Associate Professor Sebastiano Barbieri
Associate Professor

Sebastiano Barbieri

Email: 

Background

Dr. Sebastiano Barbieri is Associate Professor and Principal Research Fellow at the Queensland Digital Health Centre, University of Queensland (UQ) and Adjunct Associate Professor at the Centre for Big Data Research in Health, University of New South Wales (UNSW). His work lies at the intersection of machine learning and healthcare, where he develops innovative computational methods to tackle pressing challenges in medicine.

Aiming to improve patient outcomes and streamline clinical workflows, Dr. Barbieri develops machine learning models tailored to real-world healthcare applications. His current research spans risk prediction using electronic medical records, medical image processing, and the safe and effective integration of AI into clinical decision-making processes.

A strong advocate for responsible AI in healthcare, Dr. Barbieri champions the use of emerging technologies such as synthetic data generation and federated learning. These approaches not only enhance data accessibility and privacy but also accelerate the development of robust, data-driven solutions for digital health.

Availability

Associate Professor Sebastiano Barbieri is:
Available for supervision

Qualifications

  • Bachelor of Mathematics, Universität des Saarlandes
  • Masters (Coursework) of Image Processing, Universität des Saarlandes
  • Doctor of Philosophy of Computer Science, Jacobs University
  • Masters (Coursework) of Biostatistics, Macquarie University

Search Professor Sebastiano Barbieri’s works on UQ eSpace

17 works between 2017 and 2026
All (17) Journal Article (16) Conference Publication (1)

1 - 17 of 17 works

2026

Journal Article

Acquisition-independent deep learning for quantitative MRI parameter estimation using neural controlled differential equations

Kuppens, Daan, Barbieri, Sebastiano, van den Berg, Daisy, Schouten, Pepijn, Thoeny, Harriet C., van Laarhoven, Hanneke WM., Wennen, Myrte and Gurney-Champion, Oliver J. (2026). Acquisition-independent deep learning for quantitative MRI parameter estimation using neural controlled differential equations. Medical Image Analysis, 107 (Part A) 103768, 1-15. doi: 10.1016/j.media.2025.103768

Acquisition-independent deep learning for quantitative MRI parameter estimation using neural controlled differential equations

2025

Journal Article

Aortic valve leaflet motion for diagnosis and classification of aortic stenosis using single view echocardiography

Meredith, Thomas, Mohammed, Farhan, Pomeroy, Amy, Barbieri, Sebastiano, Meijering, Erik, Jorm, Louisa, Roy, David, Hayward, Christopher, Kovacic, Jason C., Muller, David W. M., Feneley, Michael P. and Namasivayam, Mayooran (2025). Aortic valve leaflet motion for diagnosis and classification of aortic stenosis using single view echocardiography. Journal of Cardiovascular Imaging, 33 (1) 8, 1-10. doi: 10.1186/s44348-025-00051-8

Aortic valve leaflet motion for diagnosis and classification of aortic stenosis using single view echocardiography

2025

Conference Publication

Generating clinically realistic EHR data via a hierarchy- and semantics-guided transformer

Zhou, Guanglin and Barbieri, Sebastiano (2025). Generating clinically realistic EHR data via a hierarchy- and semantics-guided transformer. ECAI 2025: 28th European Conference on Artificial Intelligence, Bologna, Italy, 25-30 October 2025. Amsterdam, Netherlands: IOS Press. doi: 10.3233/faia251280

Generating clinically realistic EHR data via a hierarchy- and semantics-guided transformer

2025

Journal Article

Predicting cardiovascular events from routine mammograms using machine learning

Barraclough, Jennifer Yvonne, Gandomkar, Ziba, Fletcher, Robert A., Barbieri, Sebastiano, Kuo, Nicholas I-Hsien, Rodgers, Anthony, Douglas, Kirsty, Poppe, Katrina K., Woodward, Mark, Luxan, Blanca Gallego, Neal, Bruce, Jorm, Louisa, Brennan, Patrick and Arnott, Clare (2025). Predicting cardiovascular events from routine mammograms using machine learning. Heart, heartjnl-2025. doi: 10.1136/heartjnl-2025-325705

