This review discusses AI-supported systems like anaesthesia information management systems (AIMS), mechanical robots for carrying out procedures, and pharmacological models for drug delivery. AIMS has helped in automated record-keeping, predicting bad events, and monitoring the vital signs of the patient. Their application has a vital role in improving the efficacy of anaesthesia management and patient safety. The application of AI in anaesthesia comes with its own unique difficulties. Noteworthy obstacles include issues with data quantity and quality, technical limitations, and moral and legal dilemmas. The key to overcoming these barriers is to set guidelines for the ethical use of AI in healthcare, improve the reliability and comprehension of AI systems, and certify the health data precision and security. AI has very bright potential. Exciting future directions include developments in AI and machine learning thus development of new applications, and the possible enhancement in training and education. Potential research areas include the application of AI to chronic disease management, pain management, and the reinforcement of anaesthesiologists' education. AI could be used to design authentic lifelike training simulations and individualized student feedback systems, hence transforming anaesthesia education and training methodology. For this review, we conducted a PubMed, Google Scholar, and Cochrane Database search in 2022-2023 and retrieved articles on AI and its uses in anaesthesia. Recommendations for future research and development include strengthening the safety and reliability of health data, building a better understanding of AI systems, and looking into new areas of use. The power of AI can be used to innovate anaesthesia practices by concentrating on these areas.
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Affiliation(s)
- Meghna Singhal
- Department of Anesthesiology and Critical Care, Maulana Azad Medical College, Delhi, IND
| | - Lalit Gupta
- Department of Anesthesiology and Critical Care, Maulana Azad Medical College, Delhi, IND
| | - Kshitiz Hirani
- Department of Anesthesiology and Critical Care, University College of Medical Sciences and Guru Teg Bahadur Hospital, Delhi, IND
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Hwang E, Park HS, Kim HS, Kim JY, Jeong H, Kim J, Kim SH. Development of a Bispectral index score prediction model based on an interpretable deep learning algorithm. Artif Intell Med 2023; 143:102569. [PMID: 37673590 DOI: 10.1016/j.artmed.2023.102569] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/23/2022] [Revised: 04/24/2023] [Accepted: 04/27/2023] [Indexed: 09/08/2023]
Abstract
BACKGROUND Proper maintenance of hypnosis is crucial for ensuring the safety of patients undergoing surgery. Accordingly, indicators, such as the Bispectral index (BIS), have been developed to monitor hypnotic levels. However, the black-box nature of the algorithm coupled with the hardware makes it challenging to understand the underlying mechanisms of the algorithms and integrate them with other monitoring systems, thereby limiting their use. OBJECTIVE We propose an interpretable deep learning model that forecasts BIS values 25 s in advance using 30 s electroencephalogram (EEG) data. MATERIAL AND METHODS The proposed model utilized EEG data as a predictor, which is then decomposed into amplitude and phase components using fast Fourier Transform. An attention mechanism was applied to interpret the importance of these components in predicting BIS. The predictability of the model was evaluated on both regression and binary classification tasks, where the former involved predicting a continuous BIS value, and the latter involved classifying a dichotomous status at a BIS value of 60. To evaluate the interpretability of the model, we analyzed the attention values expressed in the amplitude and phase components according to five ranges of BIS values. The proposed model was trained and evaluated using datasets collected from two separate medical institutions. RESULTS AND CONCLUSION The proposed model achieved excellent performance on both the internal and external validation datasets. The model achieved a root-mean-square error of 6.614 for the regression task, and an area under the receiver operating characteristic curve of 0.937 for the binary classification task. Interpretability analysis provided insight into the relationship between EEG frequency components and BIS values. Specifically, the attention mechanism revealed that higher BIS values were associated with increased amplitude attention values in high-frequency bands and increased phase attention values in various frequency bands. This finding is expected to facilitate a more profound understanding of the BIS prediction mechanism, thereby contributing to the advancement of anesthesia technologies.
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Affiliation(s)
- Eugene Hwang
- School of Management Engineering, Korea Advanced Institute of Science and Technology, Seoul, Republic of Korea.
| | - Hee-Sun Park
- Biosignal Analysis and Perioperative Outcome Research Laboratory, Department of Anesthesiology and Pain Medicine, Asan Medical Center, University of Ulsan College of Medicine, Seoul, Republic of Korea.
| | - Hyun-Seok Kim
- Biomedical Engineering Research Center, Asan Institute for Life Sciences, Asan Medical Center, Seoul, Republic of Korea
| | - Jin-Young Kim
- Biosignal Analysis and Perioperative Outcome Research Laboratory, Department of Anesthesiology and Pain Medicine, Asan Medical Center, University of Ulsan College of Medicine, Seoul, Republic of Korea; Department of Medical Engineering, University of Ulsan College of Medicine, Seoul, Republic of Korea
| | - Hanseok Jeong
- Department of Electrical and Computer Engineering, University of Seoul, Seoul, Republic of Korea
| | - Junetae Kim
- Graduate School of Cancer Science and Policy, National Cancer Center, Goyang-si, Gyeonggi-do, Republic of Korea; Healthcare AI Team, Healthcare Platform Center, National Cancer Center, Goyang-si, Gyeonggi-do, Republic of Korea.
| | - Sung-Hoon Kim
- Biosignal Analysis and Perioperative Outcome Research Laboratory, Department of Anesthesiology and Pain Medicine, Asan Medical Center, University of Ulsan College of Medicine, Seoul, Republic of Korea.
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He Y, Peng S, Chen M, Yang Z, Chen Y. A Transformer-Based Prediction Method for Depth of Anesthesia During Target-Controlled Infusion of Propofol and Remifentanil. IEEE Trans Neural Syst Rehabil Eng 2023; 31:3363-3374. [PMID: 37581963 DOI: 10.1109/tnsre.2023.3305363] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 08/17/2023]
Abstract
Accurately predicting anesthetic effects is essential for target-controlled infusion systems. The traditional (PK-PD) models for Bispectral index (BIS) prediction require manual selection of model parameters, which can be challenging in clinical settings. Recently proposed deep learning methods can only capture general trends and may not predict abrupt changes in BIS. To address these issues, we propose a transformer-based method for predicting the depth of anesthesia (DOA) using drug infusions of propofol and remifentanil. Our method employs long short-term memory (LSTM) and gate residual network (GRN) networks to improve the efficiency of feature fusion and applies an attention mechanism to discover the interactions between the drugs. We also use label distribution smoothing and reweighting losses to address data imbalance. Experimental results show that our proposed method outperforms traditional PK-PD models and previous deep learning methods, effectively predicting anesthetic depth under sudden and deep anesthesia conditions.
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McDermott SP, Wasan AD. Using Natural Language Processing and Machine Learning to Identify Opioids in Electronic Health Record Data. J Pain Res 2023; 16:2133-2140. [PMID: 37361429 PMCID: PMC10290467 DOI: 10.2147/jpr.s389160] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/20/2022] [Accepted: 04/27/2023] [Indexed: 06/28/2023] Open
Abstract
Purpose This study evaluates the utility of machine learning (ML) and natural language processing (NLP) in the processing and initial analysis of data within the electronic health record (EHR). We present and evaluate a method to classify medication names as either opioids or non-opioids using ML and NLP. Patients and Methods A total of 4216 distinct medication entries were obtained from the EHR and were initially labeled by human reviewers as opioid or non-opioid medications. An approach incorporating bag-of-words NLP and supervised ML classification was implemented in MATLAB and used to automatically classify medications. The automated method was trained on 60% of the input data, evaluated on the remaining 40%, and compared to manual classification results. Results A total of 3991 medication strings were classified as non-opioid medications (94.7%), and 225 were classified as opioid medications by the human reviewers (5.3%). The algorithm achieved a 99.6% accuracy, 97.8% sensitivity, 94.6% positive predictive value, F1 value of 0.96, and a receiver operating characteristic (ROC) curve with 0.998 area under the curve (AUC). A secondary analysis indicated that approximately 15-20 opioids (and 80-100 non-opioids) were needed to achieve accuracy, sensitivity, and AUC values of above 90-95%. Conclusion The automated approach achieved excellent performance in classifying opioids or non-opioids, even with a practical number of human reviewed training examples. This will allow a significant reduction in manual chart review and improve data structuring for retrospective analyses in pain studies. The approach may also be adapted to further analysis and predictive analytics of EHR and other "big data" studies.
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Affiliation(s)
- Sean P McDermott
- Division of Pain Medicine, Department of Anesthesiology and Perioperative Medicine, University of Pittsburgh Medical Center, Pittsburgh, PA, USA
| | - Ajay D Wasan
- Division of Pain Medicine, Department of Anesthesiology and Perioperative Medicine, University of Pittsburgh Medical Center, Pittsburgh, PA, USA
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11
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Yan FJ, Chen XH, Quan XQ, Wang LL, Wei XY, Zhu JL. Development and validation of an interpretable machine learning model-Predicting mild cognitive impairment in a high-risk stroke population. Front Aging Neurosci 2023; 15:1180351. [PMID: 37396650 PMCID: PMC10308219 DOI: 10.3389/fnagi.2023.1180351] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/06/2023] [Accepted: 05/30/2023] [Indexed: 07/04/2023] Open
Abstract
Background Mild cognitive impairment (MCI) is considered a preclinical stage of Alzheimer's disease (AD). People with MCI have a higher risk of developing dementia than healthy people. As one of the risk factors for MCI, stroke has been actively treated and intervened. Therefore, selecting the high-risk population of stroke as the research object and discovering the risk factors of MCI as early as possible can prevent the occurrence of MCI more effectively. Methods The Boruta algorithm was used to screen variables, and eight machine learning models were established and evaluated. The best performing models were used to assess variable importance and build an online risk calculator. Shapley additive explanation is used to explain the model. Results A total of 199 patients were included in the study, 99 of whom were male. Transient ischemic attack (TIA), homocysteine, education, hematocrit (HCT), diabetes, hemoglobin, red blood cells (RBC), hypertension, prothrombin time (PT) were selected by Boruta algorithm. Logistic regression (AUC = 0.8595) was the best model for predicting MCI in high-risk groups of stroke, followed by elastic network (ENET) (AUC = 0.8312), multilayer perceptron (MLP) (AUC = 0.7908), extreme gradient boosting (XGBoost) (AUC = 0.7691), and support vector machine (SVM) (AUC = 0.7527), random forest (RF) (AUC = 0.7451), K-nearest neighbors (KNN) (AUC = 0.7380), decision tree (DT) (AUC = 0.6972). The importance of variables suggests that TIA, diabetes, education, and hypertension are the top four variables of importance. Conclusion Transient ischemic attack (TIA), diabetes, education, and hypertension are the most important risk factors for MCI in high-risk groups of stroke, and early intervention should be performed to reduce the occurrence of MCI.
