Novel Technologies for Detecting Relapse in Clubfoot: The Old, The New and the Future

Description

The presentation discusses the crucial need for recognizing and identifying relapse in children with clubfoot, emphasizing the limitations of current practices that rely on resource-intensive serial examinations. An exploration of leveraging technology, particularly gait analysis and innovative foot shape assessment methods, is presented as a potential solution to improve accessibility and efficiency in clinical settings. The presenter shares findings from a systematic review highlighting gait differences between children with and without relapse, revealing that children with relapse show specific kinematic parameters indicating altered foot mechanics. Furthermore, the development of a simple, low-cost method for capturing foot shape using a patio tile and digital imaging is introduced, aimed at generating data that can be analyzed by artificial intelligence (AI) techniques. This method seeks to automate and improve the detection of relapse in clubfoot, paving the way for applying cutting-edge deep learning models to enhance clinical practice. The session concludes with an invitation for questions and acknowledgments of the team involved in this research.

DOI: 10.1302/3114-251080

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