![Prosper Oyibo BEng (Hons), MSc, PhD](https://profiles-cardiff.imgix.net/attachments/5292?w=200&h=200&auto=format&crop=faces&fit=crop&q=90")
Prosper Oyibo
(he/him)
BEng (Hons), MSc, PhD
Teams and roles for Prosper Oyibo
Research Associate
Lecturer
Overview
I am a Research Associate in Medical Engineering within the Life Imaging and Data Analytics Group at Cardiff University. My work focuses on bridging the gap between advanced technological innovation and global health equity through the hardware-software co-design of intelligent diagnostic systems.
Currently, I lead the technical development of SPAARC, an advanced radiomics and medical image analysis platform for cancer diagnosis. This work is supported by an EPSRC Impact Acceleration Account grant to drive the clinical and commercial translation of these predictive tools.
My research foundation uniquely combines physical optical hardware engineering with advanced AI. During my PhD in Systems and Control at Delft University of Technology, I engineered the "Schistoscope", an open-source, AI-powered digital microscope for resource-limited settings. I successfully translated this device into the field, leading international validation studies in Nigeria and Gabon that achieved high diagnostic accuracy across thousands of clinical samples.
My broader research interests include advancing software-defined computational imaging, particularly using Fourier Ptychographic Microscopy (FPM) to overcome the physical limitations of conventional optics. Through this approach, I aim to democratize access to high-performance screening for diseases like malaria and cervical cancer in low- and middle-income countries.
Publication
2025
- Oyibo, P. et al. 2025. Deep learning-based automated detection and multiclass classification of soil-transmitted helminths and Schistosoma mansoni eggs in fecal smear images. Scientific Reports 15 (1) 21495. (10.1038/s41598-025-02755-9)
- Oyibo, P. et al. 2025. 3D DeepLab-based automated GTV segmentation in head and neck cancer using PET/CT imaging. Presented at: ESTRO 2025 Vienna, Austria 2 - 6 May 2025. Radiotherapy and Oncology. Vol. 206.Elsevier. , pp.S2536-S2538. (10.1016/S0167-8140(25)01892-4)
2024
- Oyibo, P. , Brynolfsson, P. and Spezi, E. 2024. Integrating radiomic image analysis in the Hero Imaging platform. Presented at: Cardiff University School of Engineering Research Conference 2024 Cardiff, UK 12th - 14th June 2024. Published in: Spezi, E. and Bray, M. eds. Proceedings of the Cardiff University School of Engineering Research Conference 2024. Cardiff University Press. , pp.23-27. (10.18573/conf3.g)
- Oyibo, P. et al. 2024. An automated slide scanning system for membrane filter imaging in diagnosis of urogenital schistosomiasis. Journal of Microscopy 294 (1), pp.52-61. (10.1111/jmi.13269)
- Meulah, B. et al., 2024. Validation of artificial intelligence-based digital microscopy for automated detection of Schistosoma haematobium eggs in urine in Gabon. PLoS Neglected Tropical Diseases 18 (2) e0011967. (10.1371/journal.pntd.0011967)
2023
- Oyibo, P. et al. 2023. Two-stage automated diagnosis framework for urogenital schistosomiasis in microscopy images from low-resource settings. Journal of Medical Imaging 10 (04) 044005. (10.1117/1.JMI.10.4.044005)
2022
- Bengtson, M. et al., 2022. A usability study of an innovative optical device for the diagnosis of schistosomiasis in Nigeria. Presented at: Global Humanitarian Technology Conference (GHTC) Santa Clara, CA, USA 8-11 September 2022. Proceedings of Global Humanitarian Technology Conference. IEEE. , pp.17-22. (10.1109/GHTC55712.2022.9911019)
- Oyibo, P. et al. 2022. Schistoscope: An automated microscope with artificial intelligence for detection of schistosoma haematobium eggs in resource-limited settings. Micromachines 13 (5) 643. (10.3390/mi13050643)
- Meulah, B. et al., 2022. Performance evaluation of the Schistoscope 5.0 for (semi-)automated digital detection and quantification of schistosoma haematobium eggs in Urine: A field-based study in Nigeria. American Journal of Tropical Medicine and Hygiene 107 (5), pp.1047–1054. (10.4269/ajtmh.22-0276)
