Summary Neuropathy is a well-known complication of leprosy. Nerve palpation and assessment of motor and sensory nerves are routinely performed at diagnosis in leprosy, which helps establish the diagnosis and, during treatment, monitor the status of somatic nerve function. Assessment of peripheral autonomic nerve function is not commonly practiced for diagnosing leprosy or for diagnosing and monitoring neuropathy. Early impairments in autonomic function, however, may help to establish or confirm a diagnosis and predict the development of clinical leprosy and neuropathy. Several studies, including infrared thermography and electro-neurophysiological studies, have shown the presence of subclinical nerve impairments that may indicate a diagnosis of leprosy or lead to the development of neuropathy. Sudomotor and vasomotor impairments, in the presence of loss of protective sensation, also increase the risk for ulceration, often requiring chronic care. This article provides a brief discussion of the importance and assessment of peripheral autonomic nerve function in leprosy. Its focus is to review articles that have used infrared thermography to assess this function.

Peripheral autonomic nerve function (ANF) impairment (ANFI) can be one of the first indicators for leprosy or leprosy neuropathy. However, within leprosy, hardly any ANF assessment methods are used in current practice. Skin temperature could be a proxy measure to assess ANF. Therefore, this research aims to explore whether low-cost infrared (IR) video thermography can be used as an ANF assessment tool by measuring the skin temperature response (STR) of human hands before, and after applying a cold pressor test (CPT). A protocol was defined to perform a baseline measurement, apply CPT, and record the resulting STR curve during 15 minutes. An IR video camera connected to a mobile phone was selected as sensor. A setup was developed to immobilize the position of the hands and fingers relative to the camera. A Python script was developed to extract the hand palm skin temperature STR curve from an IR video for 12 ulnar and median innervated regions of interest (ROI) in 1s intervals. A Matlab script was developed to post-process the raw temperature data into filtered data. This data is used to calculate key metrics that describe the STR curve. This approach was evaluated on technical accuracy and precision by comparing IR data for 3 cameras to a reference sensor. The variability caused by the observer analyzing the data was studied by analyzing the same video 5 times by 2 observers. The subject variability was studied by enrolling 7 subjects into a pilot, testing them daily for 5 consecutive days. The results show a high mean Interclass correlation of 0.94 between the 3 IR cameras and the reference sensor. Bland-Altman plots show a mean accuracy of +0.090°C between the cameras and the reference, and a variation between −1.30 and +1.50°C. High agreement was shown between observers analyzing the data. The pilot test showed high variability in STR curve within subjects. Although the general shape of the STR was similar, the location of the steep increase in recovery varied strongly within and between subjects. This study shows that a low-cost, portable IR camera can be used to measure STR of human hands after CPT. A pilot study showed high subject variability for repeated testing of the STR curve. Future research is needed to establish its value in assessing ANF in leprosy patients or other systemic and local neuropathies and traumatic nerve conditions.