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Affiliation(s)
- Feng-Juan Yan
- Department of Geriatrics, Shenzhen Longhua District Central Hospital, Shenzhen, Guangdong, China
| | - Xie-Hui Chen
- Department of Geriatrics, Shenzhen Longhua District Central Hospital, Shenzhen, Guangdong, China
| | - Xiao-Qing Quan
- Department of Geriatrics, Shenzhen Longhua District Central Hospital, Shenzhen, Guangdong, China
| | - Li-Li Wang
- Department of Cardiology, Affiliated Hospital of Shandong University of Traditional Chinese Medicine, Jinan, Shandong, China
| | - Xin-Yi Wei
- Department of Cardiology, The Third Hospital of Jinan, Jinan, Shandong, China
| | - Jia-Liang Zhu
- The First Affiliated Hospital of Jinan University, Guangzhou, Guangdong, China
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Davoud SC, Kovacheva VP. On the Horizon: Specific Applications of Automation and Artificial Intelligence in Anesthesiology. CURRENT ANESTHESIOLOGY REPORTS 2023; 13:31-40. [PMID: 38106626 PMCID: PMC10722862 DOI: 10.1007/s40140-023-00558-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 03/19/2023] [Indexed: 04/08/2023]
Abstract
Purpose of Review The purpose of this review is to summarize the current research and critically examine artificial intelligence (AI) technologies and their applicability to the daily practice of anesthesiologists. Recent Findings Novel AI tools are developed using data from electronic health records, imaging, waveforms, clinical notes, and wearables. These tools can accurately predict the perioperative risk for adverse outcomes, the need for blood transfusion, and the risk of difficult intubation. Intraoperatively, AI models can assist with technical skill augmentation, patient monitoring, and management. Postoperatively, AI technology can aid in preventing complications and discharge planning. While further prospective validation is needed, these early applications demonstrate promise in every area of perioperative care. Summary The practice of anesthesiology is at a precipice fueled by technological innovation. The clinical AI implementation would enable personalized and safer patient care by offering actionable insights from the wealth of perioperative data.
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Affiliation(s)
- Sherwin C. Davoud
- Department of Anesthesiology, Perioperative and Pain Medicine, Brigham and Women’s Hospital, Harvard Medical School, 75 Francis St., L1, Boston, MA, USA
| | - Vesela P. Kovacheva
- Department of Anesthesiology, Perioperative and Pain Medicine, Brigham and Women’s Hospital, Harvard Medical School, 75 Francis St., L1, Boston, MA, USA
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Cascella M, Tracey MC, Petrucci E, Bignami EG. Exploring Artificial Intelligence in Anesthesia: A Primer on Ethics, and Clinical Applications. SURGERIES 2023; 4:264-274. [DOI: 10.3390/surgeries4020027] [Citation(s) in RCA: 8] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 09/12/2023] Open
Abstract
The field of anesthesia has always been at the forefront of innovation and technology, and the integration of Artificial Intelligence (AI) represents the next frontier in anesthesia care. The use of AI and its subtypes, such as machine learning, has the potential to improve efficiency, reduce costs, and ameliorate patient outcomes. AI can assist with decision making, but its primary advantage lies in empowering anesthesiologists to adopt a proactive approach to address clinical issues. The potential uses of AI in anesthesia can be schematically grouped into clinical decision support and pharmacologic and mechanical robotic applications. Tele-anesthesia includes strategies of telemedicine, as well as device networking, for improving logistics in the operating room, and augmented reality approaches for training and assistance. Despite the growing scientific interest, further research and validation are needed to fully understand the benefits and limitations of these applications in clinical practice. Moreover, the ethical implications of AI in anesthesia must also be considered to ensure that patient safety and privacy are not compromised. This paper aims to provide a comprehensive overview of AI in anesthesia, including its current and potential applications, and the ethical considerations that must be considered to ensure the safe and effective use of the technology.
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Affiliation(s)
- Marco Cascella
- Pain Unit and Research, Istituto Nazionale Tumori IRCCS Fondazione Pascale, 80100 Napoli, Italy
| | - Maura C. Tracey
- Rehabilitation Medicine Unit, Strategic Health Services Department, Istituto Nazionale Tumori-IRCCS-Fondazione Pascale, 80100 Naples, Italy
| | - Emiliano Petrucci
- Department of Anesthesia and Intensive Care Unit, San Salvatore Academic Hospital of L’Aquila, 67100 L’Aquila, Italy
| | - Elena Giovanna Bignami
- Anesthesiology, Critical Care and Pain Medicine Division, Department of Medicine and Surgery, University of Parma, 43126 Parma, Italy
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Yun WJ, Shin M, Jung S, Ko J, Lee HC, Kim J. Deep reinforcement learning-based propofol infusion control for anesthesia: A feasibility study with a 3000-subject dataset. Comput Biol Med 2023; 156:106739. [PMID: 36889025 DOI: 10.1016/j.compbiomed.2023.106739] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/12/2022] [Revised: 11/26/2022] [Accepted: 03/01/2023] [Indexed: 03/06/2023]
Abstract
In this work, we present a deep reinforcement learning-based approach as a baseline system for autonomous propofol infusion control. Specifically, design an environment for simulating the possible conditions of a target patient based on input demographic data and design our reinforcement learning model-based system so that it effectively makes predictions on the proper level of propofol infusion to maintain stable anesthesia even under dynamic conditions that can affect the decision-making process, such as the manual control of remifentanil by anesthesiologists and the varying patient conditions under anesthesia. Through an extensive set of evaluations using patient data from 3000 subjects, we show that the proposed method results in stabilization in the anesthesia state, by managing the bispectral index (BIS) and effect-site concentration for a patient showing varying conditions.
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Affiliation(s)
- Won Joon Yun
- School of Electrical Engineering, Korea University, Seoul 02841, Republic of Korea.
| | | | - Soyi Jung
- Department of Electrical and Computer Engineering, Ajou University, Suwon 16499, Republic of Korea.
| | - JeongGil Ko
- School of Integrated Technology, Yonsei University, Seoul 03722, Republic of Korea.
| | - Hyung-Chul Lee
- Seoul National University College of Medicine & Seoul National University Hospital, Seoul 03080, Republic of Korea.
| | - Joongheon Kim
- School of Electrical Engineering, Korea University, Seoul 02841, Republic of Korea.
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15
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Johnson M, Patel M, Phipps A, van der Schaar M, Boulton D, Gibbs M. The potential and pitfalls of artificial intelligence in clinical pharmacology. CPT Pharmacometrics Syst Pharmacol 2023; 12:279-284. [PMID: 36717763 PMCID: PMC10014043 DOI: 10.1002/psp4.12902] [Citation(s) in RCA: 8] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/21/2022] [Revised: 11/30/2022] [Accepted: 12/05/2022] [Indexed: 02/01/2023] Open
Affiliation(s)
- Martin Johnson
- Clinical Pharmacology and Quantitative Pharmacology, Clinical Pharmacology and Safety Science, R&D, AstraZeneca, Cambridge, UK
| | - Mishal Patel
- Clinical Pharmacology and Quantitative Pharmacology, Artificial Intelligence & Data Analytics, R&D, AstraZeneca, Cambridge, UK
| | - Alex Phipps
- Clinical Pharmacology and Quantitative Pharmacology, Clinical Pharmacology and Safety Science, R&D, AstraZeneca, Cambridge, UK
| | - Mihaela van der Schaar
- Cambridge Centre for Artificial Intelligence in Medicine, Department of Applied Mathematics and Theoretical Physics and Department of Population Health, University of Cambridge, Cambridge, UK
| | - Dave Boulton
- Clinical Pharmacology and Quantitative Pharmacology, Clinical Pharmacology & Safety Sciences, R&D, AstraZeneca, Gaithersburg, Maryland, USA
| | - Megan Gibbs
- Clinical Pharmacology and Quantitative Pharmacology, Clinical Pharmacology & Safety Sciences, R&D, AstraZeneca, Gaithersburg, Maryland, USA
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Prediction of postoperative infection in elderly using deep learning-based analysis: an observational cohort study. Aging Clin Exp Res 2023; 35:639-647. [PMID: 36598653 PMCID: PMC10014765 DOI: 10.1007/s40520-022-02325-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/06/2022] [Accepted: 12/09/2022] [Indexed: 01/05/2023]
Abstract
Elderly patients are susceptible to postoperative infections with increased mortality. Analyzing with a deep learning model, the perioperative factors that could predict and/or contribute to postoperative infections may improve the outcome in elderly. This was an observational cohort study with 2014 elderly patients who had elective surgery from 28 hospitals in China from April to June 2014. We aimed to develop and validate deep learning-based predictive models for postoperative infections in the elderly. 1510 patients were randomly assigned to be training dataset for establishing deep learning-based models, and 504 patients were used to validate the effectiveness of these models. The conventional model predicted postoperative infections was 0.728 (95% CI 0.688-0.768) with the sensitivity of 66.2% (95% CI 58.2-73.6) and specificity of 66.8% (95% CI 64.6-68.9). The deep learning model including risk factors relevant to baseline clinical characteristics predicted postoperative infections was 0.641 (95% CI 0.545-0.737), and sensitivity and specificity were 34.2% (95% CI 19.6-51.4) and 88.8% (95% CI 85.6-91.6), respectively. Including risk factors relevant to baseline variables and surgery, the deep learning model predicted postoperative infections was 0.763 (95% CI 0.681-0.844) with the sensitivity of 63.2% (95% CI 46-78.2) and specificity of 80.5% (95% CI 76.6-84). Our feasibility study indicated that a deep learning model including risk factors for the prediction of postoperative infections can be achieved in elderly. Further study is needed to assess whether this model can be used to guide clinical practice to improve surgical outcomes in elderly.
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Shi M, Huang Z, Xiao G, Xu B, Ren Q, Zhao H. Estimating the Depth of Anesthesia from EEG Signals Based on a Deep Residual Shrinkage Network. SENSORS (BASEL, SWITZERLAND) 2023; 23:1008. [PMID: 36679805 PMCID: PMC9865536 DOI: 10.3390/s23021008] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 12/05/2022] [Revised: 01/11/2023] [Accepted: 01/12/2023] [Indexed: 06/17/2023]
Abstract
The reliable monitoring of the depth of anesthesia (DoA) is essential to control the anesthesia procedure. Electroencephalography (EEG) has been widely used to estimate DoA since EEG could reflect the effect of anesthetic drugs on the central nervous system (CNS). In this study, we propose that a deep learning model consisting mainly of a deep residual shrinkage network (DRSN) and a 1 × 1 convolution network could estimate DoA in terms of patient state index (PSI) values. First, we preprocessed the four raw channels of EEG signals to remove electrical noise and other physiological signals. The proposed model then takes the preprocessed EEG signals as inputs to predict PSI values. Then we extracted 14 features from the preprocessed EEG signals and implemented three conventional feature-based models as comparisons. A dataset of 18 patients was used to evaluate the models' performances. The results of the five-fold cross-validation show that there is a relatively high similarity between the ground-truth PSI values and the predicted PSI values of our proposed model, which outperforms the conventional models, and further, that the Spearman's rank correlation coefficient is 0.9344. In addition, an ablation experiment was conducted to demonstrate the effectiveness of the soft-thresholding module for EEG-signal processing, and a cross-subject validation was implemented to illustrate the robustness of the proposed method. In summary, the procedure is not merely feasible for estimating DoA by mimicking PSI values but also inspired us to develop a precise DoA-estimation system with more convincing assessments of anesthetization levels.
