Automated 3D microprinting of movable type microvasculature-on-a-chip using AI algorithm development in the study of patient-specific vessel models for thrombosis assessment

Summary

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Synopsis

Cardiovascular diseases such as stroke and heart attacks are the leading cause of mortality worldwide. The underlying cause of disease is the development of clots within the circulatory system, which either initiate or eventually find their way to the key vessels of the human body, namely the carotid and coronary vessels, where partial or full occlusion may lead to the development of ischemic dysfunction characteristic of brain and heart failure. Thrombosis is a multivariate condition, where factors such as age, sex, activity, diet, and genetics may all play a role in the susceptibility to pathology. More notably, it has been shown that vessel geometry, vascular endothelium health, and endogenous blood coagulability all play a significant role in thrombogenesis, of which the presentations of each are highly patient-specific.

In response to this, our lab has developed personalised full-lumen microvasculature-on-a-chip devices to study the effect of patient-specific hemodynamic environment on thrombosis. The microchannels are designed to recapitulate the full 3D architecture of the patient vessels, and be coated with human carotid artery cells before being perfused with whole blood to fully replicate blood flow in the patient body. Using cutting-edge AI technology and in light of the avant-garde of machine learning and automation within global society today, we aim to develop an AI algorithm to aid the automation of 3D microprinting these vessel-chips, through both MRV segmentation of patient clinical images for double-layered chip alignment for the full lumen chips, and identifying key area of thrombus development and pattern in various patient vessel geometries. The algorithm derived images would then be used in the 3D chip fabrication process and experimental data analysis.

Additional information

Offering:

A PhD scholarship for 3.5 years at the RTP stipend rate (currently $40,109 in 2024). International applicants will have their tuition fees covered.

Successful candidates must:

Have a Bachelors degree (1st class honours or equivalent) or a Masters degree
Have be familiar with MATLAB, Python, ANSYS or any related algorithm programming software.
Have knowledge on techniques used in clinical image processing and machine learning identification.
Know how to work around 3D printing technologies, along with associated computer sofware to printer input interfaces.
Have an interest in AI, machine learning, microfluids, thrombosis, cardiovascular health.
Have a passion in biomedical research and development of novel diagnostic, testing, and treatment tools in the hematological health sector

How to Apply:

To apply, please email arnold.ju@sydney.edu.au the following:

CV
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Opportunity ID

The opportunity ID for this research opportunity is 3459