An Interdisciplinary Research Project
As an interdisciplinary research effort, Riglo combines innovative data-analysis techniques together with EMG technology in order to provide enhanced clinical decision support within the domain of Anesthesia.
Our research findings and results are translated into technology for every-day use with the purpose to improve patient healthcare:
Mobile application as a practical tool to perform quantitative neuromuscular monitoring.
EMG hardware prototype with state-of-the-art clinical decision support.
Predictive AI algorithms that go beyond monitoring and forecast a patient's neuromuscular blockade.
Neuromuscular monitoring refers to the monitoring of a patient's muscular function during and after surgery. It enables an anesthesiologist to follow up on the effect of muscular-paralysing drugs, and is required to decide when a patient can safely be removed from breathing support.
In approximately 60% of surgeries performed worldwide, patients are given muscular-paralyzing drugs. By paralysing the breathing muscles, patients need to be given artificial support in order to breathe.
As a consequence of inadequate neuromuscular monitoring, (severe) complications from residual paralysis can result when a patient no longer receives breathing support.
140 million patients per year.
60% of all surgical operations in the world are being performed with muscular-paralyzing drugs and require neuromuscular monitoring.
14%–30% of patients who develop severe respiratory complications die within 30 days of major surgery.
Postoperative airway and respiratory complications.
20-40% of patients are diagnosed with residual paralysis.
Importance of Neuromuscular monitoring?
Our research group has developed a mobile application that offers a fast and easy way of performing neuromuscular monitoring.
Measures muscle contractions of a patient.
Our algorithms derive the necessary results to inform anesthesiologists of a patient's neuromuscular blockade status according to the latest norms.
Provides anesthesiologists with a practical tool to perform quantitative monitoring when conventional monitors are unavailable.
Our latest developments are focused on an electromyography-powered hardware prototype, that is equipped with state-of-the-art clinical decision software.
Embedded with EMG-technology to measure electrical impulses.
Measures and refines a patient's current degree of muscular activity.
Clinical decision support algorithms filter anomalies that occur during measurement and inform the care-taker.
Through Deep Learning, we intend to move beyond neuromuscular monitoring and predict a patient's neuromuscular blockade.
Redefine pharmacokinetic and pharmacodynamic models of muscular-paralyzing drugs.
Provide predictive analytics to anesthesiologists on the status of their patient.
Provide recommendation systems for decision support to the clinical caretaker.