Contact information

Email
mohanad.alkhodari@rdm.ox.ac.uk

Website
Google Scholar

ORCID
0000-0002-5248-6327

Research groups

Leeson Group: Preventive Cardiology Research Group

Websites

Colleges

Wolfson College

Mohanad Alkhodari

BSc, MSc

DPhil Student

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, Alkhodari designed and developed HyTwin, an AI-assisted prototype software that integrates his research algorithms to enable efficient clinical implementation. Alongside the team, Alkhodari'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, Alkhodari 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. Alkhodari 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.

Recent publications

Correction: Enhancing CCTA image quality: a review of deep learning approaches for advanced artifact correction and denoising (Artificial Intelligence Review, (2025), 58, 10, (331), 10.1007/s10462-025-11311-w)

Alkhodari M. et al, (2025), Artificial Intelligence Review, 58

Emotional Climate Recognition in Speech-Based Conversations: Leveraging Deep Bispectral Image Analysis and Affect Dynamics

Alhussein G. et al, (2025), Expert Systems, 42

Enhancing CCTA image quality: a review of deep learning approaches for advanced artifact correction and denoising

Alkhodari M. et al, (2025), Artificial Intelligence Review, 58

Exploring emotional climate recognition in peer conversations through bispectral features and affect dynamics.

Alhussein G. et al, (2025), Comput Methods Programs Biomed, 265

Extraction of fetal heartbeat locations in abdominal phonocardiograms using deep attention transformer.

Almadani MM. et al, (2025), Comput Biol Med, 189

Emotional Climate Recognition in Speech-based Conversations: Leveraging Deep Bispectral Image Analysis and Affect Dynamics

Alhussein G. et al, (2025)

Oscillatory components of bidirectional cardio-respiratory coupling in depression and suicidal ideation: insights from swarm decomposition and entropy analysis.

Jelinek HF. et al, (2025), Front Netw Physiol, 5

Pattern-based assessment of the association of fetal heart variability with fetal development and maternal heart rate variability

Widatalla N. et al, (2025), IEEE Access

Novel Speech-Based Emotion Climate Recognition in Peers' Conversations Incorporating Affect Dynamics and Temporal Convolutional Neural Networks

Alhussein G. et al, (2025), IEEE Access, 13, 16752 - 16769

Machine Learning Identifies the Emotion Climate During Naturalistic Conversations Using Speech Features and Affect Dynamics

Alhussein G. et al, (2025), Human Behavior and Emerging Technologies, 2025

Multi-Organ Phenotypes of Offspring Born Following Hypertensive Disorders of Pregnancy: A Systematic Review.

Sattwika PD. et al, (2024), J Am Heart Assoc, 13

EmoNet: Deep Learning-based Emotion Climate Recognition Using Peers' Conversational Speech, Affect Dynamics, and Physiological Data.

Alhussein G. et al, (2024), Annu Int Conf IEEE Eng Med Biol Soc, 2024, 1 - 4

DEEP BISPECTRAL IMAGE ANALYSIS FOR SPEECH-BASED CONVERSATIONAL EMOTIONAL CLIMATE RECOGNITION

Alhussein G. et al, (2024), ACM International Conference Proceeding Series, 576 - 581

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

Temporal patterns of pre- and post-natal target organ damage associated with hypertensive pregnancy: a systematic review.

Cutler HR. et al, (2024), Eur J Prev Cardiol, 31, 77 - 99

Extraction of fetal heart beat sounds in abdominal phonocardiograms using deep attention transformer network

Almadani M. et al, (2024)

Fiber Bragg Grating Accelerometer-Based Feature Extraction for Gait Analysis

Alhussein G. et al, (2024), Proceedings - IEEE Global Communications Conference, GLOBECOM, 420 - 425

ificial intelligence in cardiovascular imaging: advances and challenges

Alkhodari M. et al, (2024), 217 - 252

Deep Learning-based Modelling of Complex Hypertensive Multi-Organ Damage with Uncertainty Quantification from Simple Clinical Measures

Kart T. et al, (2024), Proceedings - 2024 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2024, 1542 - 1547

mated Methods for Classification and Digitization of ECG Images from CVD Patients

Alkhodari M. et al, (2024), Computing in Cardiology, 51

Fiber Bragg Grating Accelerometer-Based Feature Extraction for Gait Analysis

Alhussein G. et al, (2024), Proceedings - IEEE Global Communications Conference, GLOBECOM, 420 - 425

Prediction of fetal RR intervals from maternal factors using machine learning models.

Widatalla N. et al, (2023), Sci Rep, 13

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

Emotional Climate Recognition in Conversations using Peers' Speech-based Bispectral Features and Affect Dynamics.

Alhussein G. et al, (2023), Annu Int Conf IEEE Eng Med Biol Soc, 2023, 1 - 5

Recognition of Pediatric Congenital Heart Diseases by Using Phonocardiogram Signals and Transformer-Based Neural Networks.

