Laprade, William and Bartlett, Keirah E. and Christensen, Charlotte R. and Kazandjian, Taline D. and Patel, Rohit N. and Crittenden, Edouard and Dawson, Charlotte A. and Mansourvar, Marjan and Wolff, Darian S. and Fryer, Thomas and Laustsen, Andreas H. and Casewell, Nicholas R. and Gutiérrez, José María and Hall, Steven R. and Jenkins, Timothy P. (2023) Machine-learning guided Venom Induced Dermonecrosis Analysis tooL : VIDAL. Scientific Reports, 13 (1): 21662. ISSN 2045-2322
Full text not available from this repository.Abstract
Snakebite envenoming is a global public health issue that causes significant morbidity and mortality, particularly in low-income regions of the world. The clinical manifestations of envenomings vary depending on the snake's venom, with paralysis, haemorrhage, and necrosis being the most common and medically relevant effects. To assess the efficacy of antivenoms against dermonecrosis, a preclinical testing approach involves in vivo mouse models that mimic local tissue effects of cytotoxic snakebites in humans. However, current methods for assessing necrosis severity are time-consuming and susceptible to human error. To address this, we present the Venom Induced Dermonecrosis Analysis tooL (VIDAL), a machine-learning-guided image-based solution that can automatically identify dermonecrotic lesions in mice, adjust for lighting biases, scale the image, extract lesion area and discolouration, and calculate the severity of dermonecrosis. We also introduce a new unit, the dermonecrotic unit (DnU), to better capture the complexity of dermonecrosis severity. Our tool is comparable to the performance of state-of-the-art histopathological analysis, making it an accessible, accurate, and reproducible method for assessing dermonecrosis in mice. Given the urgent need to address the neglected tropical disease that is snakebite, high-throughput technologies such as VIDAL are crucial in developing and validating new and existing therapeutics for this debilitating disease.