e-Health Research Group

The eHealth research group undertakes research into a diverse range of subjects addressing real world problems in areas of health, medicine, wellbeing and biology. Expertise is drawn from across the University with members from School of Computer Science and Mathematics. The eHealth research group, led by Dr Gabriela Czanner, has over 30 years of research expertise with a significant component of real-world applications in technology development to support healthcare practice and medicine.

Our technological expertise is aligned into six research themes:

  1. Artificial Intelligence, Machine Learning, Data Science, Data Mining, Computer-Aided Diagnosis of Diseases
  2. Statistics, Evidence, Risk Analysis, Epidemiology, Design of Clinical and Laboratory Studies
  3. Bioinformatics, Mathematical Modelling, Mathematical Biology, Dynamic Systems
  4. Digital Forensics, Cyber Security, Cloud Computing, Internet of Things (IoT)
  5. Model-based Software Engineering, Parallel/Distributed Processing of Big Data, Safety Assurance, Robotic Systems, Databases
  6. Image Processing, Computer Vision, AR/VR, AI in Games, Visualisation, Wearable/Mobile Sensors, Digital Signal Processing

The external collaborators of eHealth Research Group

Collaborations are important to the group. Consequently, researchers from the Group have formed national and international partnerships, including Harvard/MIT (USA), Royal Liverpool University Hospital and St Paul Eye’s Unit (UK), Slovak University of Technology (Slovakia), Ulster University (NI), Lancaster University (UK), Aravind Eye Care Hospital (India), University of York (UK), Dalian University of Technology (China), Universitas Multimedia Nusantara (Indonesia), University of Kurdistan Hewler, and Polytechnic university of Sulaimanyah (Iraq), Institute for Mathematics Stochastic (Göttingen, Germany) and Royal Children’s Hospital in Melbourne (Australia).

Our eHealth members

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Research Impact of eHealth Research Group

The group undertakes research and collaboration in a variety of areas and has an excellent track record of obtaining external funding from EPSRC, GCRF, British Foundation for Prevention of Blindness (BCPB), NIHR, Dunhill Medical Trust, Wellcome Trust, HEFCE, Royal Academy of Engineering, Innovate UK, Knowledge Transfer Partnership, North West Regional Innovation Fund and Bupa.

For instance, one project that has been funded by EPSRC [Grant Ref: EP/R014094/1] was working on an innovative idea to help transform early detection of diabetic eye disease China. This international research project entitled “Development of New Low Cost Point of Care Diagnostic Technologies for Diabetic Retinopathy in China” was jointly funded by The Engineering and Physical Sciences Research Council (EPSRC) and the National Institute for Health Research (NIHR) through the Global Challenges Research Fund (GCRF). This has been a collaboration of the School of Computer Science and Mathematics at Liverpool John Moores University, The University of Liverpool, Royal Liverpool University Hospital and Peking University People's Hospital. In another project, the members of the group are conducting research with Aravind Eye Hospital (India), Ulster University and Lancaster University, funded by British Council for Prevention of Blindness (BCPB). We are looking at the use of portable retinal camera and develop AI to detect glaucoma.

Highlighted publications of the eHealth Research Group

We name here several notable publications, in our six research themes:

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1. Artificial Intelligence, Machine Learning, Data Science, Data Mining, Computer-Aided Diagnosis of Diseases

Our current work includes automated detection of eye diseases, epilepsy, myocardial infarction detection and diagnosis.

  • MacCormick IJC, Williams BM, Zheng Y, Li K, Al-Bander B, Czanner S, Cheeseman R, Willoughby CE, Brown ENB, Spaeth GL, Czanner G. Accurate, fast, data efficient and interpretable glaucoma diagnosis with automated spatial analysis of the whole cup to disc profile, PLOSONE, published January 10, 2019. https://doi.org/10.1371/journal.pone.0209409.
  • T. Rahman, A. Akinbi, M.E.H. Chowdhury, T.A.Rashid, A.Sengur, A. Khandakar, K.R. Islam, A.M. Ismael, "COV-ECGNET: COVID-19 detection using ECG trace images with deep convolutional neural network", Health Information Science and Systems, vol. 10, no. 1, 2022. Available: 10.1007/s13755-021-00169-1
  • Hughes D, Komarek A, Czanner G, Garcia-Finana M. Dynamic longitudinal discriminant analysis using multiple longitudinal markers of different types. Statistical Methods in Medical Research. First published date: October 2016. DOI: 10.1177/0962280216674496
  • Krishna Adithya, V., Williams, B. M., Czanner, S., Kavitha, S., Friedman, D. S., Willoughby, C. E., Czanner, G. (2021). Effunet-spagen: An efficient and spatial generative approach to glaucoma detection. Journal of Imaging, 7(6). doi:10.3390/jimaging7060092

