Every year, over 1.6 billion people worldwide are affected by at least one Neglected Tropical Disease (NTD), and in Thailand, a rising number of asymptomatic leishmaniasis cases—mainly in HIV patients—has been detected since 1999. Current diagnosis relies on a gold-standard parasitological test that is slow, tedious, and requires skilled technicians—a serious bottleneck in low-resource settings. The research aims to develop a roadmap for applying artificial intelligence and satellite imagery to speed up and simplify detection of leishmaniasis and other NTDs. This is not a full-scale deployment; it is a pilot study using existing data and AI techniques to demonstrate what is possible and identify practical challenges. If successful, the work could transform how neglected diseases are diagnosed in rural Thailand and similar regions worldwide. Faster, cheaper, and less labour-intensive screening would allow health workers to identify cases earlier, track outbreaks more effectively, and allocate limited resources where they are most needed. The project also includes publication and dissemination workshops to share knowledge with other countries tackling the same diseases.
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Each year, over 1.6 billion people around the globe would be affected by at least one of Neglected Tropical Diseases or NTDs [1]. As a response, WHO developed the NTD road map for 2021-2030 [2] to identify necessary actions to control and eradicate NTDs. Research and innovation are highlighted as fundamental factors to future success, especially potential applications of new technologies like artificial intelligence (AI) and satellite imagery. Specific to leishmaniasis, one of 20 NTDs that is prevalent in tropical areas such as Thailand, the increasing number of asymptomatic cases has been witnessed in this ASEAN country since 1999 [3], mainly among patients with HIV. Recently, a team of local medical experts (led by the Center of Excellence in Vector Biology and Vector Borne Diseases at Chulalongkorn University) have discovered new cases and their associations to sand flies found in rural communities [4-5]. However, those findings are based on the gold standard of parasitological diagnosis that can be tedious and time-consuming [6]. The procedure requires skilled technicians that is a challenge in low-resourced developing countries. To overcome this bottleneck, the proposed project is to develop a roadmap of AI technology exploitation, which is largely missing in the current literature [7]. The network of UK, Thai and other international partners will carry out a pilot study based on existing data and AI techniques to demonstrate actual applications and identify challenges to similar analysis tasks and other NTDs. Publication and dissemination workshops are also included to extend the scope of knowledge sharing.
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