Machine learning based method for the evaluation of the Analgesia Nociception Index in the assessment of general anesthesia

González-Cava, José M.; Arnay, Rafael; Leon, Ana M.; Martín, M.C.; Reboso, José Antonio; Calvo-Rolle, José Luís; Méndez‐Pérez, Juan Albino

doi:10.1016/j.compbiomed.2020.103645

Cited by 12 publications

(8 citation statements)

References 29 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Thiam et al 15 reported average performances of 84.57% and 84.40%. Cava et al 16 had accuracy of 86.21% (83.62%– 87.93%), precision: 86.11% (83.78%–88.57%), recall: 91.18% (88.24%–91.18%), specificity: 79.17% (75%– 83.33%), AUC: 0.89 (0.87–0.90) and kappa index: 0.71 (0.66–0.75). Jiang et al 17 however reported accuracy of 96.67%, sensitivity: 100%, specificity: 93.75%, average area under the curve [AUC]: 0.8925 (Table 1).…”

Section: Resultsmentioning

confidence: 98%

Role of Artificial Intelligence and Machine Learning in the prediction of the pain: A scoping systematic review

Sankaran

Kumar

Parasuram

2022

Proc Inst Mech Eng H

View full text Add to dashboard Cite

Artificial Intelligence in healthcare is growing quickly in diagnostics and treatment management. Despite the quantity and variety of studies its role in clinical care is not clear. To identify the evidence gaps and characteristics of the Artificial Intelligence and Machine Learning techniques in predicting/diagnosing the pain? Pubmed/Embase were searched from the inception to October 2021 for articles without any language restrictions specifically addressing the following: the accuracy of AI in pain considering Brain Imaging, Patient-reported measures, and Electrophysiology, the ability of AI to differentiate stratify severity/types of pain, the ability of AI to predict pain and lastly the most accurate AI technique for given inputs. All the included studies were on humans. Eight hundred forty abstracts were reviewed, and 23 articles were finally included. Identified records were independently screened and relevant data was extracted. We performed conceptual synthesis by grouping the studies using available concepts of AL/ML techniques in diagnosing pain. Then we summarized the number of features/physiological measurements. Structured tabulation synthesis was used to show patterns predictions along with a narrative commentary. A total of 23 articles, published between 2015 and 2020 from 12 countries were included. Most studies were experimental in design. The most common design was cross sectional. Chronic or acute pains were predicted more often. Compared to control, the pain prediction was in the range of 57%–96% by AI techniques. Support Vector Machine and deep learning showed higher accuracy for classifying pain. From this study, it can be inferred that AI/ML can be used to differentiate healthy controls from patients. It can also facilitate categorizing them into new and different clinical subgroups. Lastly, it can predict future pain. The limitations are with respect to studies done after the search period. AL/ ML has a supportive role in pain diagnostics.

show abstract

Section: Resultsmentioning

confidence: 98%

Role of Artificial Intelligence and Machine Learning in the prediction of the pain: A scoping systematic review

Sankaran

Kumar

Parasuram

2022

Proc Inst Mech Eng H

View full text Add to dashboard Cite

show abstract

“…Regarding intraoperative pain management, quantifying the nociception level of the patients and adjusting analgesic drug infusion during anesthesia is still challenging. To this end, ML algorithms could be used to build index helping anesthesiologist to manage intraoperative pain as made by Gonzalez-Cava et al ( 30 ) in this paper. They evaluate the Analgesia Nociception Index (ANI) as a guidance variable for opioid infusion rate modulation.…”

Section: Discussionmentioning

confidence: 99%

“…The ANI monitor makes a Heart Rate Variability (HRV) analysis to measure the effect of the Respiratory Sinus Arrhythmia (RSA). ANI value together with the hemodynamic information outperformed non-specific traditional signs such as heart rate and blood pressure in order to quantify the nociception level and may anticipate a dose change to prevent hemodynamic events before they happen ( 30 ).…”

