More than 724,000 new cases of tuberculosis (TB) were reported in Indonesia in 2022.
This number increased to 809,000 cases in 2023, significantly higher than the pre-pandemic figures when the average annual case discovery was below 600,000.
A team of researchers from Universitas Gadjah Mada (UGM) is developing artificial intelligence (AI)-based technology to support the early detection of tuberculosis.
This innovation is expected to be a solution for Indonesia, which has relied on imported technology for active TB case-finding until now.
Dr. Antonia Morita I. Saktiawati, MD, a researcher from the UGM Center for Tropical Medicine and principal investigator in the KONEKSI Project, revealed that her team is designing AI-based software called computer-aided detection (CAD).
This technology is designed to assist healthcare workers in analyzing chest X-rays, improving the effectiveness of TB screening by making it faster and more accurate.
“We actually can develop this technology ourselves, especially with such a high number of cases,” said Dr. Saktiawati on Tuesday (Mar. 25).
She added that the research has been ongoing for quite some time with limited funding, but it is now receiving support from the KONEKSI program initiated by the Australian Department of Foreign Affairs and Trade (DFAT).
Several institutions are collaborating on this research, including UGM, the University of Melbourne, Monash University Indonesia, and Universitas Sebelas Maret. Also involved are several health and advocacy organizations, such as Yayasan Pengembangan Kesehatan dan Masyarakat Papua (YPKMP) and Sentra Advokasi Perempuan, Difabel dan Anak (SAPDA).
Dr. Saktiawati also mentioned that Indonesia currently ranks second in the world in terms of the highest number of TB cases.
Only around 81% of an estimated 1,060,000 cases have been diagnosed. Meanwhile, the World Health Organization (WHO) targets a detection coverage of 100% by utilizing technologies such as computer-aided detection (CAD).
Without timely detection, TB patients are at risk of not receiving the necessary treatment, which can lead to death and increase the spread of the disease to others.
“Therefore, early detection efforts are crucial in accelerating TB elimination in Indonesia,” explained Dr. Saktiawati.
This research aims to improve diagnostic accuracy and ensure equitable access to healthcare services for all members of society.
She highlighted that vulnerable groups, such as women, children, people with disabilities, and communities in remote areas, still face significant challenges in accessing adequate TB services.
In many regions in Indonesia, patriarchal culture remains a barrier for women to access healthcare services, including TB screening and diagnosis.
Meanwhile, other groups, such as people with disabilities, often face both physical and social barriers to receiving the necessary examinations and treatments.
Dr. Saktiawati welcomed the Ministry of Health’s efforts in implementing active case finding (ACF) in 25 regencies/cities, which increased TB case detection by 2-7% in 2024.
However, she hopes that this program can be expanded to reach remote areas so that all citizens, especially vulnerable groups, can receive equal healthcare services.
The CAD technology under development is expected to help healthcare workers analyze chest X-rays more efficiently, particularly in areas with limited medical personnel, especially radiologists.
“I believe with the support of technological innovations and inclusive policies, the target of TB elimination in Indonesia can be achieved more quickly,” she concluded.
Author: Triya Andriyani
Post-editor: Afifudin Baliya
Image: UGM Center for Tropical Medicine