Fuzzy logic based anaesthesia monitoring systems for the detection of absolute hypovolaemia

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.

Diseases diagnosis using fuzzy logic methods: A systematic and meta-analysis review

BACKGROUND AND OBJECTIVE

Diagnosis as the initial step of medical practice, is one of the most important parts of complicated clinical decision making which is usually accompanied with the degree of ambiguity and uncertainty. Since uncertainty is the inseparable nature of medicine, fuzzy logic methods have been used as one of the best methods to decrease this ambiguity. Recently, several kinds of literature have been published related to fuzzy logic methods in a wide range of medical aspects in terms of diagnosis. However, in this context there are a few review articles that have been published which belong to almost ten years ago. Hence, we conducted a systematic review to determine the contribution of utilizing fuzzy logic methods in disease diagnosis in different medical practices.

METHODS

Eight scientific databases are selected as an appropriate database and Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) method was employed as the basis method for conducting this systematic and meta-analysis review. Regarding the main objective of this research, some inclusion and exclusion criteria were considered to limit our investigation. To achieve a structured meta-analysis, all eligible articles were classified based on authors, publication year, journals or conferences, applied fuzzy methods, main objectives of the research, problems and research gaps, tools utilized to model the fuzzy system, medical disciplines, sample sizes, the inputs and outputs of the system, findings, results and finally the impact of applied fuzzy methods to improve diagnosis. Then, we analyzed the results obtained from these classifications to indicate the effect of fuzzy methods in decreasing the complexity of diagnosis.

RESULTS

Consequently, the result of this study approved the effectiveness of applying different fuzzy methods in diseases diagnosis process, presenting new insights for researchers about what kind of diseases which have been more focused. This will help to determine the diagnostic aspects of medical disciplines that are being neglected.

CONCLUSIONS

Overall, this systematic review provides an appropriate platform for further research by identifying the research needs in the domain of disease diagnosis.

A nonparametric fiducial interval for the Youden index in multi-state diagnostic settings

The Youden index is a commonly employed metric to characterize the performance of a diagnostic test at its optimal point. For tests with three or more outcome classes, the Youden index has been extended; however, there are limited methods to compute a confidence interval (CI) about its value. Often, outcome classes are assumed to be normally distributed, which facilitates computational formulas for the CI bounds; however, many scenarios exist for which these assumptions cannot be made. In addition, many of these existing CI methods do not work well for small sample sizes. We propose a method to compute a nonparametric interval about the Youden index utilizing the fiducial argument. This fiducial interval ensures that CI coverage is met regardless of sample size, underlying distributional assumptions, or use of a complex classifier for diagnosis. Two alternate fiducial intervals are also considered. A simulation was conducted, which demonstrates the coverage and interval length for the proposed methods. Comparisons were made using no distributional assumptions on the outcome classes and for when outcomes were assumed to be normally distributed. In general, coverage probability was consistently met, and interval length was reasonable. The proposed fiducial method was also demonstrated in data examining biomarkers in subjects to predict diagnostic stages ranging from normal kidney function to chronic allograph nephropathy. Published 2015. This article is a U.S. Government work and is in the public domain in the USA.

Multiple physical signs detection and decision support system for hospitalized older adults

Health monitoring systems have rapidly evolved during the past two decades and have the potential to change the way healthcare is currently delivered. Smart monitoring systems automate patient monitoring tasks and thereby improve patient workflow management. Moreover, expert systems have the potential to assist clinicians and improve their performance by accurately executing repetitive tasks, to which humans are ill-suited. Clinicians working in hospital wards are responsible for conducting a multitude of tasks which require constant vigilance, and thus the need for a smart decision support system has arisen. In particular, wireless patient monitoring systems are emerging as a low cost, reliable and accurate means of healthcare delivery. Vital signs monitoring systems are rapidly becoming part of today's healthcare delivery. The paradigm has shifted from traditional and manual recording to computer-based electronic records and, further, to handheld devices as versatile and innovative healthcare monitoring systems. The current study focuses on interpreting multiple physical signs and early warning for hospitalized older adults so that severe consequences can be minimized. Data from a total of 30 patients have been collated in New Zealand hospitals under local and national ethics approvals. The system records blood pressure, heart rate (pulse), oxygen saturation (SpO2), ear temperature and blood glucose levels from hospitalized patients and transfers this information to a web-based software application for remote monitoring and further interpretation. Ultimately, this system is aimed to achieve a high level of agreement with clinicians' interpretation when assessing specific physical signs such as bradycardia, tachycardia, hypertension, hypotension, hypoxaemia, fever and hypothermia to generate early warnings. The performance of the vital signs interpretation system was validated through off-line as well as real-time tests with a high level of agreement between the system and physician.

Evaluation of the utility of the Vigileo FloTrac(™) , LiDCO(™) , USCOM and CardioQ(™) to detect hypovolaemia in conscious volunteers: a proof of concept study

It is important to detect and treat hypovolaemia; however, detection is particularly challenging in the conscious, spontaneously breathing patient. Eight healthy male volunteers were monitored using four minimally invasive monitors: Vigileo FloTrac(™) ; LiDCOrapid(™) ; USCOM 1A; and CardioQ(™) oesophageal Doppler. Monitor output and clinical signs were recorded during incremental venesection of 2.5% estimated blood volume aliquots to a total of 20% blood volume removed. A statistically significant difference from baseline stroke volume was detected after 2.5% blood loss using the LiDCO (p = 0.007), 7.5% blood loss using the USCOM (p = 0.019), and 12.5% blood loss using the CardioQ (p = 0.046) and the FloTrac (p = 0.028). Receiver operator characteristic curves for predicting > 10% blood loss had areas under the curve of 0.68-0.82. The minimally invasive cardiac output devices tested can detect blood loss by a reduction in stroke volume in awake volunteers, and may have a role in guiding fluid replacement in conscious patients with suspected hypovolaemia.