Leprosy continues to be a significant public health concern globally, with India being one of the leading contributor. The limited healthcare resources in developing countries often lead to delay in diagnosis, resulting in permanent disabilities and social marginalization. Lack of specialists and limited knowledge regarding leprosy among the primary healthcare workers contribute to a significant delay in diagnosis, revealing the existing gaps between the global leprosy elimination strategies and real world scenarios. In this regard, it is of pivotal importance that we develop alternative approaches to diagnose leprosy cases at an early stage. 

Artificial intelligence (AI) is a constantly evolving technology, which has gained significant interest recently. Deep learning is a  subset of  AI, that utilizes artificial neural networks to autonomously learn from vast amounts of data, enabling models to perform specific tasks without the  manual inputs. It has been implemented in diagnosis of different dermatological conditions including leprosy in the recent past with significant success rates. However, there is scarcity of data available regarding utility  in Indian skin. 

In 2024, World Health Organization had conducted a real-world field study of a skin-NTDs (Neglected Tropical Diseases) mobile application, which uses AI- based models to address 12 NTDs and 24 other common skin conditions .

            A similar deep learning model can be trained with feeding clinical images of varied presentations of skin lesions in leprosy and by integrating it with a mobile application, it ensures that the technology is of practical utility. The further validation of the technology by conducting a pilot study among primary health care workers would help assess its scope for real world implementation. Thus, the study aims at developing a deep learning AI model and its validation through a pilot study among primary health care workers.