Sign In

Communications of the ACM

Arab World special section: Big trends

Biomedical Computing in the Arab World: Unlocking the Potential of a Growing Research Community

View as: Print Mobile App ACM Digital Library Full Text (PDF) In the Digital Edition Share: Send by email Share on reddit Share on StumbleUpon Share on Hacker News Share on Tweeter Share on Facebook
key and map of Saudi Arabia, illustration

Credit: Andrij Borys Associates

Health challenges represent one of the long-standing issues in the Arab region that hinder its ability to develop. Prevalence of diseases such as cardiovascular diseases, liver cirrhosis and cancer among many others has contributed to the deteriorated health status across the region leading to lower life expectancy compared to other regions. For instance, the average life expectancy in the Arab world is approximately 70 years, which is at least 10 years lower than most high-income countries.2 Among many directions of healthcare development across the region, biomedical computing research represents one main arm of tackling health challenges. Advances in computational technologies have enabled the emergence of biomedical computing as one of the most influential research areas worldwide. In recent years, life sciences have witnessed an explosion in the volumes of biomedical data generated by high-throughput technologies and other sources. The enormity of volumes and interdependence in biomedical data pose great analytical challenges in the quest to infer deeply hidden knowledge buried under this complexity. As such, the biomedical computing research community in the Arab world has been actively contributing to the efforts that tackle long-standing biomedical challenges.

Research in biomedical computing in the region dates back to mid-1970s with the establishment of the Systems and Biomedical Engineering department at Cairo University in Egypt. Since then, the number of related programs has steadily increased and researchers from different disciplines have developed interest in biomedical computing applications. Despite the limited available resources, researchers from the Arab region have made over the years strong contributions to the rapid advances that occur in the field of biomedical computing. Recent successful efforts by researchers across the region have been evident in three broad areas of biomedical computing; namely, biomedical imaging, biomedical signal analysis and bioinformatics. These efforts have materialized in advancing a diverse spectrum of bio-medical computing applications, as well as stimulating clear commercial interest.

This article sheds light on notable research efforts in the Arab world in each of the aforementioned areas of biomedical computing. It also demonstrates how this research addresses healthcare issues in the region. A glimpse of the impact of research in this area in stimulating the budding culture of entrepreneurship and startup of new ventures across the region will be discussed. Finally, we introduce a vision for different avenues of development that could magnify the outcomes of the biomedical computing research community in the region.

Back to Top

Biomedical Imaging

Biomedical image analysis has always constituted a main line of bio-medical computing research in the Arab world. Medical imaging modalities became widely available in the region in response to the strong prevalence of diseases that rely on imaging techniques for accurate diagnosis. This has made the case for biomedical image analysis research compelling, and many institutions in various countries in the region recognized this opportunity. Interest in biomedical image analysis research involving machine learning techniques has also become strong, fueling research on computer aided diagnosis and other predictive analytics techniques.

Early research in this area came from Cairo University (Cairo, Egypt) with the contributions led by Abou Bakr Youssef in tissue characterization using ultrasound images since the 1980s.15 Later on, a strong interest in magnetic resonance imaging (MRI) was developed, with an early focus on K-space and later on diffusion tensor imaging.11 Ultrasound image analysis regained momentum with the work on elasticity and strain imaging. Other biomedical image analysis research that came out of Cairo University included localization of cardiac structures using MRI, identification of schizophrenic patients using functional-MRI,4 diagnosis of Alzheimer's disease using diffusion tensor images, skin lesion classification via optical images, designing insoles to address diabetic foot complications,12 lung cancer diagnosis and prognosis evaluation, optical finger print recognition, and other image-based biometric identification techniques. Intelligent segmentation of cardiac structures and evaluation of cardiac dynamics were also the research focus of two prominent research institutions in Egypt-Aswan Heart Center and Nile University.

Across the region, numerous research groups have published work in this area, utilizing various machine learning techniques. The work at the Computational Biomedical Imaging Lab at the University of Jordan (Amman, Jordan) is concerned with computer-aided diagnosis for improving tissue characterization and understanding of disease and tumor behavior.5 The lab also utilizes fractal analysis for classification of meningioma, the most common type of brain tumors, and segmentation of cardiac structures. These methods work either on a cellular level or tissue level.6 Research on segmentation of cardiac, breast, and brain images was also heavily published utilizing fuzzy logic methods (University of Mascara, Algeria), histogram-based techniques (United Arab Emirates University, Ajman, UAE), and various supervised learning techniques (King Abdelaziz University, Saudi Arabia). With the onset of the COVID-19 crisis, many researchers have also proposed methods for fast and accurate CT image segmentation, which is crucial to the diagnosis of COVID-19.25 These research efforts, among many others, demonstrate the alignment of biomedical imaging research in the region with global healthcare issues.