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Affiliation(s)
- Meng Shi
- School of Electronics, Peking University, Beijing 100084, China
| | - Ziyu Huang
- Department of Anesthesiology, Peking University People’s Hospital, Beijing 100044, China
| | - Guowen Xiao
- School of Electronics, Peking University, Beijing 100084, China
| | - Bowen Xu
- School of Electronics, Peking University, Beijing 100084, China
| | - Quansheng Ren
- School of Electronics, Peking University, Beijing 100084, China
| | - Hong Zhao
- Department of Anesthesiology, Peking University People’s Hospital, Beijing 100044, China
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Lee SM, Lee G, Kim TK, Le T, Hao J, Jung YM, Park CW, Park JS, Jun JK, Lee HC, Kim D. Development and Validation of a Prediction Model for Need for Massive Transfusion During Surgery Using Intraoperative Hemodynamic Monitoring Data. JAMA Netw Open 2022; 5:e2246637. [PMID: 36515949 PMCID: PMC9856486 DOI: 10.1001/jamanetworkopen.2022.46637] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/17/2022] [Accepted: 09/30/2022] [Indexed: 12/15/2022] Open
Abstract
Importance Massive transfusion is essential to prevent complications during uncontrolled intraoperative hemorrhage. As massive transfusion requires time for blood product preparation and additional medical personnel for a team-based approach, early prediction of massive transfusion is crucial for appropriate management. Objective To evaluate a real-time prediction model for massive transfusion during surgery based on the incorporation of preoperative data and intraoperative hemodynamic monitoring data. Design, Setting, and Participants This prognostic study used data sets from patients who underwent surgery with invasive blood pressure monitoring at Seoul National University Hospital (SNUH) from 2016 to 2019 and Boramae Medical Center (BMC) from 2020 to 2021. SNUH represented the development and internal validation data sets (n = 17 986 patients), and BMC represented the external validation data sets (n = 494 patients). Data were analyzed from November 2020 to December 2021. Exposures A deep learning-based real-time prediction model for massive transfusion. Main Outcomes and Measures Massive transfusion was defined as a transfusion of 3 or more units of red blood cells over an hour. A preoperative prediction model for massive transfusion was developed using preoperative variables. Subsequently, a real-time prediction model using preoperative and intraoperative parameters was constructed to predict massive transfusion 10 minutes in advance. A prediction model, the massive transfusion index, calculated the risk of massive transfusion in real time. Results Among 17 986 patients at SNUH (mean [SD] age, 58.65 [14.81] years; 9036 [50.2%] female), 416 patients (2.3%) underwent massive transfusion during the operation (mean [SD] duration of operation, 170.99 [105.03] minutes). The real-time prediction model constructed with the use of preoperative and intraoperative parameters significantly outperformed the preoperative prediction model (area under the receiver characteristic curve [AUROC], 0.972; 95% CI, 0.968-0.976 vs AUROC, 0.824; 95% CI, 0.813-0.834 in the SNUH internal validation data set; P < .001). Patients with the highest massive transfusion index (ie, >90th percentile) had a 47.5-fold increased risk for a massive transfusion compared with those with a lower massive transfusion index (ie, <80th percentile). The real-time prediction model also showed excellent performance in the external validation data set (AUROC of 0.943 [95% CI, 0.919-0.961] in BMC). Conclusions and Relevance The findings of this prognostic study suggest that the real-time prediction model for massive transfusion showed high accuracy of prediction performance, enabling early intervention for high-risk patients. It suggests strong confidence in artificial intelligence-assisted clinical decision support systems in the operating field.
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Affiliation(s)
- Seung Mi Lee
- Department of Obstetrics and Gynecology, Seoul National University College of Medicine, Seoul, Korea
- Department of Obstetrics and Gynecology, Seoul National University Hospital, Seoul, Korea
- Department of Biostatistics, Epidemiology and Informatics, Perelman School of Medicine, University of Pennsylvania, Philadelphia
| | - Garam Lee
- Department of Biostatistics, Epidemiology and Informatics, Perelman School of Medicine, University of Pennsylvania, Philadelphia
| | - Tae Kyong Kim
- Department of Anesthesiology and Pain Medicine, Seoul Metropolitan Government Seoul National University Boramae Medical Center, Seoul, Korea
- Department of Anesthesiology and Pain Medicine, Seoul National University College of Medicine, Seoul, Korea
| | - Trang Le
- Department of Biostatistics, Epidemiology and Informatics, Perelman School of Medicine, University of Pennsylvania, Philadelphia
| | - Jie Hao
- Department of Biostatistics, Epidemiology and Informatics, Perelman School of Medicine, University of Pennsylvania, Philadelphia
| | - Young Mi Jung
- Department of Obstetrics and Gynecology, Seoul National University College of Medicine, Seoul, Korea
- Department of Obstetrics and Gynecology, Seoul National University Hospital, Seoul, Korea
| | - Chan-Wook Park
- Department of Obstetrics and Gynecology, Seoul National University College of Medicine, Seoul, Korea
- Department of Obstetrics and Gynecology, Seoul National University Hospital, Seoul, Korea
| | - Joong Shin Park
- Department of Obstetrics and Gynecology, Seoul National University College of Medicine, Seoul, Korea
- Department of Obstetrics and Gynecology, Seoul National University Hospital, Seoul, Korea
| | - Jong Kwan Jun
- Department of Obstetrics and Gynecology, Seoul National University College of Medicine, Seoul, Korea
- Department of Obstetrics and Gynecology, Seoul National University Hospital, Seoul, Korea
| | - Hyung-Chul Lee
- Department of Anesthesiology and Pain Medicine, Seoul National University College of Medicine, Seoul, Korea
| | - Dokyoon Kim
- Department of Biostatistics, Epidemiology and Informatics, Perelman School of Medicine, University of Pennsylvania, Philadelphia
- Institute for Biomedical Informatics, University of Pennsylvania, Philadelphia
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Wang X, Chen Y, Zhao Y, Wang Z, Zhao L, Hou J, Liu F. Effect of Parecoxib Sodium Combined with Dexmedetomidine on Analgesia and Postoperative Pain of Patients Undergoing Hysteromyomectomy. COMPUTATIONAL AND MATHEMATICAL METHODS IN MEDICINE 2022; 2022:5123933. [PMID: 36277001 PMCID: PMC9586765 DOI: 10.1155/2022/5123933] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 07/27/2022] [Revised: 08/16/2022] [Accepted: 09/15/2022] [Indexed: 11/25/2022]
Abstract
Background Propofol combined with remifentanil is the most common anesthesia method in laparoscopic hysteromyomectomy. However, whether the combination of the two is helpful to patients undergoing hysteromyomectomy still requires unclear. Objective To determine the effect of parecoxib sodium combined with dexmedetomidine on analgesia and postoperative pain of patients undergoing hysteromyomectomy. Methods Altogether, 72 patients receiving hysteromyomectomy in our hospital from February 2017 to March 2019 were enrolled. Among them, 35 patients treated with parecoxib sodium were assigned to the control group, while the rest 37 patients treated with parecoxib sodium combined with dexmedetomidine were assigned to the research group. The following items of the two groups were evaluated: visual analog scale (VAS) score, mechanical pain threshold (MPT), Riker sedation-agitation scale (RSAS) score, and expression of serum cortisol and melatonin. Results At 12 and 24 h after operation, the VAS score of the research group was lower than that of the control group (P < 0.05), and at 6, 12, and 24 h after operation, the MPT of the research group was notably higher than that of the control group (P < 0.05). In addition, at 10 min after extubation, the research group got notably lower RSAS score than the control group (P < 0.05). Before extubation and at 20 min after extubation, the research group showed notably higher melatonin expression and notably lower serum cortisol expression than the control group (both P < 0.05). Conclusion Parecoxib sodium combined with dexmedetomidine can effectively control the postoperative pain of patients undergoing hysteromyomectomy, reduce the incidence of agitation, and effectively control serum cortisol and melatonin in them.
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Affiliation(s)
- Xiaowei Wang
- Department of Anesthesia, Handan Central Hospital, China
| | - Yongxue Chen
- Department of Anesthesia, Handan Central Hospital, China
| | - Yonglei Zhao
- Department of Anesthesia, Handan Central Hospital, China
| | - Zhigang Wang
- Department of Anesthesia, Handan Central Hospital, China
| | - Lu Zhao
- Department of Anesthesia, Handan Central Hospital, China
| | - Junde Hou
- Department of Anesthesia, Handan Central Hospital, China
| | - Fei Liu
- Department of Anesthesia, Handan Central Hospital, China
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VitalDB, a high-fidelity multi-parameter vital signs database in surgical patients. Sci Data 2022; 9:279. [PMID: 35676300 PMCID: PMC9178032 DOI: 10.1038/s41597-022-01411-5] [Citation(s) in RCA: 20] [Impact Index Per Article: 10.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/06/2021] [Accepted: 05/18/2022] [Indexed: 12/30/2022] Open
Abstract
In modern anesthesia, multiple medical devices are used simultaneously to comprehensively monitor real-time vital signs to optimize patient care and improve surgical outcomes. However, interpreting the dynamic changes of time-series biosignals and their correlations is a difficult task even for experienced anesthesiologists. Recent advanced machine learning technologies have shown promising results in biosignal analysis, however, research and development in this area is relatively slow due to the lack of biosignal datasets for machine learning. The VitalDB (Vital Signs DataBase) is an open dataset created specifically to facilitate machine learning studies related to monitoring vital signs in surgical patients. This dataset contains high-resolution multi-parameter data from 6,388 cases, including 486,451 waveform and numeric data tracks of 196 intraoperative monitoring parameters, 73 perioperative clinical parameters, and 34 time-series laboratory result parameters. All data is stored in the public cloud after anonymization. The dataset can be freely accessed and analysed using application programming interfaces and Python library. The VitalDB public dataset is expected to be a valuable resource for biosignal research and development. Measurement(s) | vital signs of patients during surgery • perioperative patient information | Technology Type(s) | Vital Signs Measurement • Electronic Medical Record | Factor Type(s) | vital signs data including various numeric and waveform data acquired from multiple patient monitors • perioperative patient information acquired from the electronic medical record system | Sample Characteristic - Organism | Homo sapiens | Sample Characteristic - Environment | hospital | Sample Characteristic - Location | South Korea |
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Bellini V, Valente M, Turetti M, Del Rio P, Saturno F, Maffezzoni M, Bignami E. Current Applications of Artificial Intelligence in Bariatric Surgery. Obes Surg 2022; 32:2717-2733. [PMID: 35616768 PMCID: PMC9273529 DOI: 10.1007/s11695-022-06100-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/21/2021] [Revised: 05/02/2022] [Accepted: 05/04/2022] [Indexed: 11/27/2022]
Abstract
The application of artificial intelligence technologies is growing in several fields of healthcare settings. The aim of this article is to review the current applications of artificial intelligence in bariatric surgery. We performed a review of the literature on Scopus, PubMed and Cochrane databases, screening all relevant studies published until September 2021, and finally including 36 articles. The use of machine learning algorithms in bariatric surgery is explored in all steps of the clinical pathway, from presurgical risk-assessment and intraoperative management to complications and outcomes prediction. The models showed remarkable results helping physicians in the decision-making process, thus improving the quality of care, and contributing to precision medicine. Several legal and ethical hurdles should be overcome before these methods can be used in common practice.
