{"id":145494,"date":"2026-07-01T08:56:26","date_gmt":"2026-07-01T00:56:26","guid":{"rendered":"https:\/\/www.curtin.edu.au\/research\/?post_type=hdr-r-projects&p=145494"},"modified":"2026-07-01T08:56:26","modified_gmt":"2026-07-01T00:56:26","slug":"automatic-control-for-safe-assisted-mechanical-ventilation-of-lungs","status":"publish","type":"hdr-r-projects","link":"https:\/\/www.curtin.edu.au\/research\/hdr-r-projects\/automatic-control-for-safe-assisted-mechanical-ventilation-of-lungs\/","title":{"rendered":"Automatic control for safe assisted mechanical ventilation of lungs"},"content":{"rendered":"\n
\"Graphic<\/figure>\n\n\n\n

Mechanical ventilation is the mechanical delivery of air to a patient\u2019s respiratory system when the patient cannot maintain sufficient ventilation independently. In spontaneously breathing patients, who remain conscious and continue to make breathing efforts, excessive respiratory effort can lead to dangerously high lung pressures and overinflation. This condition, known as Patient Self-Inflicted Lung Injury, can result in significant lung damage. Current ventilator control systems primarily adjust breathing support according to the patient\u2019s respiratory effort; however, they do not specifically focus on minimising the risk of self-inflicted injury while maintaining adequate ventilation.<\/p>\n\n\n\n

Recent research has introduced a new metric called the Flow Index, developed by researchers in Brescia, to improve the quantification of patient respiratory effort. This metric has the potential to enhance the detection of unsafe breathing pressures and excessive effort in mechanically ventilated patients. By integrating the Flow Index with other physiological metrics, future ventilator systems may provide more adaptive and protective support.<\/p>\n\n\n\n

This project focuses on the development of an advanced feedback control system for assisted mechanical ventilation. The system will dynamically regulate ventilator assistance for spontaneously breathing patients to ensure adequate ventilation while reducing the likelihood of P-SILI. The project will involve modelling the patient\u2013ventilator interaction, developing reliable non-invasive effort metrics, and designing innovative control algorithms capable of optimising breathing quality and safety.<\/p>\n\n\n\n

Aim  <\/p>\n\n\n\n

The primary aim of this project is to develop an intelligent ventilator control system capable of supporting spontaneously breathing patients while minimising the risk of Patient Self-Inflicted Lung Injury (P-SILI). The system will adapt the level of ventilatory assistance in response to changes in patient respiratory effort and physiological conditions.<\/p>\n\n\n\n

A further aim is to create a mathematical model that accurately describes the interaction between the patient and the ventilator during assisted breathing. Unlike controlled ventilation in unconscious patients, partially autonomous ventilation involves a complex mutual interaction in which the patient adapts breathing frequency and waveform to the ventilator, while the ventilator responds through triggering mechanisms. Although this interaction is clinically recognised, comprehensive engineering models capable of describing this behaviour remain limited.<\/p>\n\n\n\n

The project also aims to evaluate and combine multiple respiratory effort metrics, including the Flow Index and airway occlusion techniques, to improve the estimation of patient breathing effort and breathing quality. These non-invasive estimates will be validated against invasive physiological measurements such as diaphragm electrical activity and intrapleural pressure.<\/p>\n\n\n\n

Another important aim is to design innovative control algorithms that extend beyond conventional airway pressure regulation. The proposed algorithms will seek to maintain safe tidal volumes, improve breath quality, and ensure protection against P-SILI while adapting to uncertainties within the human respiratory system.<\/p>\n\n\n\n

Objectives <\/p>\n\n\n\n

The objectives of this project are divided into three major research activities: modelling, metrics development, and control algorithm design.<\/p>\n\n\n\n

The first objective is to develop mathematical models of both the patient respiratory system and ventilator behaviour during assisted ventilation. These models will describe the dynamic interaction between patient respiratory effort and ventilator support. Real patient data will be used for model development and validation to ensure physiological relevance and accuracy.<\/p>\n\n\n\n