Back to Top

Biomedical Signal Analysis

Biomedical signal analysis has risen as one of the key research areas, given the advances in the technology of recording different physiological signals from the human body. These signals can be utilized in diagnosing various diseases as well as modulating the function of different organs.

One example is the work of the Biomedical Signal Processing research group at Khalifa University (KU, Abu Dhabi, UAE) in the area of cardiovascular disease, which represents a leading cause of death in the region as well as worldwide. One line of work of the KU team is developing noninvasive fetal phonocardiogram as well as adult Electro-cardiogram (ECG) signal processing techniques to prevent stillbirths and sudden cardiac deaths. The KU team successfully demonstrated the first proof of concept that a low-cost phonocardiogram sensor can detect fetal heart sounds and give a reliable estimation of the fetal heart rate and its variability, which were validated by simultaneously recorded fetal ECG signals.17 The work in this study resulted in a handheld fetal phonogram working prototype, which was validated in a number of healthy pregnancies. Additionally, the KU team has contributed to the worldwide research efforts to diagnose and predict cardiac arrhythmia complications. The KU team has developed a new device presenting a novel algorithm which was implemented in an application specific integrated circuit (ASIC) chip by using ECG signals to predict a heart attack long before its onset.9

Brain signal analysis represents one other notable research direction that is being pursued by multiple groups around the region. The LAST-RE group at the Lebanese University (Tripoli, Lebanon) has been working on developing novel 'neuromarkers' to identify and characterize networks associated with cognitive deficits in patients, particularly at early stage. It is recognized that neurological pathologies, such as Alzheimer's disease (AD), are caused by alterations in these brain networks. In this context, the LASTRE group investigated dynamic topological changes of AD networks in terms of brain network segregation and integration.14 In their analysis, functional brain networks were reconstructed from brain Electroencephalography (EEG) activity in different frequency bands. The achieved results revealed that networks in AD patients are characterized by less integration and higher segregation compared to networks reconstructed from healthy subjects. This could complement current AD diagnostic metrics, especially at early stages of the disease.

In addition, multiple research groups in the region have contributed to the development of a plethora of brain signal analysis techniques for a variety of applications. One study from the Biomedical Engineering group at the American University of Sharjah (AUS, Sharjah, UAE) has proposed a technique to assess the mental capacity to preserve attention for long durations.8 Their technique was able to monitor changes in the communication patterns among different brain regions with reduced attention. Another research direction that is being pursued in this area is driving brain activity through artificial stimulation, which has shown its merits in compensating for different types of disability. One example is the work of the Biomedical and Neuro Engineering Laboratory (BNEL) at Ain Shams University (ASU, Cairo, Egypt) to develop visual prostheses for restoring vision to the blind. They have proposed a novel Kalman filter-based technique to automatically tune electrical stimulation delivered through visual prostheses devices which has been shown to evoke neuronal activity similar to natural responses.13 Taken together, biomedical signal analysis research in the region has resulted in influential and diverse contributions that aim at resolving multiple technical challenges in the field and at addressing several population health issues.

Back to Top


The Arab world has a vast and versatile environment inhabited by large populations having interesting structure and dynamics. This has led to multiple bioinformatics research efforts that employ high-performance computational methods to tackle hereditary diseases prevalent in the region.

Biomedical signal analysis has risen as one of the key research areas, given the advances in the technology of recording different physiological signals from the human body.