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Affiliation(s)
- Valentina Bellini
- Anesthesiology, Critical Care and Pain Medicine Division, Department of Medicine and Surgery, University of Parma, Viale Gramsci 14, 43126, Parma, Italy
| | - Marina Valente
- General Surgery Unit, Department of Medicine and Surgery, University of Parma, Viale Gramsci 14, 43126, Parma, Italy
| | - Melania Turetti
- Anesthesiology, Critical Care and Pain Medicine Division, Department of Medicine and Surgery, University of Parma, Viale Gramsci 14, 43126, Parma, Italy
| | - Paolo Del Rio
- General Surgery Unit, Department of Medicine and Surgery, University of Parma, Viale Gramsci 14, 43126, Parma, Italy
| | - Francesco Saturno
- Anesthesiology, Critical Care and Pain Medicine Division, Department of Medicine and Surgery, University of Parma, Viale Gramsci 14, 43126, Parma, Italy
| | - Massimo Maffezzoni
- Anesthesiology, Critical Care and Pain Medicine Division, Department of Medicine and Surgery, University of Parma, Viale Gramsci 14, 43126, Parma, Italy
| | - Elena Bignami
- Anesthesiology, Critical Care and Pain Medicine Division, Department of Medicine and Surgery, University of Parma, Viale Gramsci 14, 43126, Parma, Italy
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22
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Matthews L, Levett DZH, Grocott MPW. Perioperative Risk Stratification and Modification. Anesthesiol Clin 2022; 40:e1-e23. [PMID: 35595387 DOI: 10.1016/j.anclin.2022.03.001] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/18/2022]
Abstract
This article discusses the important topic of perioperative risk stratification and the interventions that can be used in the perioperative period for risk modification. It begins with a brief overview of the commonly used scoring systems, risk-prediction models, and assessments of functional capacity and discusses some of the evidence behind each. It then moves on to examine how perioperative risk can be modified through the use of shared decision making, management of multimorbidity, and prehabilitation programs, before considering what the future of risk stratification and modification may hold.
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Affiliation(s)
- Lewis Matthews
- Perioperative and Critical Care Theme, NIHR Southampton Biomedical Research Centre, University Hospital Southampton/University of Southampton, Tremona Road, Southampton SO16 6YD, United Kingdom; Integrative Physiology and Critical Illness Group, Clinical and Experimental Sciences, Faculty of Medicine, University of Southampton, Southampton, United Kingdom; Shackleton Department of Anaesthesia, University Hospital Southampton NHS Foundation Trust, Tremona Road, Southampton SO16 6YD, United Kingdom.
| | - Denny Z H Levett
- Perioperative and Critical Care Theme, NIHR Southampton Biomedical Research Centre, University Hospital Southampton/University of Southampton, Tremona Road, Southampton SO16 6YD, United Kingdom; Integrative Physiology and Critical Illness Group, Clinical and Experimental Sciences, Faculty of Medicine, University of Southampton, Southampton, United Kingdom
| | - Michael P W Grocott
- Perioperative and Critical Care Theme, NIHR Southampton Biomedical Research Centre, University Hospital Southampton/University of Southampton, Tremona Road, Southampton SO16 6YD, United Kingdom; Integrative Physiology and Critical Illness Group, Clinical and Experimental Sciences, Faculty of Medicine, University of Southampton, Southampton, United Kingdom
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Bellini V, Valente M, Gaddi AV, Pelosi P, Bignami E. Artificial intelligence and telemedicine in anesthesia: potential and problems. Minerva Anestesiol 2022; 88:729-734. [PMID: 35164492 DOI: 10.23736/s0375-9393.21.16241-8] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/08/2022]
Abstract
INTRODUCTION The application of novel technologies like Artificial Intelligence (AI), Machine Learning (ML) and telemedicine in anesthesiology could play a role in transforming the future of health care. In the present review we discuss the current applications of AI and telemedicine in anesthesiology and perioperative care, exploring their potential influence and the possible hurdles. EVIDENCE ACQUISITION AI technologies have the potential to deeply impact all phases of perioperative care from accurate risk prediction to operating room organization, leading to increased cost-effective care quality and better outcomes. Telemedicine is reported as a successful mean within the anaesthetic pathway, including preoperative evaluation, remote patient monitoring, and postoperative care. EVIDENCE SYNTHESIS The utilization of AI and telemedicine is promising encouraging results in perioperative management, nevertheless several hurdles remain to be overcome before these tools could be integrated in our daily practice. CONCLUSIONS AI models and telemedicine can significantly influence all phases of perioperative care, helping physicians in the development of precision medicine.
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Affiliation(s)
- Valentina Bellini
- Anesthesiology, Critical Care and Pain Medicine Division, Department of Medicine and Surgery, University of Parma, Parma, Italy
| | - Marina Valente
- General Surgery Unit, Department of Medicine and Surgery, University of Parma, Parma, Italy
| | - Antonio V Gaddi
- Center for Metabolic diseases and Atherosclerosis, University of Bologna, Bologna, Italy
| | - Paolo Pelosi
- Department of Anesthesia and Intensive Care, Ospedale Policlinico San Martino, IRCCS for Oncology and Neuroscience, Genoa, Italy.,Department of Surgical Sciences and Integrated Diagnostics (DISC), University of Genoa, Genoa, Italy
| | - Elena Bignami
- Anesthesiology, Critical Care and Pain Medicine Division, Department of Medicine and Surgery, University of Parma, Parma, Italy -
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Propofol Anesthesia Depth Monitoring Based on Self-Attention and Residual Structure Convolutional Neural Network. COMPUTATIONAL AND MATHEMATICAL METHODS IN MEDICINE 2022; 2022:8501948. [PMID: 35132332 PMCID: PMC8817884 DOI: 10.1155/2022/8501948] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 10/26/2021] [Revised: 11/24/2021] [Accepted: 01/04/2022] [Indexed: 11/17/2022]
Abstract
Methods We compare nine index values, select CNN+EEG, which has good correlation with BIS index, as an anesthesia state observation index to identify the parameters of the model, and establish a model based on self-attention and dual resistructure convolutional neural network. The data of 93 groups of patients were selected and randomly grouped into three parts: training set, validation set, and test set, and compared the best and worst results predicted by BIS. Result The best result is that the model's accuracy of predicting BLS on the test set has an overall upward trend, eventually reaching more than 90%. The overall error shows a gradual decrease and eventually approaches zero. The worst result is that the model's accuracy of predicting BIS on the test set has an overall upward trend. The accuracy rate is relatively stable without major fluctuations, but the final accuracy rate is above 70%. Conclusion The prediction of BIS indicators by the deep learning method CNN algorithm shows good results in statistics.
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25
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Wu Z, Gong J, He X, Wu Z, Shen J, Shang J. Body mass index and pharmacodynamics of target-controlled infusion of propofol: A prospective non-randomized controlled study. J Clin Pharm Ther 2022; 47:662-667. [PMID: 35018648 DOI: 10.1111/jcpt.13594] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/06/2021] [Accepted: 12/14/2021] [Indexed: 11/26/2022]
Abstract
WHAT IS KNOWN AND OBJECTIVE In our preliminary study, there were large individual variations at sedation levels during propofol target-controlled infusion (TCI). The present study aimed to assess the effects of body mass index (BMI) on the pharmacodynamic index of propofol TCI. METHODS This prospective, non-randomized controlled trial evaluated 175 female patients undergoing breast lumpectomy. Anesthesia was induced with propofol using the TCI system embedded Schnider model. The effect compartment concentration was set to 3 μg/ml, and the start time of infusion was recorded. When the target concentration reached 3 μg/ml, the patient could not be awakened (Ramsay sedation score ≥4), and when the Bispectral Index (BIS) was <60, the infusion was discontinued, and the time point was recorded. The observation end-point was set at the Observer's Assessment of Alertness/Sedation (OAA/S) score of <4. The correlation between the BMI and the pharmacodynamic index of propofol was evaluated. RESULTS AND DISCUSSION Propofol induction time was significantly correlated with the BMI (p < 0.001). The induction time of the underweight subjects was 10.14 ± 2.19 min, which was remarkably higher than that of normal weight (6.48 ± 3.44 min) and overweight (4.75 ± 2.53 min) individuals (p < 0.001). There were still significant differences after multivariable-adjusted regressions (p < 0.001). There were no significant differences in recovery time and sedative effect indicators, such as Ramsay score, BIS value, and effect compartment concentration, between the three groups (p > 0.05 for all). WHAT IS NEW AND CONCLUSION These results suggest that the BMI is one of the critical factors affecting the pharmacodynamic index of propofol TCI, and the induction time decreased progressively with increasing BMI. The Schnider model might underpredict doses of propofol for underweight individuals.
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Affiliation(s)
- Zijuan Wu
- Department of Pharmacy, The Affiliated Changzhou No. 2 People's Hospital of Nanjing Medical University, Changzhou, China
| | - Jinhong Gong
- Department of Pharmacy, The Affiliated Changzhou No. 2 People's Hospital of Nanjing Medical University, Changzhou, China
| | - Xiaomei He
- Department of Pharmacy, The Affiliated Changzhou No. 2 People's Hospital of Nanjing Medical University, Changzhou, China
| | - Zhouquan Wu
- Department of Anesthesiology, The Affiliated Changzhou No. 2 People's Hospital of Nanjing Medical University, Changzhou, China
| | - Jingjing Shen
- Department of Anesthesiology, The Affiliated Changzhou No. 2 People's Hospital of Nanjing Medical University, Changzhou, China
| | - Jingjing Shang
- Department of Pharmacy, The Affiliated Changzhou No. 2 People's Hospital of Nanjing Medical University, Changzhou, China
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Luca AR, Ursuleanu TF, Gheorghe L, Grigorovici R, Iancu S, Hlusneac M, Grigorovici A. Impact of quality, type and volume of data used by deep learning models in the analysis of medical images. INFORMATICS IN MEDICINE UNLOCKED 2022. [DOI: 10.1016/j.imu.2022.100911] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/18/2022] Open
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Predicting anesthetic infusion events using machine learning. Sci Rep 2021; 11:23648. [PMID: 34880365 PMCID: PMC8655034 DOI: 10.1038/s41598-021-03112-2] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/05/2021] [Accepted: 11/19/2021] [Indexed: 02/08/2023] Open
Abstract
Recently, research has been conducted to automatically control anesthesia using machine learning, with the aim of alleviating the shortage of anesthesiologists. In this study, we address the problem of predicting decisions made by anesthesiologists during surgery using machine learning; specifically, we formulate a decision making problem by increasing the flow rate at each time point in the continuous administration of analgesic remifentanil as a supervised binary classification problem. The experiments were conducted to evaluate the prediction performance using six machine learning models: logistic regression, support vector machine, random forest, LightGBM, artificial neural network, and long short-term memory (LSTM), using 210 case data collected during actual surgeries. The results demonstrated that when predicting the future increase in flow rate of remifentanil after 1 min, the model using LSTM was able to predict with scores of 0.659 for sensitivity, 0.732 for specificity, and 0.753 for ROC-AUC; this demonstrates the potential to predict the decisions made by anesthesiologists using machine learning. Furthermore, we examined the importance and contribution of the features of each model using Shapley additive explanations-a method for interpreting predictions made by machine learning models. The trends indicated by the results were partially consistent with known clinical findings.