2. Statistics, Evidence, Risk Analysis, Epidemiology, Design of Clinical and Laboratory Studies

Our recent work includes studies of risk factors and evidence synthesis for eye diseases and cardiovascular diseases.

  • Mehta, J., Czanner, G., Harding, S., Newsham D., Robinson J. Visual risk factors for falls in older adults: a case-control study. BMC Geriatr 22, 134 (2022). https://doi.org/10.1186/s12877-022-02784-3
  • R Hussain, G Czanner, A Taktak, B Damato, A Praidou, H Heimann. Mortality of patients with uveal melanoma detected by diabetic retinopathy screening. Retina 2020, 40 (11), 2198-2206
  • Galvain, T., Hill, R., Donegan, S. Lisboa P, Lip GYH, Czanner G. The management of anticoagulants in patients with atrial fibrillation and history of falls or risk of falls: protocol for a systematic review and meta-analysis. Syst Rev 11, 63 (2022). https://doi.org/10.1186/s13643-022-01937-0

3. Bioinformatics, Mathematical Modelling, Mathematical Biology, Dynamic Systems

Our current work is on understanding the biological processes in ion channels and spiking behaviour of neurons in health and disease.

  • Siekmann I, Fackrell M, Crampin EJ, Taylor P. 2016. Modelling modal gating of ion channels with hierarchical Markov models, Proceedings of the Royal Society A: Mathematical, Physical and Engineering Sciences, 472
  • Siekmann I, Sneyd J, Crampin EJ. 2014. Statistical analysis of modal gating in ion channels, Proceedings of the Royal Society A: Mathematical, Physical and Engineering Sciences, 470
  • Siekmann I, Wagner LE, Yule D, Crampin EJ, Sneyd J. 2012. A Kinetic Model for Type I and II IP3R Accounting for Mode Changes, Biophysical Journal, 103 :658-668
  • Siekmann I, Bjelosevic S, Landman K, Monagle P, Ignjatovic V, Crampin EJ. 2019. Mathematical modelling indicates that lower activity of the haemostatic system in neonates is primarily due to lower prothrombin concentration, Scientific Reports, 9
  • Czanner G, Sarma SV, Ba D, Eden UT, Wu W, Eskandar E, Lim HH, Temereanca S, Suzuki WA and Brown EN. Measuring the signal-to-noise ratio of a neuron. Proceedings National Academy of Sciences (PNAS) USA. 2015 Jun 9;112(23):7141-6. (IF 9.4)

4. Digital Forensics, Cyber Security, Cloud Computing, Internet of Things (IoT)

Our current work includes security problems post COVID-19 pandemic and challenges for wearable IoT forensics.

  • A. Akinbi, M. Forshaw and V. Blinkhorn, "Contact tracing apps for the COVID-19 pandemic: a systematic literature review of challenges and future directions for neo-liberal societies", Health Information Science and Systems, vol. 9, no. 1, 2021. Available: 10.1007/s13755-021-00147-7
  • L. Dawson and A. Akinbi, "Challenges and opportunities for wearable IoT forensics: TomTom Spark 3 as a case study", Forensic Science International: Reports, vol. 3, p. 100198, 2021. Available: 10.1016/j.fsir.2021.100198
  • A. Akinbi and E. Ojie, "Forensic analysis of open-source XMPP/Jabber multi-client instant messaging apps on Android smartphones", SN Applied Sciences, vol. 3, no. 4, 2021. Available: 10.1007/s42452-021-04431-9

5. Model-based Software Engineering, Parallel/Distributed Processing of Big Data, Safety Assurance, Robotic Systems, Databases

We currently work on development and application of the model based software engineering to domains from big data, safety assurance and to sports science.

