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Computer Scientist at NHS, working on healthcare solutions. In particular, I'm developing and applying state-of-the-art computer vision and deep learning approaches in the context of medical imaging. I work with large-scale, real-world data, with the goal of building intelligent systems that have real-world impact. I received MS from the University of Surrey where I worked on applications of machine learning, computer vision, and robotics. Before joining NHS, I was at the University of Surrey working on a variety of projects involving Machine/Deep learning.
An artificial intelligence-assisted low-cost portable device for the rapid detection of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is presented here. This standalone temperature-controlled device houses tubes designed for conducting reverse transcription loop-mediated isothermal amplification (RT-LAMP) assays. Moreover, the device utilises tubes illuminated by LEDs, an in-built camera, and a small onboard computer with automated image acquisition and processing algorithms. This intelligent device significantly reduces the normal assay run time and removes the subjectivity associated with operator interpretation of colourimetric RT-LAMP results. To further improve this device’s usability, a mobile app has been integrated into the system to control the LAMP assay environment and to visually display the assay results by connecting the device to a smartphone via Bluetooth. This study was undertaken using ~5000 images produced from the ~200 LAMP amplification assays using the prototype device. Synthetic RNA and a small panel of positive and negative SARS-CoV-2 patient samples were assayed for this study. State-of-the-art image processing and artificial intelligence algorithms were applied to these images to analyse them and to select the most efficient algorithm. The template matching algorithm for image extraction and MobileNet CNN architecture for classification results provided 98.0% accuracy with an average run time of 20 min to confirm the endpoint result. Two working points were chosen based on the best compromise between sensitivity and specificity. The high sensitivity point has a sensitivity value of 99.12% and specificity value of 70.8%, while at the high specificity point, the sensitivity is 96.05% and specificity 93.59%. Furthermore, this device provides an efficient and cost-effective platform for non-health professionals to detect not only SARS-CoV-2 but also other pathogens in resource-limited laboratories, factories, airports, schools, universities, and homes.
Mukunthan Tharmakulasingam; Nouman S. Chaudhry; Anil Fernando; Manoharanehru Branavan; Wamadeva Balachandran; Aurore C. Poirier; Mohammed A. Rohaim; Muhammad Munir; Roberto M. La Ragione. An Artificial Intelligence-Assisted Portable Low-Cost Device for the Rapid Detection of SARS-CoV-2. Electronics 2021, 10, 2065 .
AMA StyleMukunthan Tharmakulasingam, Nouman S. Chaudhry, Anil Fernando, Manoharanehru Branavan, Wamadeva Balachandran, Aurore C. Poirier, Mohammed A. Rohaim, Muhammad Munir, Roberto M. La Ragione. An Artificial Intelligence-Assisted Portable Low-Cost Device for the Rapid Detection of SARS-CoV-2. Electronics. 2021; 10 (17):2065.
Chicago/Turabian StyleMukunthan Tharmakulasingam; Nouman S. Chaudhry; Anil Fernando; Manoharanehru Branavan; Wamadeva Balachandran; Aurore C. Poirier; Mohammed A. Rohaim; Muhammad Munir; Roberto M. La Ragione. 2021. "An Artificial Intelligence-Assisted Portable Low-Cost Device for the Rapid Detection of SARS-CoV-2." Electronics 10, no. 17: 2065.
Until vaccines and effective therapeutics become available, the practical solution to transit safely out of the current coronavirus disease 19 (CoVID-19) lockdown may include the implementation of an effective testing, tracing and tracking system. However, this requires a reliable and clinically validated diagnostic platform for the sensitive and specific identification of SARS-CoV-2. Here, we report on the development of a de novo, high-resolution and comparative genomics guided reverse-transcribed loop-mediated isothermal amplification (LAMP) assay. To further enhance the assay performance and to remove any subjectivity associated with operator interpretation of results, we engineered a novel hand-held smart diagnostic device. The robust diagnostic device was further furnished with automated image acquisition and processing algorithms and the collated data was processed through artificial intelligence (AI) pipelines to further reduce the assay run time and the subjectivity of the colorimetric LAMP detection. This advanced AI algorithm-implemented LAMP (ai-LAMP) assay, targeting the RNA-dependent RNA polymerase gene, showed high analytical sensitivity and specificity for SARS-CoV-2. A total of ~200 coronavirus disease (CoVID-19)-suspected NHS patient samples were tested using the platform and it was shown to be reliable, highly specific and significantly more sensitive than the current gold standard qRT-PCR. Therefore, this system could provide an efficient and cost-effective platform to detect SARS-CoV-2 in resource-limited laboratories.
Mohammed Rohaim; Emily Clayton; Irem Sahin; Julianne Vilela; Manar Khalifa; Mohammad Al-Natour; Mahmoud Bayoumi; Aurore Poirier; Manoharanehru Branavan; Mukunthan Tharmakulasingam; Nouman Chaudhry; Ravinder Sodi; Amy Brown; Peter Burkhart; Wendy Hacking; Judy Botham; Joe Boyce; Hayley Wilkinson; Craig Williams; Jayde Whittingham-Dowd; Elisabeth Shaw; Matt Hodges; Lisa Butler; Michelle Bates; Roberto La Ragione; Wamadeva Balachandran; Anil Fernando; Muhammad Munir. Artificial Intelligence-Assisted Loop Mediated Isothermal Amplification (ai-LAMP) for Rapid Detection of SARS-CoV-2. Viruses 2020, 12, 972 .