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Afshar S, Boostani R, Sanei S. A Combinatorial Deep Learning Structure for Precise Depth of Anesthesia Estimation From EEG Signals. IEEE J Biomed Health Inform 2021; 25:3408-3415. [PMID: 33760743 DOI: 10.1109/jbhi.2021.3068481] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/08/2022]
Abstract
Electroencephalography (EEG) is commonly used to measure the depth of anesthesia (DOA) because EEG reflects surgical pain and state of the brain. However, precise and real-time estimation of DOA index for painful surgical operations is challenging due to problems such as postoperative complications and accidental awareness. To tackle these problems, we propose a new combinatorial deep learning structure involving convolutional neural networks (inspired by the inception module), bidirectional long short-term memory, and an attention layer. The proposed model uses the EEG signal to continuously predicts the bispectral index (BIS). It is trained over a large dataset, mostly from those under general anesthesia with few cases receiving sedation/analgesia and spinal anesthesia. Despite the imbalance distribution of BIS values in different levels of anesthesia, our proposed structure achieves convincing root mean square error of 5.59 ± 1.04 and mean absolute error of 4.3 ± 0.87, as well as improvement in area under the curve of 15% on average, which surpasses state-of-the-art DOA estimation methods. The DOA values are also discretized into four levels of anesthesia and the results demonstrate strong inter-subject classification accuracy of 88.7% that outperforms the conventional methods.
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Shim JG, Ryu KH, Lee SH, Cho EA, Lee S, Ahn JH. Machine learning model for predicting the optimal depth of tracheal tube insertion in pediatric patients: A retrospective cohort study. PLoS One 2021; 16:e0257069. [PMID: 34473775 PMCID: PMC8412312 DOI: 10.1371/journal.pone.0257069] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/19/2021] [Accepted: 07/17/2021] [Indexed: 12/01/2022] Open
Abstract
Objective To construct a prediction model for optimal tracheal tube depth in pediatric patients using machine learning. Methods Pediatric patients aged <7 years who received post-operative ventilation after undergoing surgery between January 2015 and December 2018 were investigated in this retrospective study. The optimal location of the tracheal tube was defined as the median of the distance between the upper margin of the first thoracic(T1) vertebral body and the lower margin of the third thoracic(T3) vertebral body. We applied four machine learning models: random forest, elastic net, support vector machine, and artificial neural network and compared their prediction accuracy to three formula-based methods, which were based on age, height, and tracheal tube internal diameter(ID). Results For each method, the percentage with optimal tracheal tube depth predictions in the test set was calculated as follows: 79.0 (95% confidence interval [CI], 73.5 to 83.6) for random forest, 77.4 (95% CI, 71.8 to 82.2; P = 0.719) for elastic net, 77.0 (95% CI, 71.4 to 81.8; P = 0.486) for support vector machine, 76.6 (95% CI, 71.0 to 81.5; P = 1.0) for artificial neural network, 66.9 (95% CI, 60.9 to 72.5; P < 0.001) for the age-based formula, 58.5 (95% CI, 52.3 to 64.4; P< 0.001) for the tube ID-based formula, and 44.4 (95% CI, 38.3 to 50.6; P < 0.001) for the height-based formula. Conclusions In this study, the machine learning models predicted the optimal tracheal tube tip location for pediatric patients more accurately than the formula-based methods. Machine learning models using biometric variables may help clinicians make decisions regarding optimal tracheal tube depth in pediatric patients.
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Affiliation(s)
- Jae-Geum Shim
- Department of Anesthesiology and Pain Medicine, Kangbuk Samsung Hospital, Sungkyunkwan University School of Medicine, Seoul, Korea
| | - Kyoung-Ho Ryu
- Department of Anesthesiology and Pain Medicine, Kangbuk Samsung Hospital, Sungkyunkwan University School of Medicine, Seoul, Korea
| | - Sung Hyun Lee
- Department of Anesthesiology and Pain Medicine, Kangbuk Samsung Hospital, Sungkyunkwan University School of Medicine, Seoul, Korea
| | - Eun-Ah Cho
- Department of Anesthesiology and Pain Medicine, Kangbuk Samsung Hospital, Sungkyunkwan University School of Medicine, Seoul, Korea
| | - Sungho Lee
- Department of Anesthesiology and Pain Medicine, Kangbuk Samsung Hospital, Sungkyunkwan University School of Medicine, Seoul, Korea
| | - Jin Hee Ahn
- Department of Anesthesiology and Pain Medicine, Kangbuk Samsung Hospital, Sungkyunkwan University School of Medicine, Seoul, Korea
- * E-mail:
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Ursuleanu TF, Luca AR, Gheorghe L, Grigorovici R, Iancu S, Hlusneac M, Preda C, Grigorovici A. Deep Learning Application for Analyzing of Constituents and Their Correlations in the Interpretations of Medical Images. Diagnostics (Basel) 2021; 11:1373. [PMID: 34441307 PMCID: PMC8393354 DOI: 10.3390/diagnostics11081373] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/07/2021] [Revised: 07/25/2021] [Accepted: 07/27/2021] [Indexed: 12/13/2022] Open
Abstract
The need for time and attention, given by the doctor to the patient, due to the increased volume of medical data to be interpreted and filtered for diagnostic and therapeutic purposes has encouraged the development of the option to support, constructively and effectively, deep learning models. Deep learning (DL) has experienced an exponential development in recent years, with a major impact on interpretations of the medical image. This has influenced the development, diversification and increase of the quality of scientific data, the development of knowledge construction methods and the improvement of DL models used in medical applications. All research papers focus on description, highlighting, classification of one of the constituent elements of deep learning models (DL), used in the interpretation of medical images and do not provide a unified picture of the importance and impact of each constituent in the performance of DL models. The novelty in our paper consists primarily in the unitary approach, of the constituent elements of DL models, namely, data, tools used by DL architectures or specifically constructed DL architecture combinations and highlighting their "key" features, for completion of tasks in current applications in the interpretation of medical images. The use of "key" characteristics specific to each constituent of DL models and the correct determination of their correlations, may be the subject of future research, with the aim of increasing the performance of DL models in the interpretation of medical images.
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Affiliation(s)
- Tudor Florin Ursuleanu
- Faculty of General Medicine, “Grigore T. Popa” University of Medicine and Pharmacy, 700115 Iasi, Romania; (T.F.U.); (R.G.); (S.I.); (M.H.); (C.P.); (A.G.)
- Department of Surgery VI, “Sf. Spiridon” Hospital, 700111 Iasi, Romania
- Department of Surgery I, Regional Institute of Oncology, 700483 Iasi, Romania
| | - Andreea Roxana Luca
- Faculty of General Medicine, “Grigore T. Popa” University of Medicine and Pharmacy, 700115 Iasi, Romania; (T.F.U.); (R.G.); (S.I.); (M.H.); (C.P.); (A.G.)
- Department Obstetrics and Gynecology, Integrated Ambulatory of Hospital “Sf. Spiridon”, 700106 Iasi, Romania
| | - Liliana Gheorghe
- Faculty of General Medicine, “Grigore T. Popa” University of Medicine and Pharmacy, 700115 Iasi, Romania; (T.F.U.); (R.G.); (S.I.); (M.H.); (C.P.); (A.G.)
- Department of Radiology, “Sf. Spiridon” Hospital, 700111 Iasi, Romania
| | - Roxana Grigorovici
- Faculty of General Medicine, “Grigore T. Popa” University of Medicine and Pharmacy, 700115 Iasi, Romania; (T.F.U.); (R.G.); (S.I.); (M.H.); (C.P.); (A.G.)
| | - Stefan Iancu
- Faculty of General Medicine, “Grigore T. Popa” University of Medicine and Pharmacy, 700115 Iasi, Romania; (T.F.U.); (R.G.); (S.I.); (M.H.); (C.P.); (A.G.)
| | - Maria Hlusneac
- Faculty of General Medicine, “Grigore T. Popa” University of Medicine and Pharmacy, 700115 Iasi, Romania; (T.F.U.); (R.G.); (S.I.); (M.H.); (C.P.); (A.G.)
| | - Cristina Preda
- Faculty of General Medicine, “Grigore T. Popa” University of Medicine and Pharmacy, 700115 Iasi, Romania; (T.F.U.); (R.G.); (S.I.); (M.H.); (C.P.); (A.G.)
- Department of Endocrinology, “Sf. Spiridon” Hospital, 700111 Iasi, Romania
| | - Alexandru Grigorovici
- Faculty of General Medicine, “Grigore T. Popa” University of Medicine and Pharmacy, 700115 Iasi, Romania; (T.F.U.); (R.G.); (S.I.); (M.H.); (C.P.); (A.G.)
- Department of Surgery VI, “Sf. Spiridon” Hospital, 700111 Iasi, Romania
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Pai VV, Pai RB. Artificial intelligence in dermatology and healthcare: An overview. Indian J Dermatol Venereol Leprol 2021; 87:457-467. [PMID: 34114421 DOI: 10.25259/ijdvl_518_19] [Citation(s) in RCA: 11] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/01/2019] [Accepted: 12/01/2020] [Indexed: 12/15/2022]
Abstract
Many aspects of our life are affected by technology. One of the most discussed advancements of modern technologies is artificial intelligence. It involves computational methods which in some way mimic the human thought process. Just like other branches, the medical field also has come under the ambit of artificial intelligence. Almost every field in medicine has been touched by its effect in one way or the other. Prominent among them are medical diagnosis, medical statistics, robotics, and human biology. Medical imaging is one of the foremost specialties with artificial intelligence applications, wherein deep learning methods like artificial neural networks are commonly used. artificial intelligence application in dermatology was initially restricted to the analysis of melanoma and pigmentary skin lesions, has now expanded and covers many dermatoses. Though the applications of artificial intelligence are ever increasing, large data requirements, interpretation of data and ethical concerns are some of its limitations in the present day.
