Mohanad Alkhodari BSc, MSc

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

• RDM DPhil student listed in Forbes Middle East’s 30 under 30 list
• Forbes Magazine 30 under 30 Middle East
• MIT Technology Review Magazine Innovators Under 35 MENA
• BHF-CRE Symposium Image Competition – Judges’ Choice

1. Deep bispectral image analysis for speech-based conversational emotional climate recognition Alhussein G,  Alkhodari M,  Alfalahi H,  Alshehhi A,  Hadjileontiadis L,  et al. (2025)
2. EmoNet: Deep Learning-based Emotion Climate Recognition Using Peers' Conversational Speech, Affect Dynamics, and Physiological Data. Alhussein G,  Alkhodari M,  Saleem S,  Roumeliotou E,  Hadjileontiadis LJ,  et al. (2025)
3. Exploring emotional climate recognition in peer conversations through bispectral features and affect dynamics.
   Alhussein G,  Alkhodari M,  Ziogas I,  Lamprou C,  Khandoker AH,  Hadjileontiadis LJ,  et al. (2025)
4. Emotional Climate Recognition in Speech-based Conversations: Leveraging Deep Bispectral Image Analysis and Affect Dynamics
   Alhussein G,  Alkhodari M,  Saleem S,  Khandoker AH,  Hadjileontiadis LJ,  et al. (2025)
5. Pattern-based assessment of the association of fetal heart variability with fetal development and maternal heart rate variabilityWidatalla N,  Alkhodari M,  Koide K,  Yoshida C,  Kasahara Y,  Saito M,  Kimura Y,  Khandoker A,  et al. (2025)
6. Extraction of fetal heart beat sounds in abdominal phonocardiograms using deep attention transformer network
   Almadani M,  Alkhodari M,  Ghosh S,  Hadjileontiadis L,  Khandoker A,  et al. (2024)
7. Chapter 9 Artificial intelligence in cardiovascular imaging: advances and challenges
   Alkhodari M,  Moussa M,  Dhou S,  et al. (2024)
8. Chapter 4 Artificial intelligence in mammography: advances and challenges
   Dhou S,  Alhusari K,  Alkhodari M,  et al. (2024)
9. Identification of Congenital Valvular Murmurs in Young Patients Using Deep Learning-Based Attention Transformers and Phonocardiograms.
   Alkhodari M,  Hadjileontiadis LJ,  Khandoker AH,  et al. (2024)
10. Circadian assessment of heart failure using explainable deep learning and novel multi-parameter polar images.
    Alkhodari M,  Khandoker AH,  Jelinek HF,  Karlas A,  Soulaidopoulos S,  Arsenos P,  Doundoulakis I,  Gatzoulis KA,  Tsioufis K,  Hadjileontiadis LJ,  et al. (2024)
11. 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
12. 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
13. Alkhodari M. et al, (2023), Proceedings – 2023 2023 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2023, 1886 – 1889
14. 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
15. 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
16. Deep learning identifies cardiac coupling between mother and fetus during gestation. Alkhodari M. et al, (2022), Front Cardiovasc Med, 9
17. 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