St Germain en Laye, January 16th 2025.
This study investigated the application of artificial intelligence (AI) to improve the diagnosis of anorectal motility disorders, a significant healthcare challenge due to the complexity and limited accessibility of high-resolution anorectal manometry (HR-ARM). The researchers developed and validated a machine learning (ML) model to automatically detect and differentiate various motility patterns based on HR-ARM data.
The study utilized a large dataset (701 HR-ARM exams) from a tertiary care center, classified according to the standardized London Classification. The data was split into training (80%) and testing (20%) sets for model development and evaluation. Multiple ML algorithms were tested, and the Light Gradient Boosting Machine (LGBM) classifier demonstrated superior performance, achieving an accuracy of 87% in identifying disorders of anal tone and contractility. Furthermore, individual ML models exceeded 90% accuracy in differentiating specific disorder subtypes (e.g., anal hypotension with normal contractility, anal hypertension).
The findings underscore the potential of AI to address key limitations of HR-ARM, including its complex data analysis, limited accessibility, and inter-observer variability in interpretation. By automating the detection of motility patterns, the AI model offers a promising solution for improving diagnostic accuracy, efficiency, and accessibility of HR-ARM, leading to more timely and effective management of anorectal functional disorders.
The study acknowledges limitations, primarily the single-center nature of the dataset, and suggests future research to incorporate data from multiple centers and diverse patient populations to enhance the generalizability and robustness of the AI model. The development of explainable AI models is also highlighted as a crucial next step to increase transparency and build trust in AI-driven diagnostics. This research represents a significant advancement in the application of AI to gastroenterology and offers a pathway to improve patient care in the management of anorectal disorders. The successful application of AI in this context lays the groundwork for broader adoption of AI-assisted diagnostics in other areas of gastroenterology and beyond.
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