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Affiliation(s)
| | - Rohini Bhat Pai
- Department of Anaesthesiology, Goa Medical College, Bambolim, Goa, India
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Pasero D, Terragni P. Artificial intelligence and perioperative medicine. Minerva Anestesiol 2021; 87:755-756. [PMID: 33853274 DOI: 10.23736/s0375-9393.21.15612-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/08/2022]
Affiliation(s)
- Daniela Pasero
- Department of Anesthesiology and Intensive Care Medicine, AOU Sassari, Sassari, Italy -
| | - Pierpaolo Terragni
- Department of Anesthesiology and Intensive Care Medicine, AOU Sassari, Sassari, Italy
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Maassen O, Fritsch S, Palm J, Deffge S, Kunze J, Marx G, Riedel M, Schuppert A, Bickenbach J. Future Medical Artificial Intelligence Application Requirements and Expectations of Physicians in German University Hospitals: Web-Based Survey. J Med Internet Res 2021; 23:e26646. [PMID: 33666563 PMCID: PMC7980122 DOI: 10.2196/26646] [Citation(s) in RCA: 27] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/19/2020] [Revised: 01/29/2021] [Accepted: 02/15/2021] [Indexed: 12/13/2022] Open
Abstract
BACKGROUND The increasing development of artificial intelligence (AI) systems in medicine driven by researchers and entrepreneurs goes along with enormous expectations for medical care advancement. AI might change the clinical practice of physicians from almost all medical disciplines and in most areas of health care. While expectations for AI in medicine are high, practical implementations of AI for clinical practice are still scarce in Germany. Moreover, physicians' requirements and expectations of AI in medicine and their opinion on the usage of anonymized patient data for clinical and biomedical research have not been investigated widely in German university hospitals. OBJECTIVE This study aimed to evaluate physicians' requirements and expectations of AI in medicine and their opinion on the secondary usage of patient data for (bio)medical research (eg, for the development of machine learning algorithms) in university hospitals in Germany. METHODS A web-based survey was conducted addressing physicians of all medical disciplines in 8 German university hospitals. Answers were given using Likert scales and general demographic responses. Physicians were asked to participate locally via email in the respective hospitals. RESULTS The online survey was completed by 303 physicians (female: 121/303, 39.9%; male: 173/303, 57.1%; no response: 9/303, 3.0%) from a wide range of medical disciplines and work experience levels. Most respondents either had a positive (130/303, 42.9%) or a very positive attitude (82/303, 27.1%) towards AI in medicine. There was a significant association between the personal rating of AI in medicine and the self-reported technical affinity level (H4=48.3, P<.001). A vast majority of physicians expected the future of medicine to be a mix of human and artificial intelligence (273/303, 90.1%) but also requested a scientific evaluation before the routine implementation of AI-based systems (276/303, 91.1%). Physicians were most optimistic that AI applications would identify drug interactions (280/303, 92.4%) to improve patient care substantially but were quite reserved regarding AI-supported diagnosis of psychiatric diseases (62/303, 20.5%). Of the respondents, 82.5% (250/303) agreed that there should be open access to anonymized patient databases for medical and biomedical research. CONCLUSIONS Physicians in stationary patient care in German university hospitals show a generally positive attitude towards using most AI applications in medicine. Along with this optimism comes several expectations and hopes that AI will assist physicians in clinical decision making. Especially in fields of medicine where huge amounts of data are processed (eg, imaging procedures in radiology and pathology) or data are collected continuously (eg, cardiology and intensive care medicine), physicians' expectations of AI to substantially improve future patient care are high. In the study, the greatest potential was seen in the application of AI for the identification of drug interactions, assumedly due to the rising complexity of drug administration to polymorbid, polypharmacy patients. However, for the practical usage of AI in health care, regulatory and organizational challenges still have to be mastered.
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Affiliation(s)
- Oliver Maassen
- Department of Intensive Care Medicine, University Hospital RWTH Aachen, Aachen, Germany
- SMITH Consortium of the German Medical Informatics Initiative, Leipzig, Germany
| | - Sebastian Fritsch
- Department of Intensive Care Medicine, University Hospital RWTH Aachen, Aachen, Germany
- SMITH Consortium of the German Medical Informatics Initiative, Leipzig, Germany
- Jülich Supercomputing Centre, Forschungszentrum Jülich, Jülich, Germany
| | - Julia Palm
- SMITH Consortium of the German Medical Informatics Initiative, Leipzig, Germany
- Institute of Medical Statistics, Computer and Data Sciences, Jena University Hospital, Jena, Germany
| | - Saskia Deffge
- Department of Intensive Care Medicine, University Hospital RWTH Aachen, Aachen, Germany
- SMITH Consortium of the German Medical Informatics Initiative, Leipzig, Germany
| | - Julian Kunze
- Department of Intensive Care Medicine, University Hospital RWTH Aachen, Aachen, Germany
- SMITH Consortium of the German Medical Informatics Initiative, Leipzig, Germany
| | - Gernot Marx
- Department of Intensive Care Medicine, University Hospital RWTH Aachen, Aachen, Germany
- SMITH Consortium of the German Medical Informatics Initiative, Leipzig, Germany
| | - Morris Riedel
- SMITH Consortium of the German Medical Informatics Initiative, Leipzig, Germany
- Jülich Supercomputing Centre, Forschungszentrum Jülich, Jülich, Germany
- School of Natural Sciences and Engineering, University of Iceland, Reykjavik, Iceland
| | - Andreas Schuppert
- SMITH Consortium of the German Medical Informatics Initiative, Leipzig, Germany
- Institute for Computational Biomedicine II, University Hospital RWTH Aachen, Aachen, Germany
| | - Johannes Bickenbach
- Department of Intensive Care Medicine, University Hospital RWTH Aachen, Aachen, Germany
- SMITH Consortium of the German Medical Informatics Initiative, Leipzig, Germany
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Hsieh ML, Lu YT, Lin CC, Lee CP. Comparison of the target-controlled infusion and the manual infusion of propofol anesthesia during electroconvulsive therapy: an open-label randomized controlled trial. BMC Psychiatry 2021; 21:71. [PMID: 33541306 PMCID: PMC7863537 DOI: 10.1186/s12888-021-03069-6] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/14/2020] [Accepted: 01/25/2021] [Indexed: 11/25/2022] Open
Abstract
BACKGROUND Target-controlled infusion (TCI) of propofol is a well-established method of procedural sedation and has been used in Japan for anesthesia during electroconvulsive therapy (ECT). However, the usefulness of the TCI of propofol for ECT has yet to be determined. This study aimed to compare the TCI and manual infusion (MI) of propofol anesthesia during ECT. METHODS A total of forty psychiatric inpatients receiving bitemporal ECT were enrolled in the present study and randomized into the TCI group (N = 20) and the MI group (N = 20). Clinical Global Impression (CGI) and Montreal Cognitive Assessment (MoCA) scores were measured before and after ECT. The clinical outcomes, anesthesia-related variables, and ECT-related variables were compared between the two groups. Generalized estimating equations (GEEs) were used to model the comparison throughout the course of ECT. RESULTS A total of 36 subjects completed the present study, with 18 subjects in each group. Both the groups didn't significantly differ in the post-ECT changes in CGI and MoCA scores. However, concerning MoCA scores after 6 treatments of ECT, the MI group had improvement while the TCI group had deterioration. Compared with the MI group, the TCI group had higher doses of propofol, and longer procedural and recovery time. The TCI group seemed to have more robust seizures in the early course of ECT but less robust seizures in the later course of ECT compared with the MI group. CONCLUSIONS The present study does not support the use of TCI of propofol for anesthesia of ECT. TRIAL REGISTRATION (ClinicalTrials.gov): NCT03863925 . Registered March 5, 2019 - Retrospectively registered.
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Affiliation(s)
- Meng-Ling Hsieh
- grid.413801.f0000 0001 0711 0593Department of Anesthesiology, Chang Gung Memorial Hospital, Linkou, Taoyuan, Taiwan ,grid.145695.aSchool of Medicine, Chang Gung University, Taoyuan, Taiwan
| | - Yen-Ting Lu
- grid.413801.f0000 0001 0711 0593Department of Anesthesiology, Chang Gung Memorial Hospital, Linkou, Taoyuan, Taiwan ,grid.145695.aSchool of Medicine, Chang Gung University, Taoyuan, Taiwan
| | - Chih-Chung Lin
- grid.413801.f0000 0001 0711 0593Department of Anesthesiology, Chang Gung Memorial Hospital, Linkou, Taoyuan, Taiwan ,grid.145695.aSchool of Medicine, Chang Gung University, Taoyuan, Taiwan
| | - Chin-Pang Lee
- School of Medicine, Chang Gung University, Taoyuan, Taiwan. .,Department of Psychiatry, Chang Gung Memorial Hospital, Linkou, Taoyuan, Taiwan.
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Wang D, Song Z, Zhang C, Chen P. Bispectral index monitoring of the clinical effects of propofol closed-loop target-controlled infusion: Systematic review and meta-analysis of randomized controlled trials. Medicine (Baltimore) 2021; 100:e23930. [PMID: 33530193 PMCID: PMC7850716 DOI: 10.1097/md.0000000000023930] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/04/2020] [Revised: 10/07/2020] [Accepted: 11/27/2020] [Indexed: 01/05/2023] Open
Abstract
BACKGROUND To investigate whether closed-loop systems under bispectral index anesthesia depth monitoring can reduce the intraoperative propofol dosage. METHODS All randomized controlled trials (RCTs) on reducing propofol dosage under closed-loop systems were collected, and the literature was screened out, the abstracts and full texts were carefully read, and the references were tracked, data extraction and quality evaluation were conducted on the included research, and the RevMan5.3 software was used for meta-analysis. The main results were propofol and the incidence of adverse reactions such as hypertensive hypotension and postoperative cognitive dysfunction. A total of 879 cases were included in 8 articles, including 450 occurrences in the closed-loop system group and 429 cases in the open-loop system group. RESULTS Compared with manual control, closed-loop systems under bispectral index anesthesia depth monitoring reduced the dose of propofol (MD: -0.62, 95% CI: -1.08--0.16, P = .008), with heterogeneity (I2 = 80%). Closed-loop systems significantly reduced the incidence of abnormal blood pressure (MD: -0.02, 95%CI: -0.05-0.01, P = .15, I2 = 74%) and postoperative cognitive dysfunction (MD: -0.08, 95% CI: -0.14 -0.01, P = .02, I2 = 94%). CONCLUSION Bispectral index monitoring of propofol closed-loop target-controlled infusion system can reduce the amount of propofol, reduce the incidence of adverse reactions such as hypertensive or hypotension and postoperative cognitive dysfunction.
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Nogales A, García-Tejedor ÁJ, Monge D, Vara JS, Antón C. A survey of deep learning models in medical therapeutic areas. Artif Intell Med 2021; 112:102020. [PMID: 33581832 DOI: 10.1016/j.artmed.2021.102020] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/18/2020] [Revised: 12/21/2020] [Accepted: 01/10/2021] [Indexed: 12/18/2022]
Abstract
Artificial intelligence is a broad field that comprises a wide range of techniques, where deep learning is presently the one with the most impact. Moreover, the medical field is an area where data both complex and massive and the importance of the decisions made by doctors make it one of the fields in which deep learning techniques can have the greatest impact. A systematic review following the Cochrane recommendations with a multidisciplinary team comprised of physicians, research methodologists and computer scientists has been conducted. This survey aims to identify the main therapeutic areas and the deep learning models used for diagnosis and treatment tasks. The most relevant databases included were MedLine, Embase, Cochrane Central, Astrophysics Data System, Europe PubMed Central, Web of Science and Science Direct. An inclusion and exclusion criteria were defined and applied in the first and second peer review screening. A set of quality criteria was developed to select the papers obtained after the second screening. Finally, 126 studies from the initial 3493 papers were selected and 64 were described. Results show that the number of publications on deep learning in medicine is increasing every year. Also, convolutional neural networks are the most widely used models and the most developed area is oncology where they are used mainly for image analysis.
