A cohort of young researchers from the Council for Scientific and Industrial Research (CSIR) has recently unveiled Fourth Industrial Revolution (4IR) pioneering technologies aimed at improving the country’s healthcare system in remote rural areas.
Machine learning technology
To address the challenge of limited diagnostic resources in the rural parts of the country, the CSIR is developing a machine learning-powered diagnostics system. This include machine learning algorithms to help medical professional diagnose diseases with better accuracy and speed. Machine learning is a branch of AI technologies that aims to mitigate the potential errors made mostly by inexperienced medical professionals. It also seeks to expedite the diagnosis of diseases, which is often delayed because traditional treatment approaches rely on human involvement. It reduces the spread of infectious diseases by delivering precise and swift disease diagnoses.
Cutting-edge technology to detect TB
PhD candidate Nkgaphe Tsebesebe, explained further: “The technology can be used in busy medical centres that handle many patient samples each day. With this technology, the diagnostic process can be accelerated, reducing patients’ waiting time. It can diagnose thousands or even millions of samples in just a few seconds, which is particularly helpful in preventing the spread of viral and infectious diseases.”
Sipho Chauke, another PhD candidate, spoke about the optical-based biosensor technology, which is used to detect Mycobacterium tuberculosis (TB). It is a miniaturisedpoint-of-care device that utilises light to detect TB bacteria in samples containing nucleic acid, Chauke explained. He said its primary objective is to assist healthcare systems in remote rural areas by facilitating the diagnosis of TB and streamlining the initiation and administration of treatment for patients. Furthermore, it significantly reduces the diagnostic time required for TB cases, makes TB diagnostic affordable, and offer large-scale diagnostics of various diseases.
“End TB strategy”
Perhaps more importantly, the technology contributes to the World Health Organisation’s “End TB strategy”, which aims to eradicate TB by 2025. The technology offers access to other medical technologies that can enable the diagnosis of TB in a cost-effective way for ordinary South Africans. Furthermore, by making TB diagnosis easily available, the technology will also enable early detection and thus lead to early treatment initiation, prevention or control of the spread of TB and a reduction in the number of multidrug-resistant TB cases.
Improving early detection of TB
Chauke said even though molecular tests are available to detect and diagnose TB, these take longer and are often expensive. In addition, he said, there are no point-of-care tests commercially available locally to ease the burden of using molecular tests and the costs associated with running them. Furthermore, added Chauke, this technology will assist ordinary South Africans by improving early clinical prognosis and treatment initiation for TB, and thus decrease the rate of transmission and spread of TB between people, especially in rural areas.
Fast and reliable prediction
Major changes in the virus genome of SARS-CoV-2 and HIV-1 have fuelled the need for the fast and reliable prediction of emerging mutations in managing the disease. The CSIR has developed Localised Surface Plasmon Resonance system to use optical biosensors to analyse biological elements such as nucleic acids, protein, antibodies and cells without interfering with the molecules in the solution. Its low complexity optics and ability to excite unpolarised light make it ideal for point-of-care device development. In a point-of-care setting, this system eliminates the need for timeous laboratory testing for diagnostic purposes.
Laser-driven diagnostic technique
Yet another student, Phumlani Mcoyi said: “With a growing interest in laser-based techniques for point-of-care diagnostics, mutation detection will guide the development of the point-of-care diagnostic system, which will be of particular interest to the most disadvantaged South African communities. The availability of a simple, fast and reliable laser-driven diagnostic technique will reduce the time and costs involved in mutation detection in the health sector.”
Creating seamless connectivity
The machine learning diagnostics and the optical-based biosensor technology that detect Mycobacterium TB utilise IoMT and AI to connect multiple machines, such as X-ray scanners, between different medical facilities and mobile clinics. This creates a seamless connectivity that allows patients to be scanned and scanned images to be transmitted to a centralised database. With the aid of the AI algorithms to perform diagnoses, the results are then send back to the facility or directly to the patient, using their preferred method of communication.
For more information, watch the video: https://youtu.be/vKvc1oVxAno