Hassanuzzaman M. et al, (2023), Annu Int Conf IEEE Eng Med Biol Soc, 2023, 1 - 4

Heart Failure Assessment Using Multiparameter Polar Representations and Deep Learning.

Alkhodari M. et al, (2023), Annu Int Conf IEEE Eng Med Biol Soc, 2023, 1 - 4

Investigating the effects of beta-blockers on circadian heart rhythm using heart rate variability in ischemic heart disease with preserved ejection fraction.

Saleem S. et al, (2023), Sci Rep, 13

Novel Speech-Based Emotion Climate Recognition in Peers’ Conversations Incorporating Affect Dynamics and Temporal Convolutional Neural Networks

Alhussein G. et al, (2023)

Novel Speech-Based Emotion Climate Recognition in Peers’ Conversations Incorporating Affect Dynamics and Temporal Convolutional Neural Networks

Alhussein G. et al, (2023)

Investigating automated regression models for estimating left ventricular ejection fraction levels in heart failure patients using circadian ECG features.

Al Younis SM. et al, (2023), PLoS One, 18

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

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

Random Forest and Attention-Based Networks in Quantifying Neurological Recovery

Moussa M. et al, (2023), Computing in Cardiology

FHSU-NETR: Transformer-Based Deep Learning Model for the Detection of Fetal Heart Sounds in Phonocardiography

Almadani M. et al, (2023), Computing in Cardiology

Fhsu-Net: Deep Learning-Based Model for the Extraction of Fetal Heart Sounds in Abdominal Phonocardiography

Alkhodari M. et al, (2023), IEEE International Workshop on Machine Learning for Signal Processing, MLSP, 2023-September

Deep Bispectral Analysis of Conversational Speech Towards Emotional Climate Recognition

Alhussein G. et al, (2023), 5th IEEE International Conference on Artificial Intelligence in Engineering and Technology, IICAIET 2023, 170 - 175

MULTI-ORGAN PHENOTYPES OF OFFSPRING BORN FOLLOWING HYPERTENSIVE DISORDERS OF PREGNANCY: A SYSTEMATIC REVIEW

Sattwika PD. et al, (2023), JOURNAL OF HUMAN HYPERTENSION, 37, 1 - 2

TEMPORAL PHENOTYPES OF TARGET ORGAN DAMAGE ASSOCIATED WITH HYPERTENSIVE PREGNANCIES: AN EVIDENCE SYNTHESIS WITHOUT META-ANALYSIS

Cutler H. et al, (2023), JOURNAL OF HUMAN HYPERTENSION, 37, 17 - 18

XDEEPPOLAR: A NEW CLINICAL TOOL FOR MULTIPARAMETER DATA INTEGRATION TO ASSESS LEFT VENTRICULAR EJECTION FRACTION

Alkhodari M. et al, (2023), MEDICINE, 102, 6 - 7

wo-step pre-processing tool to remove Gaussian and ectopic noise for heart rate variability analysis.

Saleem S. et al, (2022), Sci Rep, 12

Machine Learning for Screening Microvascular Complications in Type 2 Diabetic Patients Using Demographic, Clinical, and Laboratory Profiles.

Rashid M. et al, (2022), J Clin Med, 11

Using prior information to enhance microwave tomography images in bone health assessment.

Alkhodari M. et al, (2022), Biomed Eng Online, 21

Fluoroscopic 3D Image Generation from Patient-Specific PCA Motion Models Derived from 4D-CBCT Patient Datasets: A Feasibility Study.

Dhou S. et al, (2022), J Imaging, 8

Recognition of pulmonary diseases from lung sounds using convolutional neural networks and long short-term memory.

Fraiwan M. et al, (2022), J Ambient Intell Humaniz Comput, 13, 4759 - 4771

Detection of COVID-19 in smartphone-based breathing recordings: A pre-screening deep learning tool.

Alkhodari M. and Khandoker AH., (2022), PLoS One, 17

Similarities between maternal and fetal RR interval tachograms and their association with fetal development.

Widatalla N. et al, (2022), Front Physiol, 13

Deep learning identifies cardiac coupling between mother and fetus during gestation.

Alkhodari M. et al, (2022), Front Cardiovasc Med, 9

Ensemble Transformer-Based Neural Networks Detect Heart Murmur in Phonocardiogram Recordings

Alkhodari M. et al, (2022), Computing in Cardiology, 2022-September

Emotional Climate Recognition in Interactive Conversational Speech Using Deep Learning

Alhussein G. et al, (2022), Proceedings - 2022 IEEE International Conference on Digital Health, ICDH 2022, 96 - 103

ssociation between maternal-fetal cardiac coupling strengths with maternal and fetal parameters

Alkhodari M. et al, (2022), 2022 12th Conference of the European Study Group on Cardiovascular Oscillations, ESGCO 2022

Effects of Beta-Blocker on Heart Rate Variability in Heart Failure with Preserved Ejection Fraction

Saleem S. et al, (2022), Computing in Cardiology, 2022-September

Monitoring Bone Density Using Microwave Tomography of Human Legs: A Numerical Feasibility Study.