Predicting cardiovascular events from routine mammograms using machine learning

2025

Journal Article

A scoping review of the governance of federated learning in healthcare

Eden, Rebekah, Chukwudi, Ignatius, Bain, Chris, Barbieri, Sebastiano, Callaway, Leonie, de Jersey, Susan, George, Yasmeen, Gorse, Alain-Dominique, Lawley, Michael, Marendy, Peter, McPhail, Steven M., Nguyen, Anthony, Samadbeik, Mahnaz and Sullivan, Clair (2025). A scoping review of the governance of federated learning in healthcare. npj Digital Medicine, 8 (1) 427, 427-1. doi: 10.1038/s41746-025-01836-3

A scoping review of the governance of federated learning in healthcare

2025

Journal Article

Predictive factors of hypoglycemia in type 2 diabetes: a prospective study using machine learning

Shabestari, Motahare, Mehrabbeik, Akram, Barbieri, Sebastiano, Marques-Vidal, Pedro, Heshmati-nasab, Poria and Azizi, Reyhaneh (2025). Predictive factors of hypoglycemia in type 2 diabetes: a prospective study using machine learning. Scientific Reports, 15 (1) 18143, 1-13. doi: 10.1038/s41598-025-03030-7

Predictive factors of hypoglycemia in type 2 diabetes: a prospective study using machine learning

2025

Journal Article

Generative AI mitigates representation bias and improves model fairness through synthetic health data

Marchesi, Raffaele, Micheletti, Nicolo, Kuo, Nicholas I-Hsien, Barbieri, Sebastiano, Jurman, Giuseppe and Osmani, Venet (2025). Generative AI mitigates representation bias and improves model fairness through synthetic health data. PLoS Computational Biology, 21 (5) e1013080, 1-23. doi: 10.1371/journal.pcbi.1013080

Generative AI mitigates representation bias and improves model fairness through synthetic health data

2025

Journal Article

Machine learning cluster analysis identifies increased 12-month mortality risk in transcatheter aortic valve replacement recipients

Meredith, Thomas, Mohammed, Farhan, Pomeroy, Amy, Barbieri, Sebastiano, Meijering, Erik, Jorm, Louisa, Roy, David, Kovacic, Jason, Feneley, Michael, Hayward, Christopher, Muller, David and Namasivayam, Mayooran (2025). Machine learning cluster analysis identifies increased 12-month mortality risk in transcatheter aortic valve replacement recipients. Frontiers in Cardiovascular Medicine, 12 1444658, 1444658. doi: 10.3389/fcvm.2025.1444658

Machine learning cluster analysis identifies increased 12-month mortality risk in transcatheter aortic valve replacement recipients

2024

Journal Article

Enriching data science and health care education: application and impact of synthetic data sets through the health gym project

Kuo, Nicholas I-Hsien, Perez-Concha, Oscar, Hanly, Mark, Mnatzaganian, Emmanuel, Hao, Brandon, Di Sipio, Marcus, Yu, Guolin, Vanjara, Jash, Valerie, Ivy Cerelia, de Oliveira Costa, Juliana, Churches, Timothy, Lujic, Sanja, Hegarty, Jo, Jorm, Louisa and Barbieri, Sebastiano (2024). Enriching data science and health care education: application and impact of synthetic data sets through the health gym project. JMIR Medical Education, 10 (1) e51388, e51388. doi: 10.2196/51388

Enriching data science and health care education: application and impact of synthetic data sets through the health gym project

2023

Journal Article

Generating synthetic clinical data that capture class imbalanced distributions with generative adversarial networks: example using antiretroviral therapy for HIV

Kuo, Nicholas I-Hsien, Garcia, Federico, Sönnerborg, Anders, Böhm, Michael, Kaiser, Rolf, Zazzi, Maurizio, Polizzotto, Mark, Jorm, Louisa and Barbieri, Sebastiano (2023). Generating synthetic clinical data that capture class imbalanced distributions with generative adversarial networks: example using antiretroviral therapy for HIV. Journal of Biomedical Informatics, 144 104436. doi: 10.1016/j.jbi.2023.104436

Generating synthetic clinical data that capture class imbalanced distributions with generative adversarial networks: example using antiretroviral therapy for HIV

2023

Journal Article

Increasing national trend of direct-acting antiviral discontinuation among people treated for HCV 2016-2021