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Affiliation(s)
- Alberto Nogales
- CEIEC, Research Institute, Universidad Francisco de Vitoria, Ctra. M-515 Pozuelo-Majadahonda km 1800, 28223, Pozuelo de Alarcón, Spain.
| | - Álvaro J García-Tejedor
- CEIEC, Research Institute, Universidad Francisco de Vitoria, Ctra. M-515 Pozuelo-Majadahonda km 1800, 28223, Pozuelo de Alarcón, Spain.
| | - Diana Monge
- Faculty of Medicine, Research Institute, Universidad Francisco de Vitoria, Ctra. M-515 Pozuelo-Majadahonda km 1800, 28223, Pozuelo de Alarcón, Spain.
| | - Juan Serrano Vara
- CEIEC, Research Institute, Universidad Francisco de Vitoria, Ctra. M-515 Pozuelo-Majadahonda km 1800, 28223, Pozuelo de Alarcón, Spain.
| | - Cristina Antón
- Faculty of Medicine, Research Institute, Universidad Francisco de Vitoria, Ctra. M-515 Pozuelo-Majadahonda km 1800, 28223, Pozuelo de Alarcón, Spain.
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Egnell AC, Johansson S, Chen C, Berges A. Clinical Pharmacology Modeling and Simulation in Drug Development. SYSTEMS MEDICINE 2021. [DOI: 10.1016/b978-0-12-801238-3.11546-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/15/2022] Open
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38
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Abstract
This article provides an overview of knowledge gaps that need to be addressed in cardiac anesthesia, including mitigating the inflammatory effects of cardiopulmonary bypass, defining myocardial infarction after cardiac surgery, improving perioperative neurologic outcomes, and the optimal management of patients undergoing valve replacement. In addition, emerging approaches to research conduct are discussed, including the use of new analytical techniques like machine learning, pragmatic trials, and adaptive designs.
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Affiliation(s)
- Jessica Spence
- Departments of Anesthesia and Critical Care and Health Research Methods, Evaluation, and Impact, McMaster University, HSC 2V9 - 1280 Main Street West, Hamilton, ON L8S 4K1, Canada; Population Health Research Institute (PHRI), C3-7B David Braley Cardiac, Vascular and Stroke Research Institute (DBCVSRI), 237 Barton Street East, Hamilton, ON L8L 2X2, Canada
| | - C David Mazer
- Department of Anesthesia, Li Ka Shing Knowledge Institute of St. Michael's Hospital, 30 Bond Street, Toronto, ON M5B 1W8, Canada; Departments of Anesthesia and Physiology, University of Toronto, Toronto, ON, Canada.
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Ingrande J, Gabriel RA, McAuley J, Krasinska K, Chien A, Lemmens HJM. The Performance of an Artificial Neural Network Model in Predicting the Early Distribution Kinetics of Propofol in Morbidly Obese and Lean Subjects. Anesth Analg 2020; 131:1500-1509. [PMID: 33079873 DOI: 10.1213/ane.0000000000004897] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/05/2022]
Abstract
BACKGROUND Induction of anesthesia is a phase characterized by rapid changes in both drug concentration and drug effect. Conventional mammillary compartmental models are limited in their ability to accurately describe the early drug distribution kinetics. Recirculatory models have been used to account for intravascular mixing after drug administration. However, these models themselves may be prone to misspecification. Artificial neural networks offer an advantage in that they are flexible and not limited to a specific structure and, therefore, may be superior in modeling complex nonlinear systems. They have been used successfully in the past to model steady-state or near steady-state kinetics, but never have they been used to model induction-phase kinetics using a high-resolution pharmacokinetic dataset. This study is the first to use an artificial neural network to model early- and late-phase kinetics of a drug. METHODS Twenty morbidly obese and 10 lean subjects were each administered propofol for induction of anesthesia at a rate of 100 mg/kg/h based on lean body weight and total body weight for obese and lean subjects, respectively. High-resolution plasma samples were collected during the induction phase of anesthesia, with the last sample taken at 16 hours after propofol administration for a total of 47 samples per subject. Traditional mammillary compartment models, recirculatory models, and a gated recurrent unit neural network were constructed to model the propofol pharmacokinetics. Model performance was compared. RESULTS A 4-compartment model, a recirculatory model, and a gated recurrent unit neural network were assessed. The final recirculatory model (mean prediction error: 0.348; mean square error: 23.92) and gated recurrent unit neural network that incorporated ensemble learning (mean prediction error: 0.161; mean square error: 20.83) had similar performance. Each of these models overpredicted propofol concentrations during the induction and elimination phases. Both models had superior performance compared to the 4-compartment model (mean prediction error: 0.108; mean square error: 31.61), which suffered from overprediction bias during the first 5 minutes followed by under-prediction bias after 5 minutes. CONCLUSIONS A recirculatory model and gated recurrent unit artificial neural network that incorporated ensemble learning both had similar performance and were both superior to a compartmental model in describing our high-resolution pharmacokinetic data of propofol. The potential of neural networks in pharmacokinetic modeling is encouraging but may be limited by the amount of training data available for these models.
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Affiliation(s)
| | - Rodney A Gabriel
- From the Department of Anesthesiology
- Division of Biomedical Informatics, Department of Medicine, University of California, San Diego School of Medicine, La Jolla, California
| | - Julian McAuley
- Department of Computer Science and Engineering, University of California, San Diego, La Jolla, California
| | | | - Allis Chien
- Stanford University Mass Spectrometry Laboratory, Stanford, California
| | - Hendrikus J M Lemmens
- Department of Anesthesiology, Perioperative and Pain Medicine, Stanford University School of Medicine, Stanford, California
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40
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The present and future role of artificial intelligence and machine learning in anesthesiology. Int Anesthesiol Clin 2020; 58:7-16. [PMID: 32841964 DOI: 10.1097/aia.0000000000000294] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/07/2023]
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41
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Comparative Analysis on Machine Learning and Deep Learning to Predict Post-Induction Hypotension. SENSORS 2020; 20:s20164575. [PMID: 32824073 PMCID: PMC7472016 DOI: 10.3390/s20164575] [Citation(s) in RCA: 12] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 07/07/2020] [Revised: 08/10/2020] [Accepted: 08/11/2020] [Indexed: 12/13/2022]
Abstract
Hypotensive events in the initial stage of anesthesia can cause serious complications in the patients after surgery, which could be fatal. In this study, we intended to predict hypotension after tracheal intubation using machine learning and deep learning techniques after intubation one minute in advance. Meta learning models, such as random forest, extreme gradient boosting (Xgboost), and deep learning models, especially the convolutional neural network (CNN) model and the deep neural network (DNN), were trained to predict hypotension occurring between tracheal intubation and incision, using data from four minutes to one minute before tracheal intubation. Vital records and electronic health records (EHR) for 282 of 319 patients who underwent laparoscopic cholecystectomy from October 2018 to July 2019 were collected. Among the 282 patients, 151 developed post-induction hypotension. Our experiments had two scenarios: using raw vital records and feature engineering on vital records. The experiments on raw data showed that CNN had the best accuracy of 72.63%, followed by random forest (70.32%) and Xgboost (64.6%). The experiments on feature engineering showed that random forest combined with feature selection had the best accuracy of 74.89%, while CNN had a lower accuracy of 68.95% than that of the experiment on raw data. Our study is an extension of previous studies to detect hypotension before intubation with a one-minute advance. To improve accuracy, we built a model using state-of-art algorithms. We found that CNN had a good performance, but that random forest had a better performance when combined with feature selection. In addition, we found that the examination period (data period) is also important.
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Park Y, Han SH, Byun W, Kim JH, Lee HC, Kim SJ. A Real-Time Depth of Anesthesia Monitoring System Based on Deep Neural Network With Large EDO Tolerant EEG Analog Front-End. IEEE TRANSACTIONS ON BIOMEDICAL CIRCUITS AND SYSTEMS 2020; 14:825-837. [PMID: 32746339 DOI: 10.1109/tbcas.2020.2998172] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/11/2023]
Abstract
In this article, we present a real-time electroencephalogram (EEG) based depth of anesthesia (DoA) monitoring system in conjunction with a deep learning framework, AnesNET. An EEG analog front-end (AFE) that can compensate ±380-mV electrode DC offset using a coarse digital DC servo loop is implemented in the proposed system. The EEG-based MAC, EEGMAC, is introduced as a novel index to accurately predict the DoA, which is designed for applying to patients anesthetized by both volatile and intravenous agents. The proposed deep learning protocol consists of four layers of convolutional neural network and two dense layers. In addition, we optimize the complexity of the deep neural network (DNN) to operate on a microcomputer such as the Raspberry Pi 3, realizing a cost-effective small-size DoA monitoring system. Fabricated in 110-nm CMOS, the prototype AFE consumes 4.33 μW per channel and has the input-referred noise of 0.29 μVrms from 0.5 to 100 Hz with the noise efficiency factor of 2.2. The proposed DNN was evaluated with pre-recorded EEG data from 374 subjects administrated by inhalational anesthetics under surgery, achieving an average squared and absolute errors of 0.048 and 0.05, respectively. The EEGMAC with subjects anesthetized by an intravenous agent also showed a good agreement with the bispectral index value, confirming the proposed DoA index is applicable to both anesthetics. The implemented monitoring system with the Raspberry Pi 3 estimates the EEGMAC within 20 ms, which is about thousand-fold faster than the BIS estimation in literature.
