Development, Validation, and Comparison of a Novel Nociception/Anti-Nociception Monitor against Two Commercial Monitors in General Anesthesia

Ionescu, Clara M.; Copot, Dana; Yumuk, Erhan; De Keyser, Robin; Muresan, Cristina; Birs, Isabela Roxana; Ben Othman, Ghada; Farbakhsh, Hamed; Ynineb, Amani R.; Neckebroek, Martine

doi:10.3390/s24072031

Cited by 10 publications

(1 citation statement)

References 51 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…MAP was monitored in mmHg using a hemodynamics monitor (GE HealthCare, Chicago, IL, USA). The primary objective outcome of this clinical study has been published in [19]. The biometrics of the 70 patients are detailed in Appendix B, and the variability in MAP among these patients is discussed in Appendix C.…”

Section: Clinical Trial Protocolmentioning

confidence: 99%

Selecting Optimal Long Short-Term Memory (LSTM) Architectures for Online Estimation of Mean Arterial Pressure (MAP) in Patients Undergoing General Anesthesia

Ben Othman,

Copot,

Yumuk

et al. 2024

Applied Sciences

Self Cite

View full text Add to dashboard Cite

In the realm of anesthetic management during surgical procedures, the reliable estimation of mean arterial pressure (MAP) is critical for ensuring patient safety and optimizing drug administration. This paper investigates the determination of the optimal Long Short-Term Memory (LSTM) architectures aimed at enhancing the estimation of MAP. Using data from a trial involving 70 patients undergoing Total Intravenous Anesthesia (TIVA) provides the effect-site concentrations of Propofol and Remifentanil as key input variables for LSTM models. Our solution categorizes the selection strategies into three distinct methodologies: (i) a population-based method applying a single model across all patients, (ii) a patient-specific method tailoring models to individual physiological responses, and (iii) a novel category-specific method that groups patients based on the correlation between input variables, the effect-site concentrations of Propofol and Remifentanil, and MAP output. The novelty of this paper lies in the proposed method to identify the optimal architecture, evaluating 288 models to fine-tune the best model for each patient and category. Our findings suggest that the patient-specific model outperforms others, highlighting the benefits of personalized model architectures in medical artificial intelligence (AI) applications. The category-specific models provide a pragmatic solution, with reasonable accuracy and enhanced computational efficiency. By contrast, the population-based models, while efficient, have a lower estimation accuracy. This study confirms the significance of sophisticated LSTM architectures in medical AI, providing insights into their potential for advancing patient-specific anesthetic care by accurately online estimating MAP.

show abstract

Section: Clinical Trial Protocolmentioning

confidence: 99%