Adaptive fuzzy modeling of the hypnotic process in anesthesia

Artificial intelligence and its clinical application in Anesthesiology: a systematic review

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.

Network and Systems Medicine: Position Paper of the European Collaboration on Science and Technology Action on Open Multiscale Systems Medicine

Introduction: Network and systems medicine has rapidly evolved over the past decade, thanks to computational and integrative tools, which stem in part from systems biology. However, major challenges and hurdles are still present regarding validation and translation into clinical application and decision making for precision medicine. Methods: In this context, the Collaboration on Science and Technology Action on Open Multiscale Systems Medicine (OpenMultiMed) reviewed the available advanced technologies for multidimensional data generation and integration in an open-science approach as well as key clinical applications of network and systems medicine and the main issues and opportunities for the future. Results: The development of multi-omic approaches as well as new digital tools provides a unique opportunity to explore complex biological systems and networks at different scales. Moreover, the application of findable, applicable, interoperable, and reusable principles and the adoption of standards increases data availability and sharing for multiscale integration and interpretation. These innovations have led to the first clinical applications of network and systems medicine, particularly in the field of personalized therapy and drug dosing. Enlarging network and systems medicine application would now imply to increase patient engagement and health care providers as well as to educate the novel generations of medical doctors and biomedical researchers to shift the current organ- and symptom-based medical concepts toward network- and systems-based ones for more precise diagnoses, interventions, and ideally prevention. Conclusion: In this dynamic setting, the health care system will also have to evolve, if not revolutionize, in terms of organization and management.

Solar Thermal Collector Output Temperature Prediction by Hybrid Intelligent Model for Smartgrid and Smartbuildings Applications and Optimization

Currently, there is great interest in reducing the consumption of fossil fuels (and other non-renewable energy sources) in order to preserve the environment; smart buildings are commonly proposed for this purpose as they are capable of producing their own energy and using it optimally. However, at times, solar energy is not able to supply the energy demand fully; it is mandatory to know the quantity of energy needed to optimize the system. This research focuses on the prediction of output temperature from a solar thermal collector. The aim is to measure solar thermal energy and optimize the energy system of a house (or building). The dataset used in this research has been taken from a real installation in a bio-climate house located on the Sotavento Experimental Wind Farm, in north-west Spain. A hybrid intelligent model has been developed by combining clustering and regression methods such as neural networks, polynomial regression, and support vector machines. The main findings show that, by dividing the dataset into small clusters on the basis of similarity in behavior, it is possible to create more accurate models. Moreover, combining different regression methods for each cluster provides better results than when a global model of the whole dataset is used. In temperature prediction, mean absolute error was lower than 4 ∘ C.

A Fault Detection System for a Geothermal Heat Exchanger Sensor Based on Intelligent Techniques

This paper proposes a methodology for dealing with an issue of crucial practical importance in real engineering systems such as fault detection and recovery of a sensor. The main goal is to define a strategy to identify a malfunctioning sensor and to establish the correct measurement value in those cases. As study case, we use the data collected from a geothermal heat exchanger installed as part of the heat pump installation in a bioclimatic house. The sensor behaviour is modeled by using six different machine learning techniques: Random decision forests, gradient boosting, extremely randomized trees, adaptive boosting, k-nearest neighbors, and shallow neural networks. The achieved results suggest that this methodology is a very satisfactory solution for this kind of systems.

Closed loop administration of propofol based on a Smith predictor: a randomized controlled trial

BACKGROUND

Delay in the propofol pharmacodynamics effect is commonly observed in total intravenous anesthesia (TIVA). To face the delay in the hypnosis control, we have proposed a proportional-integral (PI) controller with a Smith predictor (PI+Smith). We have evaluated the feasibility of this closed-loop control for propofol administration and compared the performance with manual administration guided by the Bispectral Index (BIS).

METHODS

Fifty-seven adult patients under TIVA with propofol and remifentanil were randomly assigned to a PI+Smith or a manual control (MC) group. The BIS target was set to 50. The performance was compared through the global score (GS), median performance error (MDPE), median absolute performance error (MDAPE), offset and Wobble.

RESULTS

A total of 29 patients in the MC and 25 in the PI+Smith groups completed this study. Performance was significantly better in the PI+Smith group: global score was 25 (19 to 37) for PI+Smith versus 44 (32 to 57) for MC (P<0.001); MDPE was -0.9 (-5.6 to 2) for PI+Smith versus -11 (-16 to -4.3) for MC (P<0.001); MDAPE was 10.8 (8.8 to 14.3) for PI+Smith versus 17 (12.8 to 19.2) for MC (P<0.001); offset was -0.6 (-3.2 to 0.06) for PI+Smith versus -3.7 (-7.0 to -0.8) for MC (P=0.01). The percentage time of BIS within the 40-60 range during the maintenance phase was higher in the PI+Smith group 80.8 (68.7 to 87.9) than in the MC group 59.1 (53.4 to 72.5) (P<0.001).

CONCLUSIONS

The use of a specific mechanism in the PI controller to deal with the delay outperformed satisfactorily manual practice. The controller was able to regulate propofol administration, maintaining the BIS value within a desirable range and coping with oscillations.