Section: Discussionmentioning

confidence: 99%

Artificial intelligence and anesthesia: a narrative review

Carnà¹,

Russo²,

Vincenzo³

et al. 2022

Ann Transl Med

View full text Add to dashboard Cite

Background and Objective The aim of this narrative review is to analyze whether or not artificial intelligence (AI) and its subsets are implemented in current clinical anesthetic practice, and to describe the current state of the research in the field. AI is a general term which refers to all the techniques that enable computers to mimic human intelligence. AI is based on algorithms that gives machines the ability to reason and perform functions such as problem-solving, object and word recognition, inference of world states, and decision-making. It includes machine learning (ML) and deep learning (DL). Methods We performed a narrative review of the literature on Scopus, PubMed and Cochrane databases. The research string comprised various combinations of “artificial intelligence”, “machine learning”, “anesthesia”, “anesthesiology”. The databases were searched independently by two authors. A third reviewer would mediate any disagreement the results of the two screeners. Key Content and Findings The application of AI has shown excellent results in both anesthesia and in operating room (OR) management. In each phase of the perioperative process, pre-, intra- and postoperative ones, it is able to perform different and specific tasks, using various techniques. Conclusions Thanks to the use of these new technologies, even anesthesia, as it is happening for other disciplines, is going through a real revolution, called Anesthesia 4.0. However, AI is not free from limitations and open issues. Unfortunately, the models created, provided they have excellent performance, have not yet entered daily practice. Clinical impact analyzes and external validations are needed before this happens. Therefore, qualitative research will be needed to better understand the ethical, cultural, and societal implications of integrating AI into clinical workflows.

show abstract

“…If ANI/HFVI studies are accumulated and their validity is confirmed, ANI-based administration may be realized [ 37 ]. A machine learning-based method combining ANI and hemodynamic monitoring has already been investigated with continuous administration of remifentanil [ 65 ]. These trends indicate the promising development of automated anesthesia/general management system by combining artificial intelligence and ANI/HFVI.…”

Section: Outlooks Of Ani/hfvimentioning

confidence: 99%

Analgesia nociception index and high frequency variability index: promising indicators of relative parasympathetic tone

2022

View full text Add to dashboard Cite

At present, there is no objective and absolute measure of nociception, although various monitoring techniques have been developed. One such technique is the Analgesia Nociception Index (ANI), which is calculated from heart rate variability that reflects the relative parasympathetic tone. ANI is expressed on a non-unit scale of 0–100 (100 indicates maximal relative parasympathetic tone). Several studies indicated that ANI-guided anesthesia may help reduce intraoperative opioid use. The usefulness of ANI in the intensive care unit (ICU) and during surgery has also been reported. However, some limitations of ANI have also been reported; for example, ANI is affected by emotions and some drugs. In 2022, a high frequency variability index (HFVI), which was renamed from ANI and uses the same algorithm as ANI, was commercialized; therefore, ANI/HFVI are currently in the spotlight. Unlike ANI, HFVI can be displayed along with other biometric information on the Root ® monitor. ANI/HFVI monitoring may affect the prognosis of not only patients in the perioperative period but those in ICU, those who receive home medical care, or outpatients. In this article, we present an updated review on ANI that has been published in the last decade, introduce HFVI, and discuss the outlooks of ANI/HFVI.

show abstract

Machine learning based method for the evaluation of the Analgesia Nociception Index in the assessment of general anesthesia

Cited by 12 publications

References 29 publications

Role of Artificial Intelligence and Machine Learning in the prediction of the pain: A scoping systematic review

Role of Artificial Intelligence and Machine Learning in the prediction of the pain: A scoping systematic review

Artificial intelligence and anesthesia: a narrative review

Analgesia nociception index and high frequency variability index: promising indicators of relative parasympathetic tone

Contact Info

Product

Resources

About