System-Level Analysis of Closed-Loop Anesthesia Control Under Temporal Sensor Faults via UPPAAL-SMC.

Hypnosis control is an essential process commonly used during medical surgeries and operations. In clinical practice, this process is achieved by an anesthesiologist who estimates the required dose for a patient and monitors the hypnotic status of the patient. For closed-loop sedation control systems to be approved for clinical practice, they have to demonstrate efficiency and robustness under parameter uncertainties and potential device faults. In this paper, new modeling and analysis of the closed-loop anesthesia administration are proposed using priced timed automata. The modeling involved the physiological system, the closed-loop controllers, and the fault scenario. The physiological model is based on a general model that accounts for parameter variability and residual errors from a broad group of data-sets. Two control techniques are analyzed: the proportional-integral-derivative controller and a variant of the sliding mode controller. The results have shown that the performance of both controllers was impacted by the sensor fault with the later one outperforming the former.Clinical relevance- The proposed in-silico methodology is used to estimate the performance degradation in closedloop anesthesia administration as a result of temporal faults. Moreover, it allows for evaluating different control techniques and help design reliable automatic control.

A hybrid camera- and ultrasound-based approach for needle localization and tracking using a 3D motorized curvilinear ultrasound probe.

Three-dimensional (3D) motorized curvilinear ultrasound probes provide an effective, low-cost tool to guide needle interventions, but localizing and tracking the needle in 3D ultrasound volumes is often challenging. In this study, a new method is introduced to localize and track the needle using 3D motorized curvilinear ultrasound probes. In particular, a low-cost camera mounted on the probe is employed to estimate the needle axis. The camera-estimated axis is used to identify a volume of interest (VOI) in the ultrasound volume that enables high needle visibility. This VOI is analyzed using local phase analysis and the random sample consensus algorithm to refine the camera-estimated needle axis. The needle tip is determined by searching the localized needle axis using a probabilistic approach. Dynamic needle tracking in a sequence of 3D ultrasound volumes is enabled by iteratively applying a Kalman filter to estimate the VOI that includes the needle in the successive ultrasound volume and limiting the localization analysis to this VOI. A series of ex vivo animal experiments are conducted to evaluate the accuracy of needle localization and tracking. The results show that the proposed method can localize the needle in individual ultrasound volumes with maximum error rates of 0.7 mm for the needle axis, 1.7° for the needle angle, and 1.2 mm for the needle tip. Moreover, the proposed method can track the needle in a sequence of ultrasound volumes with maximum error rates of 1.0 mm for the needle axis, 2.0° for the needle angle, and 1.7 mm for the needle tip. These results suggest the feasibility of applying the proposed method to localize and track the needle using 3D motorized curvilinear ultrasound probes.