The Council for Scientific and Industrial Research (CSIR) is using artificial intelligence (AI) to develop cutting-edge technologies to improve the country\u2019s rural health systems.<\/p>\n<\/p>\n
Young CSIR researchers this week displayed groundbreaking Fourth Industrial Revolution (4IR) innovations aimed at enhancing South Africa\u2019s healthcare system in remote regions.<\/p>\n<\/p>\n
To address the issue of limited diagnostic resources in rural areas, the CSIR is developing a machine learning-powered diagnostics system.<\/p>\n<\/p>\n
The technology combines machine learning algorithms to independently help medical professionals diagnose diseases with better accuracy and speed.<\/p>\n<\/p>\n
\u201cMachine learning is a branch of AI technologies that aims to mitigate the potential errors made by newly appointed medical professionals. Additionally, it seeks to expedite the diagnosis of diseases, which is often delayed because traditional treatment approaches are reliant on human involvement,\u201d according to a statement.<\/p>\n<\/p>\n
By delivering precise and swift disease diagnoses, machine learning has the potential to reduce the spread of infectious diseases.<\/p>\n<\/p>\n
PhD candidate Nkgaphe Tsebesebe said the technology could be used in busy medical centres that handle many patient samples each day.<\/p>\n<\/p>\n
\u201cWith this technology, the diagnostic process can be accelerated, reducing patients\u2019 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,\u201d Tsebesebe explained.<\/p>\n<\/p>\n
Meanwhile, another PhD candidate, Sipho Chauke, has developed an optical-based biosensor technology for the detection of Mycobacterium tuberculosis (TB).<\/p>\n<\/p>\n
It is a miniaturised point-of-care device that utilises light to detect TB bacteria in samples containing nucleic acid.<\/p>\n<\/p>\n
Its primary objective is to assist healthcare systems in remote areas by facilitating the diagnosis of TB and streamlining the initiation and administration of treatment for patients.<\/p>\n<\/p>\n
\u201cThe technology also aims to significantly reduce the diagnostic time required for TB cases, make TB diagnostic affordable and offer large-scale diagnostics of various diseases.\u201d<\/p>\n<\/p>\n
This innovation, according to the CSIR, enables the diagnosis of TB at little to no hassle to ordinary South Africans or the end user.<\/p>\n<\/p>\n
\u201cBy making TB diagnosis available to all through this technology, the aim of the \u2018End TB Strategy\u2019 can be achieved through the early detection of TB, which will result in early treatment.\u201d<\/p>\n<\/p>\n
Another invention is a CSIR-developed Localised Surface Plasmon Resonance system, which uses optical biosensors to analyse biological elements such as nucleic acids, proteins, antibodies and cells without interfering with the molecules in the solution.<\/p>\n<\/p>\n
Young scientist, Phumlani Mcoyi, said: \u201cWith 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.\u201d<\/p>\n<\/p>\n
He believes that 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. \u2013 SAnews.gov.za<\/p>\n<\/p>\n
Source: South African Government News Agency<\/p><\/p>\n","protected":false},"excerpt":{"rendered":"
The Council for Scientific and Industrial Research (CSIR) is using artificial intelligence (AI) to develop cutting-edge technologies to improve the country\u2019s rural health systems.Young CSIR researchers this week displayed groundbreaking Fourth Industri…<\/p>\n","protected":false},"author":1,"featured_media":0,"comment_status":"closed","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[14],"tags":[],"yoast_head":"\n