There have been multiple efforts around the region to develop national genome programs. One prominent example is the Saudi Human Genome Program, which has the capacity of 100,000 samples. The project focuses on unraveling the mutations responsible for inherited disorders in the Saudi/Arab population. The project, which was started in 2014, is recognized as one of the top 10 human genome projects worldwide and is conducted by the King Abdulaziz City for Science and Technology (KACST, Riyadh, Saudi Arabia).24 The findings of the project have paved the way for the implementation of precision medicine in Saudi Arabia. The project could identify many new mutations related to disease. It has also reclassified, as benign, many variants, previously thought to be responsible for disease because of limited data.3 King Faisal Specialist Hospital and Research Center (KFSHRC, Riyadh, Saudi Arabia) has launched the Clinical Genomics transformation project, which leverages genomic testing for clinical practice. It has already contributed to the successful diagnosis and treatment of thousands of patients with inherited disorders as well as cancer.20 The Qatar genome project, which is one of the early projects in the region, was launched to study and isolate genetic characteristics of the local population. The first phase was completed successfully, with the analysis of 20,000 genomes.23 The current phase of the project targets 10% of the population, and subsequent phases have an ambitious goal of completing the analysis of 350,000 genomes. The Emirati project has also completed the characterization of 1,000 individual genomes,7 with aspirations to eventually cover the entire population of the country. Very recently, Egypt also announced the starting of the Egyptian Genome Program targeting 100,000 samples, in addition to a number of studies to analyze cohorts of patients. Although of limited scale, these studies shed light on the genetic bases of certain diseases in the Egyptian population.22 North African Arab countries Tunisia, Morocco, Sudan, and Egypt have also joined the African genome project H3Africa, which is an Africa-wide initiative aiming at studying genetic variations in the continent. Genetic studies for population characterization and disease studies have also been conducted by researchers from Lebanon, Tunisia, Kuwait, and Bahrain.1 Researchers from Qatar Computing Research Institute (QCRI) have demonstrated the use of machine learning techniques in characterizing molecular interactions of cancer subtypes.19 In addition to medical research, there was also a number of genome projects of regional interest that were successfully completed. These included the Date Palm genome projects in Saudi Arabia and Qatar, and the Egyptian Buffalo genome projects. These projects serve as a foundation for improving the traits of these plants and animals to optimize food production.

There have been multiple efforts around the region to develop national genome programs.

Recognizing the importance of this research area, the Computational Bioscience Research Center (CBRC) was established at the inception of King Abdullah University of Science and Technology (KAUST, Thuwal, Saudi Arabia) in 2009. The interdisciplinary nature of research requires designs of integrated computational and experimental methods and tools for use in life sciences and biotechnology development. The computational and experimental facilities at KAUST enables CBRC to achieve such a synergistic approach. In collaboration with local health care providers, CBRC research is particularly focused on population and comparative genetics and genome-wide association studies. One research thrust at CBRC is in the field of metagenomics, which is useful in many applications, such as soil management, bioprospecting for industrially relevant compounds, detection of novel genes and proteins, medical diagnostics and epidemiology, oil industry and many others. In particular, CBRC utilizes metagenomics for bio-prospecting of cellulase for exploring novel enzymatic genes in the Red Sea, including those for bio-fuel development.10 CBRC also conducts research in structural biology; proteins structure analysis, prediction, and engineering; and cellular signaling. A related work aims for the development of computational tools and resources for designing efficient microbial cell factories.21 These are microorganisms whose metabolic processes are altered, for example through gene editing methods, in such a way that they increase production of chemical compounds that may be of industrial or pharmaceutical interest. These examples show the contributions of bioinformatics researchers in the region to enhance the quality of life of millions of people around the region.

Back to Top

Entrepreneurial Activities

Built upon the success demonstrated in different biomedical computing tracks, the Arab region has witnessed in the past few years a strong momentum for entrepreneurial activities in many sectors. While biomedical computing research in the region did not lean toward the translational side that transforms basic research outcomes to products and tools, we have witnessed a few cases of patents resulting from research and fueling the birth or the growth of startups. For example, the work of the Bio-medical Signal Processing research group at Khalifa University raised a significant commercial interest that resulted in a UAE-based startup company licensed to commercialize a phonogram technology for home monitoring of fetal well-being.16 Another example is in the field of smart radiology and ultrasound imaging analysis which materialized into an Egypt-based startup.18 Moreover, several graduates of biomedical engineering programs, medical schools, and graduates of other life science related programs have started up their own health-tech ventures. Some examples of these cases cater to radiology computer-aided diagnosis (Intexil,, smart radiology workflow (Dileny Tech,, radiology reporting (Rology,, cardiac events monitoring (BioBusiness,, remote patient monitoring (Pulse,, diabetic eye care (, bioinformatics (Proteinea,, and early diagnosis of cancer (Prognica, The emergence of these entrepreneurial activities is driven by both increased interest from investors and expanded awareness of researchers and graduates of relevant programs.

