Dr Xiaoxuan Liu MBChB

Dr Xiaoxuan Liu

Institute of Inflammation and Ageing

Contact details

Address
Institute of Inflammation and Ageing
University of Birmingham
Edgbaston
Birmingham
B15 2TT
UK

Dr Xiao Liu is a clinical researcher at the Academic Unit of Ophthalmology, Institute of Inflammation and Ageing, University of Birmingham and a junior doctor in ophthalmology at University Hospitals Birmingham NHS Foundation Trust.

Her doctoral research is around the validation of instrument-based technologies for detecting and quantifying inflammation in the eye. She is working with Professor Alastair Denniston’s team to develop a new technique based on optical coherence tomography (OCT), a non-invasive retinal imaging device, for the assessment of vitreous inflammation in uveitis.

Dr Liu has particular interests in health data research and applications of machine learning in healthcare. Her work is based around diagnostic test evaluation (including machine learning algorithms as tests) and improving reporting standards for machine learning interventions in clinical studies (CONSORT-AI and SPIRIT-AI).

She is also an honorary research fellow under the Artificial Intelligence hub at Moorfields Eye Hospital Reading Centre in London and sits on the Health Data Research UK Early Career Researcher Committee.

Qualifications

  • MBChB in Medicine and Surgery, University of Birmingham, 2015

Biography

Dr Xiaoxuan Liu studied medicine at the University of Birmingham College of Medical and Dental Sciences and graduated in in 2015. She completed her foundation programme as a junior doctor at University Hospitals Birmingham NHS Foundation Trust and Sandwell and West Birmingham Hospitals NHS Trust.

Dr Liu began her doctoral study at the University of Birmingham in 2017 – present. She is a clinical research fellow at University Hospitals Birmingham NHS Foundation Trust and an honorary clinical research fellow at Moorfields Eye Hospital, London.

Research

Xiaoxuan Liu is a member of the ‘Extended OCT-Quantification of Uveitis Activity for Trial Outcomes and Reporting’ (EQUATOR) working group.

Other activities

  • Clinical research fellow at University Hospitals Birmingham NHS Foundation Trust
  • Honorary clinical research fellow at Moorfields Eye Hospital, London
  • Health Data Research UK Early Career Researchers Committee

Publications

Liu X, Faes L, Kale AU, et al. A comparison of deep learning performance against health-care professionals in detecting diseases from medical imaging: a systematic review and meta-analysis. The Lancet Digital Health. September 2019. doi:10.1016/S2589-7500(19)30123-2

CONSORT-AI and SPIRIT-AI Steering Group. Reporting guidelines for clinical trials evaluating artificial intelligence interventions are needed. Nat Med. September 2019. doi:10.1038/s41591-019-0603-3

Liu X, Faes L, Calvert MJ, Denniston AK, CONSORT/SPIRIT-AI Extension Group. Extension of the CONSORT and SPIRIT statements. Lancet. September 2019. doi:10.1016/S0140-6736(19)31819-7

Faes L, Wagner SK, Fu DJ, et al. Automated deep learning design for medical image classification by health-care professionals with no coding experience: a feasibility study. The Lancet Digital Health. 2019;1(5):e232-e242.

Liu X, Solebo AL, Faes L, et al. Instrument-based Tests for Measuring Anterior Chamber Cells in Uveitis: A Systematic Review. Ocular Immunology and Inflammation. 2019:1-12. doi:10.1080/09273948.2019.1640883

Liu X, Kelly SR, Montesano G, et al. Evaluating the Impact of Uveitis on Visual Field Progression Using Large Scale Real-World Data. Am J Ophthalmol. June 2019. doi:10.1016/j.ajo.2019.06.004

Ometto G, Moghul I, Montesano G, et al. ReLayer: a Free, Online Tool for Extracting Retinal Thickness From Cross-Platform OCT Images. Transl Vis Sci Technol. 2019;8(3):25.

Bailie HN, Liu X, Bruynseels A, Denniston AK, Shah P, Sii F. The Uveitis Patient Passport: A Self-Care Tool. Ocul Immunol Inflamm 2019:1–6. doi:10.1080/09273948.2019.1569240.

Liu X, Solebo AL, Keane PA, Moore DJ, Denniston AK. Instrument-based tests for measuring anterior chamber cells in uveitis: a systematic review protocol. Syst Rev 2019;8:30. doi:10.1186/s13643-019-0946-3.

Liu X, Keane PA, Denniston AK. Time to regenerate: the doctor in the age of artificial intelligence. J R Soc Med. 2018;111:113–6. Available from: http://journals.sagepub.com/doi/10.1177/0141076818762648.

Damato EM, Dawson S, Liu X, Mukherjee C, Horsburgh J, Denniston AK, et al. A retrospective cohort study of patients treated with anti-tuberculous therapy for presumed ocular tuberculosis. J Ophthalmic Inflamm Infect. 2017;7:23. Available from: http://www.ncbi.nlm.nih.gov/pubmed/29204932

Montesano G, Way CM, Ometto G, Ibrahim H, Jones PR, Carmichael R, et al. Optimizing OCT acquisition parameters for assessments of vitreous haze for application in uveitis. Sci Rep. Nature Publishing Group; 2018. Available from: http://www.nature.com/articles/s41598-018-20092-y

Liu X, Sii F, Horsburgh J, Shah P. Anuric acute kidney injury due to low dose oral acetazolamide with hypercrystalluria. Clin Experiment Ophthalmol. Wiley/Blackwell (10.1111); 2017;45:927–9. Available from: http://doi.wiley.com/10.1111/ceo.12980

Liu X, Calvert PA, Arif S, Keane PA, Denniston AK. Patent foramen ovale presenting as visual loss. JRSM open. SAGE Publications; 2016;8:2054270416669302. Available from: http://www.ncbi.nlm.nih.gov/pubmed/28203381