Alkhodari M. et al, (2021), Sensors (Basel), 21

Image Classification in Microwave Tomography using a Parametric Intensity Model

Alkhodari M. et al, (2021), ICCSPA 2020 - 4th International Conference on Communications, Signal Processing, and their Applications, 2021-January

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

Estimating Left Ventricle Ejection Fraction Levels Using Circadian Heart Rate Variability Features and Support Vector Regression Models.

Alkhodari M. et al, (2021), IEEE J Biomed Health Inform, 25, 746 - 754

Deep Learning Predicts Heart Failure With Preserved, Mid-Range, and Reduced Left Ventricular Ejection Fraction From Patient Clinical Profiles.

Alkhodari M. et al, (2021), Front Cardiovasc Med, 8

numerical feasibility study on monitoring bone health using microwave tomography: towards a wearable design

Alkhodari M. et al, (2021)

Fluoroscopic 3D Image Generation from Patient-Specific PCA Motion Models Derived from 4D-CBCT Patient Datasets: A Feasibility Study

Dhou S. et al, (2021)

Machine Learning for Screening Microvascular Complications in Type-2 Diabetic Patients using Demographic, Clinical, and Laboratory Profiles

Rashid M. et al, (2021)

Detection of COVID-19 in smartphone-based breathing recordings using CNN-BiLSTM: a pre-screening deep learning tool

Alkhodari M. and Khandoker A., (2021)

matic identification of respiratory diseases from stethoscopic lung sound signals using ensemble classifiers

Fraiwan L. et al, (2021), Biocybernetics and Biomedical Engineering, 41, 1 - 14

Revisiting Left Ventricular Ejection Fraction Levels: A Circadian Heart Rate Variability-Based Approach

Alkhodari M. et al, (2021), IEEE Access, 9, 130111 - 130126

Prediction of LVEF using BiLSTM and Swarm Decomposition-based 24-h HRV Components

Alkhodari M. et al, (2021), Proceedings - 2021 14th International Congress on Image and Signal Processing, BioMedical Engineering and Informatics, CISP-BMEI 2021

Swarm Decomposition Enhances the Discrimination of Cardiac Arrhythmias in Varied-Lead ECG Using ResNet-BiLSTM Network Activations

Alkhodari M. et al, (2021), Computing in Cardiology, 2021-September

Screening Cardiovascular Autonomic Neuropathy in Diabetic Patients with Microvascular Complications Using Machine Learning: A 24-Hour Heart Rate Variability Study

Alkhodari M. et al, (2021), IEEE Access, 9, 119171 - 119187

matic identification of respiratory diseases from stethoscopic lung sound signals using ensemble classifiers

Fraiwan L. et al, (2021), BIOCYBERNETICS AND BIOMEDICAL ENGINEERING, 41, 1 - 14

Swarm Decomposition Enhances the Discrimination of Cardiac Arrhythmias in Varied-Lead ECG Using ResNet-BiLSTM Network Activations

Alkhodari M. et al, (2021), 2021 COMPUTING IN CARDIOLOGY (CINC)

Comparative Study of Meningioma Tumors Segmentation Methods from MR Images

Alkhodari M. et al, (2020), 2020 3rd International Conference on Signal Processing and Information Security, ICSPIS 2020

Identification of Cardiac Arrhythmias from 12-lead ECG Using Beat-wise Analysis and a Combination of CNN and LSTM

Alkhodari M. et al, (2020), Computing in Cardiology, 2020-September

Investigating Circadian Heart Rate Variability in Coronary Artery Disease Patients with Various Degrees of Left Ventricle Ejection Fraction.

Alkhodari M. et al, (2020), Annu Int Conf IEEE Eng Med Biol Soc, 2020, 714 - 717

Discrimination Amongst Various Degrees of Left Ventricular Ejection Fraction in CAD Patients Using Circadian Heart Rate Variability Features

Alkhodari M. et al, (2020), 2020 11th Conference of the European Study Group on Cardiovascular Oscillations: Computation and Modelling in Physiology: New Challenges and Opportunities, ESGCO 2020

Neonatal sleep stage identification using long short-term memory learning system.

Fraiwan L. and Alkhodari M., (2020), Med Biol Eng Comput, 58, 1383 - 1391

Predicting hypertensive patients with higher risk of developing vascular events using heart rate variability and machine learning

Alkhodari M. et al, (2020), IEEE Access, 8, 192727 - 192739

Classification of Focal and Non-Focal Epileptic Patients Using Single Channel EEG and Long Short-Term Memory Learning System

Fraiwan L. and Alkhodari M., (2020), IEEE Access, 8, 77255 - 77262

Investigating the use of uni-directional and bi-directional long short-term memory models for automatic sleep stage scoring

Fraiwan L. and Alkhodari M., (2020), Informatics in Medicine Unlocked, 20