Leprosy is a neglected tropical disease affecting people worldwide. Left untreated or starting therapy too late, the infection can cause permanent damage to the nerves, skin, eyes, and limbs of patients. While current standard diagnostic techniques generally detect the disease once symptoms have manifested, infrared thermography presents a potential tool for the early detection of leprosy-related neuropathy. A research initiative has been established to explore whether autonomic nerve impairment in the hands can be identified using infrared thermography, necessitating a new method for temperature evaluation. This work discusses the development of a semi-automatic temperature analysis method for twelve regions of interest on the hands. The approach utilises real-time landmark tracking based on an existing machine-learning model, adapted to the context of the application. After applying a cold impulse and recording the response with an infrared camera, the rewarming temperature and recovery values are determined within the regions. Results presented in this report indicate no significant improvement compared to the existing manual method. The success of the model outcomes highly depends on the accuracy of the machine-learning-based hand landmark detection, resulting in insufficiently reliable outcomes. The underlying explanations and the potential for future improvements are further discussed in this thesis, considering the evident potential and benefits of the automated approach.