Fight against Tuberculosis and Infectious Diseases with Artificial Intelligence
About Us | Contact Us | E-Paper
Title :    Text :    Source : 

Fight against Tuberculosis and Infectious Diseases with Artificial Intelligence

AI technology can be used in predicting diseases outbreak and tracking spread of infectious diseases by analysing data from multiple sources

Post by RK News on Saturday, December 31, 2022

First slide



Artificial intelligence (AI) has enormous potential to revolutionize the field of public health by improving efficacy and effectiveness of health care services and helping to address some of the pressing challenges facing the sector. From analysing medical records, improving diagnoses and clinical care, developing personalised treatment plans to predict disease outbreaks, and identifying trends in the population, AI is being used in a variety of ways to improve public health outcome.


AI technology can be used in predicting diseases outbreak and tracking spread of infectious diseases by analysing data from multiple sources.  Factors such as travel patterns and social media activity, electronic medical records are used in preventing the disease outbreaks. AI algorithms can identify early warning signs of the outbreak and alert authorities to take preventive measures.


Another promising application of AI in infectious diseases control is in the development of new diagnostic modalities, treatments, and therapies. AI can be used to analyse vast amount of data on genetics and biochemistry of infectious agents which can facilitate researchers in identifying potential targets for new drugs and therapies. The development of new treatments is especially important in the face of emerging diseases for which effective treatment is not available. As an example, for EbolaVirus, AI has been used by the researchers to identify a protein that is essential for the virus to replicate. This protein can potentially be the target for the drug to stop spread of the virus and save lives.


AI has emerged as a research and public health tool in the response to the COVID-19 pandemic. The prediction model that integrates the clinical symptoms, the lung lesion volume, and the radiomics features of patients with COVID-19, resulting in a new model to predict the severity of COVID-19. For disease detection and diagnosis, AI tools such as machine learning algorithms can be used to accurately diagnose infectious diseases. These tools can analyse medical images, such as X-rays and CT scan to identify signs and severity of infectious diseases.


Tuberculosis (TB) infects ten million people per year and disproportionately affects people in low-to-middle-income countries. Diagnosing TB early is difficult because its symptoms can mimic those of common respiratory diseases. The weakest link in fight against TB is reinforcing diagnosis. WHO through its “End TB Strategy” aims to end global TB epidemic in 2030, and the efforts are underway to dramatically reduce the incidence of Tuberculosis in the coming decade. India is committed to make India TB Free by2025 five years ahead of Global target, and to achieve this ambitious target, Government of India through National Strategic Plan (2017-2025) is planning to work towards elimination of TB.




Countries with a significant TB burden apply diagnostic services by sputum smear microscopy and GeneXpert MTB-RIF test (GXP). However,  low sensitivity of the smear test and  high costs of  GXP test have led  World Health Organization (WHO) to recommend  use of digital chest X-rays and CAD for systematic TB screening. Cost effective screening, specifically chest X ray is identified as one way to improve the screening process, however radiologists are not always available to interpret results. To overcome this challenge, WHO recommended use of Computer Aided Detection (CAD) for screening and triaging.


Moreover, the challenge of diagnosing tuberculosis is multi-faceted, and demands a diagnostic solution that is affordable, quick, accurate, and easy to implement in underdeveloped regions. Utilizing artificial intelligence (AI) as a tool to diagnose tuberculosis with unprecedented speed and ease is now being used in many countries. The AI based tools are built on existing database in medical imaging to identify potential TB patients for follow up testing.


Government of India is exploring Artificial Intelligence (AI) for tackling the burden of Tuberculosis (TB) in India. The Central TB Division of Ministry of Health and Family Welfare has signed a Memorandum of Understanding (MoU) with Wadhwani Institute for Artificial Intelligence, Mumbai to explore the application of cutting-edge AI technology in its fight against the disease.


Ministry of Health and Family Welfare and Wadhwani AI will support National TB Elimination Programme in developing, piloting, and deploying AI-based solutions. It will support the programme in vulnerability and hot-spot mapping, modelling novel methods of screening and diagnostics and enabling decision support for care-givers apart from supporting the Revised National Tuberculosis Control Program (RNTCP) in adoption of other AI technologies.


AI can be used to improve TB diagnosis by using chest X rays and sputum samples. AI algorithm can be trained to analyse chest X- ray images and sputum samples and identify patterns that are indicative of TB. This will help the healthcare providers in diagnosing TB accurately and reduce the risk of false negatives.  Overall, the use of AI in TB diagnosis has a potential to significantly improve the accuracy and speed of TB diagnosis, which will ultimately lead to improved treatment outcomes and reduce transmission of the disease.


Despite many benefits of using AI in public health there are many challenges and ethical considerations that need to be addressed. The main concern is data privacy as most of the AI algorithms are dependent on the available personal health data which can be accessed by other unauthorised parties. Ensuring the security and privacy of personal health data is crucial to protect patient privacy and built trust in the use of AI in health care. Another ethical concern is bias in AI algorithms leading to biased outcomes which in turn can have serious consequences for patient care. To overcome this challenge, it is imperative to ensure that AI models are trained on diverse and representative data sets to help reduce the risk of bias and improve the accuracy of AI based health care services.



Despite these challenges, the future of AI has tremendous scope as one of the major tools in public health management and will continue to advance and become more sophisticated with the time and we will continue to see more innovative applications of the AI in the field of Public Health.


Bottom of Form


(The Author is State Tuberculosis Officer Kashmir. Email:


Latest Post