The second objective is to investigate and develop non-invasive methods for estimating patient respiratory effort and breathing quality. This includes implementing and evaluating metrics such as the Flow Index and airway occlusion measurements. The project will focus on optimally combining these metrics to provide reliable estimates despite the uncertainty and variability inherent in human physiology. These estimates will then be compared with invasive reference measurements obtained from diaphragm electrical activity and intrapleural pressure recordings.<\/p>\n\n\n\n

The third objective is to design and evaluate advanced feedback control algorithms for assisted mechanical ventilation. The control system will regulate ventilator support according to patient condition and respiratory effort while ensuring adequate ventilation and preventing lung overinflation. The algorithm will aim to improve overall breath quality and guarantee safe tidal volume delivery with reduced risk of P-SILI. Although clinical in vivo testing is outside the scope of this project, the proposed algorithms will be assessed through simulation models and validated using patient-derived data.<\/p>\n\n\n\n

Significance <\/p>\n\n\n\n

This project addresses a significant challenge in modern mechanical ventilation: balancing adequate respiratory support with the prevention of ventilator-associated lung injury in spontaneously breathing patients. Existing ventilator systems generally focus on maintaining ventilation but do not adequately address the risk of excessive patient effort and the resulting development of P-SILI.<\/p>\n\n\n\n

The significance of this research lies in its potential to improve patient safety and clinical outcomes through more intelligent and adaptive ventilator control strategies. By incorporating advanced respiratory effort metrics such as the Flow Index, the proposed system could provide earlier detection of harmful breathing patterns and automatically adjust support to reduce lung strain.<\/p>\n\n\n\n

The project also contributes to the advancement of control engineering in healthcare by developing mathematical models that describe the complex patient\u2013ventilator interaction. Such models are currently limited despite their importance in designing responsive and personalised ventilator systems. The integration of multiple physiological metrics and adaptive control techniques may also improve the quality and comfort of assisted breathing for patients.<\/p>\n\n\n\n

Furthermore, this research is particularly relevant in the context of increasing global interest in respiratory care following the COVID-19 pandemic, which highlighted the importance of effective assisted ventilation strategies. The outcomes of this project may support the future development of safer and more patient-centred ventilator technologies for critical care environments.<\/p>\n\n\n\n

Ideal Candidate <\/p>\n\n\n\n

We are seeking a self-motivated PhD candidate with excellent organisational, problem-solving, and independent working skills. The ideal candidate will possess strong mathematical and algorithmic abilities, as well as the capacity to manage multiple tasks simultaneously.<\/p>\n\n\n\n

Excellent communication skills are essential, as the candidate will be required to collaborate with researchers in mathematics and engineering, as well as medical doctors. The ability to adapt technical language and terminology to different professional audiences will be critical.<\/p>\n\n\n\n

Additionally, the applicants should meet the eligibility criteria for entry into a PhD program at Curtin University.\u00a0<\/p>\n\n\n\n

This project is open to Domestic applicants only.\u00a0<\/p>\n\n\n\n

Internship <\/p>\n\n\n\n

Through this project you will also have an internship opportunity.\u00a0As part of the PhD training programme, the student will be involved in one of the several commercial R&D projects that I am currently pursuing with industry through the Centre for Optimisation and Decision Science. The project will be related to the PhD research direction in terms of developing mathematical modelling and control.<\/p>\n\n\n\n

Scholarship  <\/p>\n\n\n\n

If you are identified as the preferred candidate for this project, you may be considered for an RTP scholarship<\/a>. <\/p>\n\n\n\n

Enquires and How to Apply <\/p>\n\n\n\n

For enquires about this opportunity contact Dr Fabrizio Padula at\u00a0Fabrizio.Padula@curtin.edu.au<\/a><\/p>\n\n\n\n

To formally apply submit an\u00a0Expression of Interest<\/a>\u00a0to Dr Fabrizio Padula during the Central Scholarship round (July 1st – July 31st 2026)\u00a0<\/p>\n\n\n\n

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