Carson, Joanne, Barbieri, Sebastiano, Matthews, Gail V., Dore, Gregory J. and Hajarizadeh, Behzad (2023). Increasing national trend of direct-acting antiviral discontinuation among people treated for HCV 2016-2021. Hepatology Communications, 7 (4) e0125. doi: 10.1097/HC9.0000000000000125

Increasing national trend of direct-acting antiviral discontinuation among people treated for HCV 2016-2021

2022

Journal Article

A machine learning approach to predict the added-sugar content of packaged foods

Davies, Tazman, Louie, Jimmy Chun Yu, Ndanuko, Rhoda, Barbieri, Sebastiano, Perez-Concha, Oscar and Wu, Jason H. Y (2022). A machine learning approach to predict the added-sugar content of packaged foods. Journal of Nutrition, 152 (1), 343-349. doi: 10.1093/jn/nxab341

A machine learning approach to predict the added-sugar content of packaged foods

2021

Journal Article

Using administrative data to predict cessation risk and identify novel predictors among new entrants to opioid agonist treatment

Bharat, Chrianna, Degenhardt, Louisa, Dobbins, Timothy, Larney, Sarah, Farrell, Michael and Barbieri, Sebastiano (2021). Using administrative data to predict cessation risk and identify novel predictors among new entrants to opioid agonist treatment. Drug and Alcohol Dependence, 228 109091, 1-8. doi: 10.1016/j.drugalcdep.2021.109091

Using administrative data to predict cessation risk and identify novel predictors among new entrants to opioid agonist treatment

2021

Journal Article

Improved unsupervised physics-informed deep learning for intravoxel incoherent motion modeling and evaluation in pancreatic cancer patients

Kaandorp, Misha P. T., Barbieri, Sebastiano, Klaassen, Remy, van Laarhoven, Hanneke W. M., Crezee, Hans, While, Peter T., Nederveen, Aart J. and Gurney-Champion, Oliver J. (2021). Improved unsupervised physics-informed deep learning for intravoxel incoherent motion modeling and evaluation in pancreatic cancer patients. Magnetic Resonance in Medicine, 86 (4), 2250-2265. doi: 10.1002/mrm.28852

Improved unsupervised physics-informed deep learning for intravoxel incoherent motion modeling and evaluation in pancreatic cancer patients

2020

Journal Article

Deep learning how to fit an intravoxel incoherent motion model to diffusion-weighted MRI

Barbieri, Sebastiano, Gurney-Champion, Oliver J., Klaassen, Remy and Thoeny, Harriet C. (2020). Deep learning how to fit an intravoxel incoherent motion model to diffusion-weighted MRI. Magnetic Resonance in Medicine, 83 (1), 312-321. doi: 10.1002/mrm.27910

Deep learning how to fit an intravoxel incoherent motion model to diffusion-weighted MRI

2017

Journal Article

Selection for biopsy of kidney transplant patients by diffusion-weighted MRI

Steiger, Philipp, Barbieri, Sebastiano, Kruse, Anja, Ith, Michael and Thoeny, Harriet C. (2017). Selection for biopsy of kidney transplant patients by diffusion-weighted MRI. European Radiology, 27 (10), 4336-4344. doi: 10.1007/s00330-017-4814-z

Selection for biopsy of kidney transplant patients by diffusion-weighted MRI

2017

Journal Article

Differentiation of prostate cancer lesions with high and with low Gleason score by diffusion-weighted MRI

Barbieri, Sebastiano, Brönnimann, Michael, Boxler, Silvan, Vermathen, Peter and Thoeny, Harriet C. (2017). Differentiation of prostate cancer lesions with high and with low Gleason score by diffusion-weighted MRI. European Radiology, 27 (4), 1547-1555. doi: 10.1007/s00330-016-4449-5

Differentiation of prostate cancer lesions with high and with low Gleason score by diffusion-weighted MRI

Availability

Associate Professor Sebastiano Barbieri is:
Available for supervision

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Available projects

Supervision history

Current supervision

  • Doctor Philosophy

    NINA national infrastructure for digital health

    Principal Advisor

    Other advisors: Professor Clair Sullivan

  • Master Philosophy

    Investigating the Impact of Artificial Intelligence on Clinical Workflows, Efficiency, and Health Economics: Implementation Insights, Bias Evaluation, and Strategic Mitigation strategies for Clinical AI Integration

    Principal Advisor

    Other advisors: Mr Anton Van Der Vegt

Enquiries

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