  • Reilly B, Morgan O, Czanner G, Robinson MA. Automated Classification of Changes of Direction in Soccer Using Inertial Measurement Units. Sensors. 2021; 21(14):4625. https://doi.org/10.3390/s21144625
  • R. Wei, Z. Jiang, X. Guo, H. Mei, A. Zolotas, T. Kelly. “Designing Critical Systems with Iterative Automated Safety Analysis.” 59th Design Automation Conference (DAC ‘22)
  • A. Zolotas, H. Hoyos Rodriguez, S. Hutchesson, B. Sanchez, A. Grigg, M. Li, D. Kolovos, R. Paige. "Bridging proprietary modelling and open-source model management tools: the case of PTC integrity modeller and epsilon." International Journal on Software and Systems Modeling. 19, 17–38 (2020). https://doi.org/10.1007/s10270-019-00732-1
  • R. Wei, A. Zolotas, H. Hoyos Rodriguez, S. Gerasimou, D. Kolovos, R. Paige. "Automatic Generation of UML Profile Graphical Editors for Papyrus." International Journal on Software and Systems Modeling.
  • J. Harbin, S. Gerasimou, N. Matragkas, A. Zolotas, R. Calinescu. "Model-driven Simulation-based Analysis for Multi-robot Systems." 2021 ACM/IEEE 24th International Conference on Model Driven Engineering Languages and Systems (MODELS '21) https://eprints.whiterose.ac.uk/176796/

6. Image Processing, Computer Vision, AR/VR, AI in Games, Visualisation, Wearable/Mobile Sensors, Digital Signal Processing

Our current work is on understanding the movements of football players using GPS data, automated processing of images from OCT, MRI, and non-invasive microscopy.

  • X Chen, BM Williams, SR Vallabhaneni, G Czanner, R Williams, Y Zheng. Learning active contour models for medical image segmentation. Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp 11632-11640, 2019 (Citations=79)
  • Mölder, A. L., Persson, J., El-Schich, Z., Czanner, S., & Gjörloff-Wingren, A. (2017). Supervised classification of etoposide-treated in vitro adherent cells based on noninvasive imaging morphology. Journal of Medical Imaging, 4(2). doi:10.1117/1.JMI.4.2.021106
  • Natalia, F., Young, J.C., Afriliana, N., Meidia, H., Yunus, R.E., Sudirman, S.: Automated selection of mid-height intervertebral disc slice in traverse lumbar spine MRI using a combination of deep learning feature and machine learning classifier. PLoS One. 17, e0261659 (2022). https://doi.org/10.1371/journal.pone.0261659
  • Natalia, F., Meidia, H., Afriliana, N., Young, J.C., Yunus, R.E., Al-Jumaily, M., Al-Kafri, A., Sudirman, S.: Automated measurement of anteroposterior diameter and foraminal widths in MRI images for lumbar spinal stenosis diagnosis. PLoS One. 15, 1–27 (2020). https://doi.org/10.1371/journal.pone.0241309
  • Al-Kafri, A.S., Sudirman, S., Hussain, A., Al-Jumeily, D., Natalia, F., Meidia, H., Afriliana, N., Al-Rashdan, W., Bashtawi, M., Al-Jumaily, M.: Boundary Delineation of MRI Images for Lumbar Spinal Stenosis Detection Through Semantic Segmentation Using Deep Neural Networks. IEEE Access. 7, 43487–43501 (2019). https://doi.org/10.1109/ACCESS.2019.2908002

Contact the e-Health Research Group

If you’d like to ask a question or find out more about information about this Group, please contact the team using the details below.

Contact: Dr Gabriela Czanner

Email: g.czanner@ljmu.ac.uk

Phone: +44 (0) 151 231 8038

Address:

School of Computer Science and Mathematics
Liverpool John Moores University
Byrom Street
Liverpool
L3 3AF
UK