College: Wolfson College
Site: John Radcliffe Hospital
Research Building: Old Road Campus Research Building
Mohanad Alkhodari’s research interests include machine/deep learning, biosignals and bioimaging, and healthcare informatics. His current research focuses on developing artificial intelligence tools to leverage personalised healthcare in clinical practice, particularly for cardiovascular assessment. The ultimate goal of his DPhil project is to understand the hypertension progression landscape over the life course with the help of evolutionary AI-based models and large-scale multi-organ multi-modality data.
His research has led to the development of novel methodologies for estimating severity and discovering distinct phenotypes based on multi-organ damage associated with hypertension, offering new insights into disease mechanisms. As part of his DPhil, Mohanad designed and developed HyTwin, an AI-assisted prototype software that integrates his research algorithms to enable efficient clinical implementation. Alongside the team, Mohanad’s research has received international recognition, including being ranked among the top 25 student-led studies at the 2023 IEEE BIBM conference. His HyTwin prototype was further honoured with recognition on the 2024 Forbes 30 Under 30 MENA list and named a semi-finalist for the 2024 MIT Technology Review Innovators Under 35 Global after receiving the award for the MENA region in 2023.
With an h-index/i10-index of 15/23, Mohanad authored and co-authored three book chapters and more than 50 scientific papers in international journals and conferences, where he was the first, leading, corresponding, or presenting author in majority of them. Mohanad is an associate editor at PLoS ONE and an active reviewer for several reputable journals including IEEE JBHI, AHA/ASA Hypertension, and Frontiers in Physiology.
Hypertension
Artificial intelligence
Biosignals/bioimaging
HyperScore: A unified measure to model hypertension progression using multi-modality measurements and semi-supervised learning. Alkhodari M. et al, (2023), Proceedings – 2023 2023 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2023, 1886 – 1889
The role of artificial intelligence in hypertensive disorders of pregnancy: towards personalized healthcare. Alkhodari M. et al, (2023), Expert Rev Cardiovasc Ther, 21, 531 – 543
Modelling relations between blood pressure, cardiovascular phenotype, and clinical factors using large scale imaging data. Kart T. et al, (2023), Eur Heart J Cardiovasc Imaging, 24, 1361 – 1362
Deep learning identifies cardiac coupling between mother and fetus during gestation. Alkhodari M. et al, (2022), Front Cardiovasc Med, 9
Convolutional and recurrent neural networks for the detection of valvular heart diseases in phonocardiogram recordings. Alkhodari M. and Fraiwan L., (2021), Comput Methods Programs Biomed, 200
Circadian assessment of heart failure using explainable deep learning and novel multi-parameter polar images. Alkhodari M. et al, (2024), Comput Methods Programs Biomed, 248
Identification of Congenital Valvular Murmurs in Young Patients Using Deep Learning-Based Attention Transformers and Phonocardiograms. Alkhodari M. et al, (2024), IEEE J Biomed Health Inform, 28, 1803 – 1814
Dr Abhirup Banerjee - Academic
Alhussein G, Alkhodari M, Alfalahi H, Alshehhi A, Hadjileontiadis L, et al. (2025)
Alhussein G, Alkhodari M, Saleem S, Roumeliotou E, Hadjileontiadis LJ, et al. (2025)
Alhussein G, Alkhodari M, Ziogas I, Lamprou C, Khandoker AH, Hadjileontiadis LJ, et al. (2025)
Alhussein G, Alkhodari M, Saleem S, Khandoker AH, Hadjileontiadis LJ, et al. (2025)
Widatalla N, Alkhodari M, Koide K, Yoshida C, Kasahara Y, Saito M, Kimura Y, Khandoker A, et al. (2025)
Almadani M, Alkhodari M, Ghosh S, Hadjileontiadis L, Khandoker A, et al. (2024)
Alkhodari M, Moussa M, Dhou S, et al. (2024)
Dhou S, Alhusari K, Alkhodari M, et al. (2024)
Alkhodari M, Hadjileontiadis LJ, Khandoker AH, et al. (2024)
Alkhodari M, Khandoker AH, Jelinek HF, Karlas A, Soulaidopoulos S, Arsenos P, Doundoulakis I, Gatzoulis KA, Tsioufis K, Hadjileontiadis LJ, et al. (2024)