KI in der instrumentellen Ganganalyse

Abstract

BACKGROUND: Artificial intelligence (AI) is considered a key technology for alleviating the burden on the healthcare system. For instrumental gait analysis, AI-based evaluations promise a direct and intuitive access to clinically relevant information in orthopaedics and trauma surgery, avoiding the challenging and time-consuming manual evaluations of large amounts of patient data. OBJECTIVE: The objective of this work is to investigate the specific challenges and limitations of using AI for clinical evaluation of gait analysis data and to propose effective solutions to address these limitations. METHOD: This work combines a systematic literature review on AI in gait analysis with practical experiences from applications of AI in the authors' own published research projects. RESULTS: Six key challenges have been identified. While AI methods work best when extensive training data, a limited number of influencing factors, and a clearly defined target variable are available, instrumental gait analysis is characterised by opposite conditions (little training data, multiple influencing factors, and fuzzy target variables). To address these contradicting characteristics, a catalogue of possible solution approaches focusing on integrating clinical expert knowledge into AI development and operation is outlined. CONCLUSION: It is shown that AI offers significant potential for improving the efficiency and quality of gait data exploitation. However, current AI approaches from other fields are only partially transferable to gait analysis due to insufficient fitting. By addressing the specific challenges for AI in gait analysis, it can be expected that specialized procedures and best practices can be developed, which will boost AI assistance in IGA clinical evaluation.

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