2020
- Carel Diehl, J. et al., 2020. Schistoscope: smartphone versus Raspberry Pi based low-cost diagnostic device for urinary Schistosomiasis. Presented at: 2020 IEEE Global Humanitarian Technology Conference (GHTC) Seattle 29 October - 1 November 2020. Proceedings of 2020 IEEE Global Humanitarian Technology Conference (GHTC). IEEE. (10.1109/ghtc46280.2020.9342871)
Articles
- Oyibo, P. et al. 2025. Deep learning-based automated detection and multiclass classification of soil-transmitted helminths and Schistosoma mansoni eggs in fecal smear images. Scientific Reports 15 (1) 21495. (10.1038/s41598-025-02755-9)
- Oyibo, P. et al. 2024. An automated slide scanning system for membrane filter imaging in diagnosis of urogenital schistosomiasis. Journal of Microscopy 294 (1), pp.52-61. (10.1111/jmi.13269)
- Meulah, B. et al., 2024. Validation of artificial intelligence-based digital microscopy for automated detection of Schistosoma haematobium eggs in urine in Gabon. PLoS Neglected Tropical Diseases 18 (2) e0011967. (10.1371/journal.pntd.0011967)
- Oyibo, P. et al. 2023. Two-stage automated diagnosis framework for urogenital schistosomiasis in microscopy images from low-resource settings. Journal of Medical Imaging 10 (04) 044005. (10.1117/1.JMI.10.4.044005)
- Oyibo, P. et al. 2022. Schistoscope: An automated microscope with artificial intelligence for detection of schistosoma haematobium eggs in resource-limited settings. Micromachines 13 (5) 643. (10.3390/mi13050643)
- Meulah, B. et al., 2022. Performance evaluation of the Schistoscope 5.0 for (semi-)automated digital detection and quantification of schistosoma haematobium eggs in Urine: A field-based study in Nigeria. American Journal of Tropical Medicine and Hygiene 107 (5), pp.1047–1054. (10.4269/ajtmh.22-0276)
Conferences
- Oyibo, P. et al. 2025. 3D DeepLab-based automated GTV segmentation in head and neck cancer using PET/CT imaging. Presented at: ESTRO 2025 Vienna, Austria 2 - 6 May 2025. Radiotherapy and Oncology. Vol. 206.Elsevier. , pp.S2536-S2538. (10.1016/S0167-8140(25)01892-4)
- Oyibo, P. , Brynolfsson, P. and Spezi, E. 2024. Integrating radiomic image analysis in the Hero Imaging platform. Presented at: Cardiff University School of Engineering Research Conference 2024 Cardiff, UK 12th - 14th June 2024. Published in: Spezi, E. and Bray, M. eds. Proceedings of the Cardiff University School of Engineering Research Conference 2024. Cardiff University Press. , pp.23-27. (10.18573/conf3.g)
- Bengtson, M. et al., 2022. A usability study of an innovative optical device for the diagnosis of schistosomiasis in Nigeria. Presented at: Global Humanitarian Technology Conference (GHTC) Santa Clara, CA, USA 8-11 September 2022. Proceedings of Global Humanitarian Technology Conference. IEEE. , pp.17-22. (10.1109/GHTC55712.2022.9911019)
- Carel Diehl, J. et al., 2020. Schistoscope: smartphone versus Raspberry Pi based low-cost diagnostic device for urinary Schistosomiasis. Presented at: 2020 IEEE Global Humanitarian Technology Conference (GHTC) Seattle 29 October - 1 November 2020. Proceedings of 2020 IEEE Global Humanitarian Technology Conference (GHTC). IEEE. (10.1109/ghtc46280.2020.9342871)
Teaching
Currently, I teach medical imaging to third- and fourth-year Medical Engineering students.
Biography
- 2019-2025: PhD in System and Control Engineering, Delft University of Technology, Netherlands. Title : Development of smart optical diagnostic device for parasitic diseases (Supervisors: Prof. Gleb Vdovine, Prof. Jan-Carel Diehl, Prof. Wellington Oyibo).
- 2014-2017: Masters of Control Engineering, Ahmadu Bello University, Zaria, Nigeria. Title : Development a power-line detection algorithm for optical images (Supervisors: Prof. M. B. Mu'azu, Prof. Boyi Jimoh).
Contact Details
[email protected]
Queen's Buildings - North Building, Room N1.51, 5 The Parade, Newport Road, Cardiff, CF24 3AA
Queen's Buildings - North Building, Room N1.51, 5 The Parade, Newport Road, Cardiff, CF24 3AA
Specialisms
- Computer vision
- Machine learning
- Digital Microscopy
- medical image analysis
- Control engineering