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.

Tissue hysteresivity is an important marker for determining the onset and progression of respiratory diseases, calculated from forced oscillation lung function test data. This study aims to reduce the number and duration of required measurements by combining multivariate data from various sensing devices. We propose using the Forced Oscillation Technique (FOT) lung function test in both a low-frequency prototype and the commercial RESMON device, combined with continuous monitoring from the Equivital (EQV) LifeMonitor and processed by artificial intelligence (AI) algorithms. While AI and deep learning have been employed in various aspects of respiratory system analysis, such as predicting lung tissue displacement and respiratory failure, the prediction or forecasting of tissue hysteresivity remains largely unexplored in the literature. In this work, the Long Short-Term Memory (LSTM) model is used in two ways: (1) to estimate the hysteresivity coefficient $\eta$ using heart rate (HR) data collected continuously by the EQV sensor, and (2) to forecast $\eta$ values by first predicting the heart rate from electrocardiogram (ECG) data. Our methodology involves a rigorous two-hour measurement protocol, with synchronized data collection from the EQV, FOT, and RESMON devices. Our results demonstrate that LSTM networks can accurately estimate the tissue hysteresivity parameter $\eta$, achieving an $R^2$ of 0.851 and a mean squared error (MSE) of 0.296 for estimation, and forecast $\eta$ with an $R^2$ of 0.883 and an MSE of 0.528, while significantly reducing the number of required measurements by a factor of three (i.e., from ten to three) for the patient. We conclude that our novel approach minimizes patient effort by reducing the measurement time and the overall ambulatory time and costs while highlighting the potential of artificial intelligence methods in respiratory monitoring.

In the operating rooms and the intensive care unit, it is crucial to manage the patient’s hemodynamic status, which includes factors like cardiac output and mean arterial pressure. Anesthesiologists confront a difficult task while monitoring high-risk patients. Cardiac output optimization has been found to enhance the result of high-risk patients in terms of hospital stay, mortality rate, post-operative problems, etc. The application of standard control approaches is restricted because the mean arterial pressure response of a patient using vasoactive medicines is modeled by a first-order dynamical system with time-varying parameters and a time-varying delay in the control input. In order to circumvent implementation challenges, this work develops an approximation technique that describes the system using a higher-order model. Predictive control is therefore used to comprehend the practical application of higher-order hemodynamic systems. The effectiveness of this strategy is demonstrated by the simulations and outcomes that are given.

Surgical procedures exceeding six hours and intensive care for traumatic injuries often require prolonged general anesthesia, commonly maintained via induced coma. Similar protocols are used for COVID-19 patients under mechanical ventilation. These conditions pose challenges such as tissue drug accumulation and overdose risk. Extended supine or prone positioning alters tissue volumes based on their physical properties. Accurate anesthesia management depends on patient-specific models that characterize pharmacokinetics (PK, drug distribution) and pharmacodynamics (PD, drug effects) using compartmental representations. However, comorbidities such as obesity are typically overlooked in existing PK models. This study augments PK models to investigate the impact of obesity on drug distribution and clearance by incorporating the risk of drug trapping in adipose tissue as a nonlinear function of body mass index (BMI). A theoretical framework links BMI with tissue porosity and permeability, introducing a "trap" compartment to model delayed clearance in obese patients. The model is validated using in vitro impedance measurements and numerical simulations. Fat tissue properties were characterized via Cole-Cole fractional-order models, with parameters identified using a genetic algorithm. The nonlinear BMI-trapping relationship was estimated using the trust region method. Simulations covered both open-loop (single-bolus) and closed-loop (continuous infusion) scenarios using model predictive control(MPC). Impedance measurements and identified Cole-Cole fractional-order models confirmed volume-dependent properties of fat tissue. Simulations using clinical data demonstrated delayed drug clearance in high-BMI patients, which support the proposed theoretical background. Open-loop simulations showed prolonged drug retention, while closed-loop control using MPC maintained anesthetic depth with reduced total drug input. Differences in BIS nadir and total drug usage were observed across BMI and age groups, supporting the model's clinical applicability. The augmented model with MPC minimizes drug trapping and reduces total drug use, lowering overdose risk and supporting safer anesthesia management.