Building Machine Learning Models in Gastrointestinal Endoscopy

Abstract

The integration of artificial intelligence (AI) into gastrointestinal (GI) endoscopy heralds a transformative shift in diagnostic capabilities, promising significant improvements in accuracy and efficiency. AI's ability to provide precise, real-time analysis can significantly aid endoscopists in detecting and characterizing various GI conditions, such as polyps, adenomas, and other lesions, potentially leading to improved patient outcomes through earlier detection and more accurate diagnoses. However, the current landscape of machine learning models in GI endoscopy is fraught with considerable variability in methodologies and quality, posing challenges for validation and generalization. Key issues include inconsistent reference standards across studies, a lack of external validation, and the use of disease-prevalence-enriched populations. These factors result in discrepancies in performance metrics and limit the comparability of results. Furthermore, the common practice of using high-quality imagery that does not reflect routine clinical practice can lead to AI models that underperform in real-world settings. To ensure the effective integration of AI in clinical practice, it is crucial to develop and validate models rigorously across diverse and representative datasets. This involves standardizing reference standards, ensuring thorough external validation, using representative patient populations, and incorporating a range of image qualities. Addressing these methodological discrepancies will enhance the reliability and robustness of AI models, thereby facilitating their adoption and improving patient care in GI endoscopy.