The PhD in Medical Sciences:
The University of Nicosia Medical School offers the degree PhD in Medical Sciences. The degree is awarded to students who successfully complete an independent research programme that breaks new ground in the chosen field of study. The PhD programme aspires to empower students to become independent researchers, thus advancing innovation and development.
The Research Project:
We are currently inviting application through a competitive process for high calibre candidates to apply for one PhD Scholarship in the fields of Neuroscience and Biomedical Engineering. The successful candidate will enrol on the PhD programme in Medical Sciences and will work under the Supervision of Dr Nicoletta Nicolaou with expertise in the fields of Neuroscience and Biomedical Engineering at the University of Nicosia Medical School.
Title of research project: Development of a closed-loop controller for automatic administration of anaesthetic and analgesic agents during surgery using machine learning methods.
Background and Rationale:
Current practice of anaesthesia during surgery involves administration of a “cocktail” of drugs (anaesthetics, analgesics, myorelaxants) to achieve the desired state of surgical anaesthesia. During surgery the patient is connected to a number of sensors that monitor vital signs (e.g. cardiovascular parameters, breathing etc.). The anaesthesiologist monitors these vital signs (visually on the monitoring device) and makes manual adjustments to the dosages of the different agents (anaesthetics, analgesics, muscle relaxants). In this open-loop approach the anaesthesiologist is effectively the one who manually closes the loop. The disadvantages of this open-loop approach are related mainly to the fact that the anaesthesiologist monitors the vital signs and is required to make a judgement call based on these visual observations as to whether or not adjustments are required to the dosages of the agents administered. These vital signs provide clues as to the underlying patient state, but they are not considered to be reliable indicators of the underlying “level of consciousness” or “depth of anaesthesia”.
In a closed-loop system, the loop is closed automatically: the patient state is estimated from the patient vital signs, and the dosages of agents are adjusted automatically by the device. The anaesthesiologist is not part of the automated closed loop, but still has the ability to bypass this automation and intervene manually. Closed-loop (CL) systems provide better stability of cardiovascular parameters (longer duration of heart rate and mean arterial pressure control), better performance and faster recovery compared to open-loop systems. The development of a CL anaesthetic administration system is a very complex process that must integrate information from a number of biological signals coming from the central and autonomic nervous systems. To date there are only a handful of CL systems that have been developed, but not yet routinely available for commercial use in routine surgery.
Aims and Objectives:
In this PhD Research Project, a CL system for automatic agent administration during surgery under general anaesthesia will be developed and simulated, using machine learning methods. The system will utilize features from the central and autonomic nervous systems (CNS and ANS respectively) for discrimination between awareness, anaesthesia and different levels of anaesthesia (light, surgical, deep anaesthesia). The system will offer improved anaesthetic experience that will be individualized, leading to a better experience (e.g. maintenance at surgical anaesthetic level, stability of cardiovascular activity, less time in recovery, minimal side effects from over-anaesthesia, faster release from hospital).
The main aims and objectives of this PhD research project are:
1. Characterize the relationships of real brain and brain-cardiovascular data recorded during surgeries under general anaesthesia using machine learning methods, as well as the relationships between these physiological signals and concentration of anaesthetic and analgesic agents.
2. Develop a closed-loop controller that utilizes the developed machine learning models to automatically modify the volume of anaesthetics and analgesics to achieve and maintain a desired level of (un)consciousness.
3. Develop a simulation that maps an observed or desired anaesthetic state to specific anaesthetic and analgesic dosages.
4. Test the performance of the developed machine learning controller on automatically modifying the anaesthetic and analgesic dosages to maintain a desired level of (un)consciousness as defined in the simulated data.
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