The Council for Scientific and Industrial Research (CSIR) is using Artificial Intelligence (AI) to develop cutting-edge technologies to improve the country’s rural health systems.
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Young researchers from the CSIR recently showcased groundbreaking Fourth Industrial Revolution(4IR) innovations aimed at improving South Africa's healthcare system in remote areas of the country.
To address the issue of limited diagnostic resources in rural areas, the CSIR is developing machine-learning-powered diagnostics systems using cutting-edge machine-learning algorithms to autonomously help medical professionals to diagnose diseases with improved accuracy and speed.
Machine learning is a branch of AI that can mitigate potential errors made by newly appointed medical professionals. In addition, it seeks to expedite the diagnosis of diseases, which is often delayed because traditional treatment approaches are reliant on human involvement. By delivering precise and swift disease diagnoses, machine learning has the potential to reduce the spread of infectious diseases.
“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 times. It can diagnose thousands of samples in just a few seconds, which is particularly helpful in preventing the spread of viral and infectious diseases,” says PhD student Nkgaphe Tsebesebe.
During the briefing, Sipho Chauke, another PhD candidate, discussed optical-based biosensor technology for the detection of Mycobacterium tuberculosis (TB), which is a miniaturised point-of-care device that uses light to detect TB bacteria in samples containing nucleic acid. Its primary objective is to assist healthcare systems in remote areas, particularly rural regions, by facilitating the diagnosis of TB and streamlining the initiation and administration of treatment for patients. The technology aims to reduce significantly diagnostic times for TB cases, make TB diagnostic affordable, and offer large-scale diagnostics of various other diseases.
The World Health Organization has an ‘End TB’ strategy that aims to eradicate TB by 2025. The CSIR-developed optical-based biosensor technology for detecting TB contributes to this strategy by offering ordinary South Africans access to medical technologies. Making TB diagnosis available to all through this technology will result in early treatment initiation, prevention, or control, of the spread of TB, and a reduction in the number of multidrug-resistant TB cases.
“Although molecular tests are available for detecting and diagnosing TB, they take several weeks to give a diagnosis and are often expensive to run. In addition, there are no local point-of-care tests commercially available to ease the burden of using molecular tests and the costs associated with running them. This technology will assist ordinary South Africans by improving early clinical prognosis and treatment initiation for TB, thereby decreasing the rate of transmission and the spread of TB between people, especially in remote settings within South Africa,” says Sipho Chauke.
Point of care solutions and the IoMT
Major changes in the virus genome of SARS-CoV-2 and HIV-1, characterised by new variants of concern and accumulative mutations resulting in drug resistance, have fuelled the need for the fast and reliable prediction of emerging mutations in managing the disease. The CSIR-developed Localised Surface Plasmon Resonance system uses 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.
“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 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,” says PhD candidate Phumlani Mcoyi.
Machine learning-powered diagnostics systems and the optical-based biosensor technology for the detection of TB use IoMT (Internet of medical things) and AI to connect multiple machines, such as X-ray scanners, between different medical facilities and mobile clinics. This connectivity allows patients to be scanned and the scanned images to be transmitted to a centralised database. These technologies use AI algorithms to perform diagnoses and send the results back to the facility or directly to the patient, using their preferred method of communication.