Back to Top

Regional Conferences

There are three main regular bio-medical engineering conferences in the Arab world. The first to get started was the Cairo International Biomedical Engineering Conference (CIBEC), which is held in Cairo in December every other year since 2002. The conference with the highest outreach is the Middle East Conference on Biomedical Engineering (MECBME), as it rotates among different countries in the region, including UAE, Jordan, and Tunisia. The International Conference on Advances in Biomedical Engineering (ICAMBE) is held regularly in Lebanon, and typically enjoys a high level of attendance from many Franco-Arab researchers living abroad, in addition to researchers from around the region. An important observation regarding these three conferences is the high level of coordination among their organizers in order to avoid time overlap and to increase geographic coverage. These conferences have succeeded over the years in bringing together biomedical computing researchers from across the region.

Back to Top

Conclusion and Future Perspectives

The biomedical computing research community in the Arab world has demonstrated valuable success over the years in multiple facets of research. In this article, we focused on three biomedical computing tracks: biomedical imaging, biomedical signal analysis and bioinformatics; outlining examples of outstanding research being carried out in the region in each track. In addition, we discussed the success of the biomedical computing community in the Arab World in establishing entrepreneurial activities. Yet, multiple aspects of development still need to be pursued. What the region lacks fundamentally is initiatives to build and publish datasets based on populations from countries in the region itself. In addition, cross-country collaboration is scarce (see for example Abdelhedi et al.1), although if activated it can have a positive impact on both the quality and quantity of biomedical computing research output. The regional conferences outlined in this article could represent one valuable opportunity to initiate the pursued cross-country collaborations. Another potential aspect of development is establishing animal research labs, which can provide a critically needed dimension of bio-medical computing research. Few labs have started to adopt this approach including, for example, the Biomedical and Neuro Engineering Laboratory at Ain Shams University and the Biomimetics Engineering Laboratory at the American University of Beirut (Beirut, Lebanon). However, providing the financial and logistical resources needed for the wide-scale establishment of animal research labs is still lacking. Despite all challenges facing researchers, the promising success of biomedical computing researchers within the Arab region as demonstrated in this article indicates that unlocking their full potential could significantly enhance the health and wellness of millions of people across the region as well as worldwide.

Back to Top


1. Abdelhedi, R. et al. Characterization of drug-metabolizing enzymes CYP2C9, CYP2C19 polymorphisms in Tunisian, Kuwaiti and Bahraini populations. Journal of Genetics 94 (2015), 765–770.

2. Abdelmoneium, A.O. and Alharahsheh, S.T. Family home caregivers for old persons in the Arab region: perceived challenges and policy implications. Open Journal of Social Sciences 4 (2016), 151–164.

3. Abouelhoda, M. et al. Revisiting the morbid genome of Mendelian disorders. Genome Biology 17 (2016), 235.

4. Algunaid, R.R. et al. Schizophrenic patient identification using graph-theoretic features of resting-state fMRI data. Biomedical Signal Processing and Control 43 (2018), 289–299.

5. Al-Kadi, O.S. A multiresolution clinical decision support system based on fractal model design for classification of histological brain tumours. Computerized Medical Imaging and Graphics 41 (2015), 67–79.

6. Al-Kadi, O.S. Spatio-temporal segmentation in 3D echocardiographic sequences using fractional Brownian motion. IEEE Transactions on Biomedical Engineering 67 (2020), 2286–2296.

7. AlSafar, H.S. et al. Introducing the first whole genomes of nationals from the United Arab Emirates. Scientific Reports 9 (2019), 1–15.

8. Al-Shargie, F.M. et al. EEG-based semantic vigilance level classification using directed connectivity patterns and graph theory analysis. IEEE Access 8 (2020), 115941–115956.

9. Bayasi, N. et al. Low-power ECG-based processor for predicting ventricular arrhythmia. IEEE Transactions on Very Large Scale Integration (VLSI) Systems 24 (2015), 1962–1974.