AMA StyleMohammed Rohaim, Emily Clayton, Irem Sahin, Julianne Vilela, Manar Khalifa, Mohammad Al-Natour, Mahmoud Bayoumi, Aurore Poirier, Manoharanehru Branavan, Mukunthan Tharmakulasingam, Nouman Chaudhry, Ravinder Sodi, Amy Brown, Peter Burkhart, Wendy Hacking, Judy Botham, Joe Boyce, Hayley Wilkinson, Craig Williams, Jayde Whittingham-Dowd, Elisabeth Shaw, Matt Hodges, Lisa Butler, Michelle Bates, Roberto La Ragione, Wamadeva Balachandran, Anil Fernando, Muhammad Munir. Artificial Intelligence-Assisted Loop Mediated Isothermal Amplification (ai-LAMP) for Rapid Detection of SARS-CoV-2. Viruses. 2020; 12 (9):972.
Chicago/Turabian StyleMohammed Rohaim; Emily Clayton; Irem Sahin; Julianne Vilela; Manar Khalifa; Mohammad Al-Natour; Mahmoud Bayoumi; Aurore Poirier; Manoharanehru Branavan; Mukunthan Tharmakulasingam; Nouman Chaudhry; Ravinder Sodi; Amy Brown; Peter Burkhart; Wendy Hacking; Judy Botham; Joe Boyce; Hayley Wilkinson; Craig Williams; Jayde Whittingham-Dowd; Elisabeth Shaw; Matt Hodges; Lisa Butler; Michelle Bates; Roberto La Ragione; Wamadeva Balachandran; Anil Fernando; Muhammad Munir. 2020. "Artificial Intelligence-Assisted Loop Mediated Isothermal Amplification (ai-LAMP) for Rapid Detection of SARS-CoV-2." Viruses 12, no. 9: 972.
Until vaccines and effective therapeutics become available, the practical way to transit safely out of the current lockdown may include the implementation of an effective testing, tracing and tracking system. However, this requires a reliable and clinically validated diagnostic platform for the sensitive and specific identification of SARS-CoV-2. Here, we report on the development of a de novo, high-resolution and comparative genomics guided reverse-transcribed loop-mediated isothermal amplification (LAMP) assay. To further enhance the assay performance and to remove any subjectivity associated with operator interpretation of result, we engineered a novel hand-held smart diagnostic device. The robust diagnostic device was further furnished with automated image acquisition and processing algorithms, and the collated data was processed through artificial intelligence (AI) pipelines to further reduce the assay run time and the subjectivity of the colorimetric LAMP detection. This advanced AI algorithm-implemented LAMP (ai-LAMP) assay, targeting the RNA-dependent RNA polymerase gene, showed high analytical sensitivity and specificity for SARS-CoV-2. A total of ∼200 coronavirus disease (CoVID-19)-suspected patient samples were tested using the platform and it was shown to be reliable, highly specific and significantly more sensitive than the current gold standard qRT-PCR. The system could provide an efficient and cost-effective platform to detect SARS-CoV-2 in resource-limited laboratories.
Mohammed A Rohaim; Emily Clayton; Irem Sahin; Julianne Vilela; Manar E Khalifa; Mohammed Q Al-Natour; Mahmoud Bayoumi; Aurore Poirier; Manoharanehru Branavan; Mukunthan Tharmakulasingam; Nouman S Chaudhry; Ravinder Sodi; Amy Brown; Peter Burkhart; Wendy Hacking; Judy Botham; Joe Boyce; Hayley Wilkinson; Craig Williams; Michelle Bates; Roberto La Ragione; Wamadeva Balachandran; Anil Fernando; Muhammad Munir. Artificial Intelligence-Assisted Loop Mediated Isothermal Amplification (ai-LAMP) for Rapid and Reliable Detection of SARS-CoV-2. 2020, 1 .
AMA StyleMohammed A Rohaim, Emily Clayton, Irem Sahin, Julianne Vilela, Manar E Khalifa, Mohammed Q Al-Natour, Mahmoud Bayoumi, Aurore Poirier, Manoharanehru Branavan, Mukunthan Tharmakulasingam, Nouman S Chaudhry, Ravinder Sodi, Amy Brown, Peter Burkhart, Wendy Hacking, Judy Botham, Joe Boyce, Hayley Wilkinson, Craig Williams, Michelle Bates, Roberto La Ragione, Wamadeva Balachandran, Anil Fernando, Muhammad Munir. Artificial Intelligence-Assisted Loop Mediated Isothermal Amplification (ai-LAMP) for Rapid and Reliable Detection of SARS-CoV-2. . 2020; ():1.
Chicago/Turabian StyleMohammed A Rohaim; Emily Clayton; Irem Sahin; Julianne Vilela; Manar E Khalifa; Mohammed Q Al-Natour; Mahmoud Bayoumi; Aurore Poirier; Manoharanehru Branavan; Mukunthan Tharmakulasingam; Nouman S Chaudhry; Ravinder Sodi; Amy Brown; Peter Burkhart; Wendy Hacking; Judy Botham; Joe Boyce; Hayley Wilkinson; Craig Williams; Michelle Bates; Roberto La Ragione; Wamadeva Balachandran; Anil Fernando; Muhammad Munir. 2020. "Artificial Intelligence-Assisted Loop Mediated Isothermal Amplification (ai-LAMP) for Rapid and Reliable Detection of SARS-CoV-2." , no. : 1.