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43
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Bowness J, El-Boghdadly K, Burckett-St Laurent D. Artificial intelligence for image interpretation in ultrasound-guided regional anaesthesia. Anaesthesia 2020; 76:602-607. [PMID: 32726498 DOI: 10.1111/anae.15212] [Citation(s) in RCA: 34] [Impact Index Per Article: 8.5] [Reference Citation Analysis] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 07/01/2020] [Indexed: 12/12/2022]
Affiliation(s)
- J Bowness
- Institute of Academic Anaesthesia, University of Dundee, Dundee, UK.,Department of Anaesthesia, Ninewells Hospital, Dundee, UK
| | - K El-Boghdadly
- Department of Anaesthesia and Peri-operative Medicine, Guy's and St Thomas's NHS Foundation Trust, London, UK.,King's College London, London, UK
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Classification of Current Procedural Terminology Codes from Electronic Health Record Data Using Machine Learning. Anesthesiology 2020; 132:738-749. [PMID: 32028374 DOI: 10.1097/aln.0000000000003150] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/30/2022]
Abstract
BACKGROUND Accurate anesthesiology procedure code data are essential to quality improvement, research, and reimbursement tasks within anesthesiology practices. Advanced data science techniques, including machine learning and natural language processing, offer opportunities to develop classification tools for Current Procedural Terminology codes across anesthesia procedures. METHODS Models were created using a Train/Test dataset including 1,164,343 procedures from 16 academic and private hospitals. Five supervised machine learning models were created to classify anesthesiology Current Procedural Terminology codes, with accuracy defined as first choice classification matching the institutional-assigned code existing in the perioperative database. The two best performing models were further refined and tested on a Holdout dataset from a single institution distinct from Train/Test. A tunable confidence parameter was created to identify cases for which models were highly accurate, with the goal of at least 95% accuracy, above the reported 2018 Centers for Medicare and Medicaid Services (Baltimore, Maryland) fee-for-service accuracy. Actual submitted claim data from billing specialists were used as a reference standard. RESULTS Support vector machine and neural network label-embedding attentive models were the best performing models, respectively, demonstrating overall accuracies of 87.9% and 84.2% (single best code), and 96.8% and 94.0% (within top three). Classification accuracy was 96.4% in 47.0% of cases using support vector machine and 94.4% in 62.2% of cases using label-embedding attentive model within the Train/Test dataset. In the Holdout dataset, respective classification accuracies were 93.1% in 58.0% of cases and 95.0% among 62.0%. The most important feature in model training was procedure text. CONCLUSIONS Through application of machine learning and natural language processing techniques, highly accurate real-time models were created for anesthesiology Current Procedural Terminology code classification. The increased processing speed and a priori targeted accuracy of this classification approach may provide performance optimization and cost reduction for quality improvement, research, and reimbursement tasks reliant on anesthesiology procedure codes.
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45
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Char DS, Burgart A. Machine-Learning Implementation in Clinical Anesthesia: Opportunities and Challenges. Anesth Analg 2020; 130:1709-1712. [PMID: 31922996 DOI: 10.1213/ane.0000000000004656] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/30/2022]
Affiliation(s)
- Danton S Char
- From the Division of Pediatric Anesthesia, Department of Anesthesia, and Center for Biomedical Ethics, Stanford University School of Medicine, Stanford, California
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46
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Belur Nagaraj S, Ramaswamy SM, Weerink MAS, Struys MMRF. Predicting Deep Hypnotic State From Sleep Brain Rhythms Using Deep Learning: A Data-Repurposing Approach. Anesth Analg 2020; 130:1211-1221. [PMID: 32287128 PMCID: PMC7147424 DOI: 10.1213/ane.0000000000004651] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/11/2023]
Abstract
BACKGROUND: Brain monitors tracking quantitative brain activities from electroencephalogram (EEG) to predict hypnotic levels have been proposed as a labor-saving alternative to behavioral assessments. Expensive clinical trials are required to validate any newly developed processed EEG monitor for every drug and combinations of drugs due to drug-specific EEG patterns. There is a need for an alternative, efficient, and economical method. METHODS: Using deep learning algorithms, we developed a novel data-repurposing framework to predict hypnotic levels from sleep brain rhythms. We used an online large sleep data set (5723 clinical EEGs) for training the deep learning algorithm and a clinical trial hypnotic data set (30 EEGs) for testing during dexmedetomidine infusion. Model performance was evaluated using accuracy and the area under the receiver operator characteristic curve (AUC). RESULTS: The deep learning model (a combination of a convolutional neural network and long short-term memory units) trained on sleep EEG predicted deep hypnotic level with an accuracy (95% confidence interval [CI]) = 81 (79.2–88.3)%, AUC (95% CI) = 0.89 (0.82–0.94) using dexmedetomidine as a prototype drug. We also demonstrate that EEG patterns during dexmedetomidine-induced deep hypnotic level are homologous to nonrapid eye movement stage 3 EEG sleep. CONCLUSIONS: We propose a novel method to develop hypnotic level monitors using large sleep EEG data, deep learning, and a data-repurposing approach, and for optimizing such a system for monitoring any given individual. We provide a novel data-repurposing framework to predict hypnosis levels using sleep EEG, eliminating the need for new clinical trials to develop hypnosis level monitors.
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Affiliation(s)
| | - Sowmya M Ramaswamy
- Department of Anesthesiology, University Medical Center Groningen, University of Groningen, Groningen, the Netherlands
| | - Maud A S Weerink
- Department of Anesthesiology, University Medical Center Groningen, University of Groningen, Groningen, the Netherlands
| | - Michel M R F Struys
- Department of Anesthesiology, University Medical Center Groningen, University of Groningen, Groningen, the Netherlands.,Department of Basic and Applied Medical Sciences, Ghent University, Ghent, Belgium
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The role of pharmacokinetics and pharmacodynamics in clinical anaesthesia practice. Curr Opin Anaesthesiol 2020; 33:483-489. [DOI: 10.1097/aco.0000000000000881] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/10/2023]
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48
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Thongprayoon C, Hansrivijit P, Bathini T, Vallabhajosyula S, Mekraksakit P, Kaewput W, Cheungpasitporn W. Predicting Acute Kidney Injury after Cardiac Surgery by Machine Learning Approaches. J Clin Med 2020; 9:jcm9061767. [PMID: 32517295 PMCID: PMC7355827 DOI: 10.3390/jcm9061767] [Citation(s) in RCA: 9] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/02/2020] [Accepted: 06/04/2020] [Indexed: 02/08/2023] Open
Abstract
Cardiac surgery-associated AKI (CSA-AKI) is common after cardiac surgery and has an adverse impact on short- and long-term mortality. Early identification of patients at high risk of CSA-AKI by applying risk prediction models allows clinicians to closely monitor these patients and initiate effective preventive and therapeutic approaches to lessen the incidence of AKI. Several risk prediction models and risk assessment scores have been developed for CSA-AKI. However, the definition of AKI and the variables utilized in these risk scores differ, making general utility complex. Recently, the utility of artificial intelligence coupled with machine learning, has generated much interest and many studies in clinical medicine, including CSA-AKI. In this article, we discussed the evolution of models established by machine learning approaches to predict CSA-AKI.
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Affiliation(s)
- Charat Thongprayoon
- Division of Nephrology, Department of Medicine, Mayo Clinic, Rochester, MN 55905, USA;
| | - Panupong Hansrivijit
- Department of Internal Medicine, University of Pittsburgh Medical Center Pinnacle, Harrisburg, PA 17105, USA;
| | - Tarun Bathini
- Department of Internal Medicine, University of Arizona, Tucson, AZ 85724, USA;
| | | | - Poemlarp Mekraksakit
- Department of Internal Medicine, Texas Tech University Health Sciences Center, Lubbock, TX 79424, USA;
| | - Wisit Kaewput
- Department of Military and Community Medicine, Phramongkutklao College of Medicine, Bangkok 10400, Thailand;
| | - Wisit Cheungpasitporn
- Division of Nephrology, Department of Medicine, University of Mississippi Medical Center, Jackson, MS 39216, USA
- Correspondence: ; Tel.: +1-601-984-5670; Fax: +1-601-984-5765
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49
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Abstract
Automated medical technology is becoming an integral part of routine anesthetic practice. Automated technologies can improve patient safety, but may create new workflows with potentially surprising adverse consequences and cognitive errors that must be addressed before these technologies are adopted into clinical practice. Industries such as aviation and nuclear power have developed techniques to mitigate the unintended consequences of automation, including automation bias, skill loss, and system failures. In order to maximize the benefits of automated technology, clinicians should receive training in human–system interaction including topics such as vigilance, management of system failures, and maintaining manual skills. Medical device manufacturers now evaluate usability of equipment using the principles of human performance and should be encouraged to develop comprehensive training materials that describe possible system failures. Additional research in human–system interaction can improve the ways in which automated medical devices communicate with clinicians. These steps will ensure that medical practitioners can effectively use these new devices while being ready to assume manual control when necessary and prepare us for a future that includes automated health care.
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50
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Kang AR, Lee J, Jung W, Lee M, Park SY, Woo J, Kim SH. Development of a prediction model for hypotension after induction of anesthesia using machine learning. PLoS One 2020; 15:e0231172. [PMID: 32298292 PMCID: PMC7162491 DOI: 10.1371/journal.pone.0231172] [Citation(s) in RCA: 26] [Impact Index Per Article: 6.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/26/2019] [Accepted: 03/17/2020] [Indexed: 12/14/2022] Open
Abstract
Arterial hypotension during the early phase of anesthesia can lead to adverse outcomes such as a prolonged postoperative stay or even death. Predicting hypotension during anesthesia induction is complicated by its diverse causes. We investigated the feasibility of developing a machine-learning model to predict postinduction hypotension. Naïve Bayes, logistic regression, random forest, and artificial neural network models were trained to predict postinduction hypotension, occurring between tracheal intubation and incision, using data for the period from between the start of anesthesia induction and immediately before tracheal intubation obtained from an anesthesia monitor, a drug administration infusion pump, an anesthesia machine, and from patients’ demographics, together with preexisting disease information from electronic health records. Among 222 patients, 126 developed postinduction hypotension. The random-forest model showed the best performance, with an area under the receiver operating characteristic curve of 0.842 (95% confidence interval [CI]: 0.736-0.948). This was higher than that for the Naïve Bayes (0.778; 95% CI: 0.65-0.898), logistic regression (0.756; 95% CI: 0.630-0.881), and artificial-neural-network (0.760; 95% CI: 0.640-0.880) models. The most important features affecting the accuracy of machine-learning prediction were a patient’s lowest systolic blood pressure, lowest mean blood pressure, and mean systolic blood pressure before tracheal intubation. We found that machine-learning models using data obtained from various anesthesia machines between the start of anesthesia induction and immediately before tracheal intubation can predict hypotension occurring during the period between tracheal intubation and incision.
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Affiliation(s)
- Ah Reum Kang
- SCH Media Labs, Soonchunhyang University, Asan, Chungnam, South Korea
| | - Jihyun Lee
- SCH Media Labs, Soonchunhyang University, Asan, Chungnam, South Korea
| | - Woohyun Jung
- Department of Anesthesiology and Pain Medicine, Soonchunhyang University Bucheon Hospital, Soonchunhyang University College of Medicine, Bucheon, Gyeonggi, South Korea
| | - Misoon Lee
- Department of Anesthesiology and Pain Medicine, Soonchunhyang University Bucheon Hospital, Soonchunhyang University College of Medicine, Bucheon, Gyeonggi, South Korea
| | - Sun Young Park
- Department of Anesthesiology and Pain Medicine, Soonchunhyang University Seoul Hospital, Soonchunhyang University College of Medicine, Seoul, South Korea
| | - Jiyoung Woo
- SCH Media Labs, Soonchunhyang University, Asan, Chungnam, South Korea
| | - Sang Hyun Kim
- Department of Anesthesiology and Pain Medicine, Soonchunhyang University Bucheon Hospital, Soonchunhyang University College of Medicine, Bucheon, Gyeonggi, South Korea
- * E-mail:
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