10. Behzad, H. et al. Metagenomic studies of the Red Sea. Gene 576 (2016), 717–723.

11. Gabr, R.E. et al. Deconvolution-interpolation gridding (DING): Accurate reconstruction for arbitrary k-space trajectories. Magnetic Resonance in Medicine: An Official Journal of the International Society for Magnetic Resonance in Medicine 56 (2006), 1182–1191.

12. Ibrahim, M. et al. A pilot study to assess the effectiveness of orthotic insoles on the reduction of plantar soft tissue strain. Clinical Biomechanics 28 (2013), 68–72.

13. Jawwad, A. et al. Modulating lateral geniculate nucleus neuronal firing for visual prostheses: A Kalman filter-based strategy. IEEE Transactions on Neural Systems and Rehabilitation Engineering 25 (2017), 1917–1927.

14. Kabbara, A. et al. Reduced integration and improved segregation of functional brain networks in Alzheimer's disease. Journal of Neural Engineering 15 (2018), 026023.

15. Kadah, Y.M. et al. Classification algorithms for quantitative tissue characterization of diffuse liver disease from ultrasound images. IEEE Transactions on Medical Imaging 15 (1996), 466–478.

16. Khandoker, A. Low cost fetal phonocardiogram. Google Patents, 2018.

17. Khandoker, A. et al. Validation of beat by beat fetal heart signals acquired from four-channel fetal phonocardiogram with fetal electrocardiogram in healthy late pregnancy. Scientific Reports 8 (2018), 1–11.

18. Mahmoud, A.M.E. and Ali, M.T.M. Method and apparatus to measure tissue displacement and strain. Google Patents, 2020.

19. Mall, R. et al. Detection of statistically significant network changes in complex biological networks. BMC Systems Biology 11 (2017), 32.

20. Monies, D. et al. Lessons learned from large-scale, first-tier clinical exome sequencing in a highly consanguineous population. The American Journal of Human Genetics 104 (2019), 1182–1201.

21. Motwalli, O. et al. In silico screening for candidate chassis strains of free fatty acid-producing cyanobacteria. BMC Genomics 18 (2017), 33.

22. Nassar, A. et al. Targeted next generation sequencing identifies somatic mutations in a cohort of Egyptian breast cancer patients. Journal of Advanced Research 24 (2020), 149–157.

23. Qoronfleh, M.W. et al. The future of medicine, healthcare innovation through precision medicine: Policy case study of Qatar. Life Sciences, Society and Policy 16 (2020), 1–20.

24. S.G.P. Team. The Saudi Human Genome Program: An oasis in the desert of Arab medicine is providing clues to genetic disease. IEEE Pulse 6 (2015), 22–26.

25. Zhou, L. et al. A rapid, accurate and machine-agnostic segmentation and quantification method for CT-based COVID-19 diagnosis. IEEE Transactions on Medical Imaging 39 (2020), 2638–2652.

Back to Top


Seif Eldawlatly, Computer and Systems Engineering Department, Faculty of Engineering, Ain Shams University, Cairo, Egypt, and Faculty of Media Engineering and Technology, German University in Cairo, Cairo, Egypt.

Mohamed Abouelhoda, Systems and Biomedical Engineering Department, Faculty of Engineering, Cairo University, Giza, Egypt, and, King Faisal Specialist Hospital and Research Center, and Saudi Human Genome Program, King Abdulaziz City for Science and Technology, Riyadh, Saudi Arabia.

Omar S. Al-Kadi, King Abdullah II School for Information Technology, University of Jordan, Amman, Jordan.

Takashi Gojobori, Computational Bioscience Research Center, King Abdullah University of Science and Technology, Thuwal, Saudi Arabia.

Boris Jankovic, Computational Bioscience Research Center, King Abdullah University of Science and Technology, Thuwal, Saudi Arabia.

Mohamad Khalil, Faculty of Engineering, Lebanese University, Tripoli, Lebanon.

Ahsan H. Khandoker, Healthcare Engineering Innovation Center (HEIC), Department of Biomedical Engineering, Khalifa University of Science and Technology, Abu Dhabi, United Arab Emirates.

Ahmed Morsy, Systems and Biomedical Engineering Department, Faculty of Engineering, Cairo University, Giza, Egypt.

Copyright held by authors/owners.
Request permission to (re)publish from the owner/author

The Digital Library is published by the Association for Computing Machinery. Copyright © 2021 ACM, Inc.


No entries found